This disclosure relates generally to electromechanical devices. More specifically, this disclosure relates to a control system for an electromechanical device for rehabilitation or exercise.
Various devices may be used by people for exercising and/or rehabilitating parts of their bodies. For example, as part of workout regimens to maintain a desired level of fitness, users may operate devices for a period of time or distance. In another example, a person may undergo knee surgery and a physician may provide a treatment plan for rehabilitation to strengthen and/or improve flexibility of the knee that includes periodically operating a rehabilitation device for a period of time and/or distance. The exercise and/or rehabilitation devices may include pedals on opposite sides. The devices may be operated by users engaging the pedals with their feet or their hands and rotating the pedals.
In general, the present disclosure provides a control system for a rehabilitation or exercise device and associated components of the device.
In one aspect, a system for rehabilitation includes a monitoring device comprising a memory device storing instructions and a network interface card, wherein the monitoring device is configured to detect information from a body part of a user. The system for rehabilitation further includes one or more processing devices operatively coupled to the monitoring device. The one or more processing devices are configured to execute the instructions for receiving configuration information specified in a treatment plan for rehabilitating the body part of the user. The one or more processing devices are configured to execute the instructions to receive the information from the monitoring device. The one or more processing devices are further configured to execute the instructions to transmit the configuration information and the information to a computing device controlling an electromechanical device, via the network interface card.
In another aspect, a system for rehabilitation includes a monitoring device comprising a memory device storing instructions and a network interface card, wherein the monitoring device is configured to detect information from a body part of a user. The system for rehabilitation further includes one or more processing devices operatively coupled to the monitoring device. The one or more processing devices are configured to execute the instructions to receive configuration information specified in a treatment plan for rehabilitating the body part of the user. The one or more processing devices are configured to execute the instructions to receive the information from the monitoring device. The one or more processing devices are further configured to execute the instructions to transmit the configuration information and the information to a computing device controlling an electromechanical device, via the network interface card. The transmitting the information to the computing device causes the computing device to present the information in a graphical animation of the body part moving in real-time.
In yet another aspect, a system for rehabilitation further includes a monitoring device comprising a memory device storing instructions and a network interface card, wherein the monitoring device is configured to detect a range of motion of a body part of a user. The system for rehabilitation further includes an electromechanical device comprising one or more pedals coupled to one or more radially-adjustable couplings. The system for rehabilitation further includes an electric motor coupled to the one or more pedals via the one or more radially-adjustable couplings. The system for rehabilitation further includes a control system comprising one or more processing devices operatively coupled to both the monitoring device and the electric motor. The one or more processing devices are configured to receive configuration information for an exercise session, set a resistance parameter and a maximum pedal force parameter based on the configuration information for the exercise session, and receive the range of motion from the monitoring device. The one or more processing devices are further configured to transmit the configuration information and the range of motion to a computing device controlling the electromechanical device, via the network interface card. The one or more processing devices are further configured to measure force applied to pedals of the electromechanical device as a user pedals the electromechanical device, wherein, based on the resistance parameter, the electric motor provides resistance during the exercise session.
From the following figures, descriptions, and claims, other technical features may be readily apparent to one skilled in the art.
Before undertaking the DETAILED DESCRIPTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The term “couple” and its derivatives refer to any direct or indirect communication between two or more elements, independent of whether those elements are in physical contact with one another. The terms “transmit,” “receive,” and “communicate,” as well as derivatives thereof, encompass both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, means to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The term “controller” means any device, system or part thereof that controls at least one operation. Such a controller may be implemented in hardware or a combination of hardware and software and/or firmware. The functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C.
Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or portions thereof adapted for implementation in a suitable computer readable program code. The phrase “computer readable program code” includes any type of computer code, including source code, linked or linkable code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), solid state device (SSD) memory, random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device.
Definitions for other certain words and phrases are provided throughout this patent document. Those of ordinary skill in the art should understand that in many if not most instances, such definitions apply to prior as well as to future uses of such defined words and phrases.
For a more complete understanding of this disclosure and its advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:
Improvement is desired in the field of devices used for rehabilitation and exercise. People may sprain, fracture, tear or otherwise injure a body part and then consult a physician to diagnose the injury. In some instances, the physician may prescribe a treatment plan that includes operating one or more electromechanical devices (e.g., pedaling devices for arms or legs) for a period of time to exercise the affected area in an attempt to regain normal or closer-to-normal function by rehabilitating the injured body part and affected proximate areas. In other instances, the person with the injury may determine to operate a device without consulting a physician. In either scenario, the devices that are operated lack effective monitoring of (i) progress of rehabilitation of the affected area and (ii) control over the electromechanical device during operation by the user. Conventional devices lack components that enable the operation of the electromechanical device in various modes designed to improve the rate and/or enhance the effectiveness of rehabilitation. Further, conventional rehabilitation systems lack monitoring devices that aid in determining one or more properties of the user (e.g., range of motion of the affected area, heartrate of the user, etc.) and enable the adjustment of components based on the determined properties. When the user is supposed to be adhering to a treatment plan, conventional rehabilitation systems may not provide to the physician real-time results of sessions. That is, typically, the physicians have to rely on the patient's word as to whether he or she is adhering to the treatment plan. As a result of the abovementioned issues, conventional rehabilitation systems that use electromechanical devices may not provide effective and/or efficient rehabilitation of the affected body part.
Accordingly, aspects of the present disclosure generally relate to a control system for a rehabilitation and exercise electromechanical device (referred to herein as “electromechanical device” or “device”). The electromechanical device may include an electric motor configured to drive one or more radially-adjustable couplings to rotationally move pedals coupled to the radially-adjustable couplings. The electromechanical device may be operated by a user engaging the pedals with his or her hands or feet and rotating the pedals to exercise and/or rehabilitate a desired body part. The electromechanical device and the control system may be included as part of a larger rehabilitation system. The rehabilitation system may also include monitoring devices (e.g., goniometers, wristbands, force sensors in the pedals, etc.) that provide valuable information about the user to the control system. As such, the monitoring devices may be in direct or indirect communication with the control system.
The monitoring devices may include a goniometer configured to measure a range of motion (e.g., angles of extension and/or bend) of a body part to which the goniometer is attached. The measured range of motion may be presented to the user and/or a physician via a user portal and/or a clinical portal. Also, to operate the electromechanical device during a treatment plan, the control system may use the measured range of motion to determine whether to adjust positions of the pedals on the radially-adjustable couplings and/or to change the mode types from one mode to another (e.g., from/to: passive, active-assisted, resistive, active) and/or durations. The monitoring devices may also include a wristband configured to track the steps of the user over a time period (e.g., a day, a week, etc.) and/or measure vital signs of the user (e.g., heartrate, blood pressure, oxygen level, etc.). The monitoring devices may also include force sensors disposed in the pedals and configured to measure the force exerted by the user on the pedals.
The control system may enable operating the electromechanical device in a variety of modes, such as a passive mode, an active-assisted mode, a resistive mode, and/or an active mode. The control system may use the information received from the measuring devices to adjust parameters (e.g., reduce resistance provided by electric motor, increase resistance provided by the electric motor, increase/decrease speed of the electric motor, adjust position of pedals on radially-adjustable couplings, etc.) while operating the electromechanical device in the various modes. The control system may receive the information from the monitoring devices, aggregate the information, make determinations using the information, and/or transmit the information to a cloud-based computing system for storage. The cloud-based computing system may maintain the information related to each user. As used herein, a cloud-based computing system refers, without limitation, to any remote computing system accessed over a network link.
A clinician and/or a machine learning model may generate a treatment plan for a user to rehabilitate a part of their body using at least the electromechanical device. A treatment plan may include a set of pedaling sessions using the electromechanical device, a set of joint extension sessions, a set of flex sessions, a set of walking sessions, a set of heartrate goals per pedaling session and/or walking session, and the like.
Each pedaling session may specify that a user is to operate the electromechanical device in a combination of one or more modes, including: passive, active-assisted, active, and resistive. The pedaling session may specify that the user is to wear the wristband and the goniometer during the pedaling session. Further, each pedaling session may include information specifying a set amount of time in which the electromechanical device is to operate in each mode, a target heartrate for the user during each mode in the pedaling session, target forces that the user is to exert on the pedals during each mode in the pedaling session, target ranges of motion the body parts are to attain during the pedaling session, positions of the pedals on the radially-adjustable couplings, and the like.
Each joint extension session may specify information relating to a target angle of extension at the joint, and each set of joint flex sessions may specify information relating to a target angle of flex at the joint. Each walking session may specify a target number of steps the user should take over a set period of time (e.g., a day, a week, etc.) and/or a target heartrate to achieve and/or maintain during the walking session.
The treatment plan may be stored in the cloud-based computing system and, when the user is ready to begin the treatment plan, downloaded to the computing device of the user. In some embodiments, the computing device that executes a clinical portal module (alternatively referred to herein as a clinical portal) may transmit the treatment plan to the computing device that executes a user portal and the user may initiate the treatment plan when ready.
In addition, the disclosed rehabilitation system may enable a physician to use the clinical portal to monitor the progress of the user in real-time. The clinical portal may present information pertaining to when the user is engaged in one or more sessions, statistics (e.g., speed, revolutions per minute, positions of pedals, forces on the pedals, vital signs, numbers of steps taken by user, ranges of motion, etc.) of the sessions, and the like. The clinical portal may also enable the physician to view before and after session images of the affected body part of the user to enable the physician to judge how well the treatment plan is working and/or to make adjustments to the treatment plan. The clinical portal may enable the physician, based on information received from the control system, to dynamically change a parameter (e.g., position of pedals, amount of resistance provided by electric motor, speed of the electric motor, duration of one of the modes, etc.) of the treatment plan in real-time.
The disclosed techniques provide numerous benefits over conventional systems. For example, to enhance the efficiency and effectiveness of rehabilitation of the user, the rehabilitation system provides granular control over the components of the electromechanical device. The control system enables, by controlling the electric motor, operating the electromechanical device in any suitable combination of the modes described herein. Further, the control system may use information received from the monitoring devices during a pedaling session to adjust parameters of components of the electromechanical device in real-time, for example. Additional benefits of this disclosure may include enabling a computing device operated by a physician to monitor the progress of a user participating in a treatment plan in real-time and/or to control operation of the electromechanical device during a pedaling session.
Additionally, the network interface cards may enable communicating data over long distances, and in one example, the computing device 102 may communicate with a network 112. Network 112 may be a public network (e.g., connected to the Internet via wired means (Ethernet) or wireless means (WiFi)), a private network (e.g., a local area network (LAN) or wide area network (WAN)), or a combination thereof. The computing device 102 may be communicatively coupled with a computing device 114 and a cloud-based computing system 116.
The computing device 102 may be any suitable computing device, such as a laptop, tablet, smartphone, computer or Internet of Things (IoT) sensor or device. (Other computing devices referenced herein may also be Internet of Things (IoT) sensors or devices.) The computing device 102 may include a display that is capable of presenting a user interface, such as a user portal 118. The user portal 118 may be implemented in computer instructions stored on the one or more memory devices of the computing device 102 and executable by the one or more processing devices of the computing device 102. The user portal 118 may present to a user various screens that enable the user to view a treatment plan, initiate a pedaling session for the purpose of executing the treatment plan, control parameters of the electromechanical device 104, view progress of rehabilitation during the pedaling session, and so forth as described in more detail below. The computing device 102 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the computing device 102, perform operations to control the electromechanical device 104.
The computing device 114 may execute a clinical portal 126. The clinical portal 126 may be implemented in computer instructions stored on the one or more memory devices of the computing device 114 and executable by the one or more processing devices of the computing device 114. The clinical portal 126 may present to a physician or a clinician various screens that enable the physician to create a treatment plan for a patient or user, view progress of the user throughout the treatment plan, view measured properties (e.g., angles of bend/extension, force exerted on the pedals 110, heart rate, steps taken, images of the affected body part) of the user during sessions of the treatment plan, and/or view properties (e.g., modes completed, revolutions per minute, etc.) of the electromechanical device 104 during sessions of the treatment plan. So the patient may begin the treatment plan, the treatment plan specific to a patient may be transmitted via the network 112 to the cloud-based computing system 116 for storage and/or to the computing device 102. The terms “patient” and “user” may be used interchangeably throughout this disclosure.
The electromechanical device 104 may be an adjustable pedaling device for exercising and rehabilitating arms and/or legs of a user. The electromechanical device 104 may include at least one or more motor controllers 120, one or more electric motors 122, and one or more radially-adjustable couplings 124. Two pedals 110 may be coupled to two radially-adjustable couplings 124 via left and right pedal assemblies that each include respective stepper motors. The motor controller 120 may be operatively coupled to the electric motor 122 and configured to provide commands to the electric motor 122 to control operation of the electric motor 122. The motor controller 120 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The motor controller 120 may provide control signals or commands to drive the electric motor 122. The electric motor 122 may be powered to drive one or more radially-adjustable couplings 124 of the electromechanical device 104 in a rotational manner. The electric motor 122 may provide the driving force to rotate the radially-adjustable couplings 124 at configurable speeds. The couplings 124 are radially-adjustable in that a pedal 110 attached to the coupling 124 may be adjusted to a number of positions on the coupling 124 in a radial fashion. Further, the electromechanical device 104 may include a current shunt to provide resistance to dissipate energy from the electric motor 122. As such, the electric motor 122 may be configured to provide resistance to rotation of the radially-adjustable couplings 124.
The computing device 102 may be communicatively connected to the electromechanical device 104 via the network interface card on the motor controller 120. The computing device 102 may transmit commands to the motor controller 120 to control the electric motor 122. The network interface card of the motor controller 120 may receive the commands and transmit the commands to the electric motor 122 to drive the electric motor 122. In this way, the computing device 102 is operatively coupled to the electric motor 122.
The computing device 102 and/or the motor controller 120 may be referred to as a control system herein. The user portal 118 may be referred to as a user interface of the control system herein. The control system may control the electric motor 122 to operate in a number of modes: passive, active-assisted, resistive, and active. The passive mode may refer to the electric motor 122 independently driving the one or more radially-adjustable couplings 124 rotationally coupled to the one or more pedals 110. In the passive mode, the electric motor 122 may be the only source of driving force on the radially-adjustable couplings. That is, the user may engage the pedals 110 with their hands or their feet and the electric motor 122 may rotate the radially-adjustable couplings 124 for the user. This may enable moving the affected body part and stretching the affected body part without the user exerting excessive force.
The active-assisted mode may refer to the electric motor 122 receiving measurements of revolutions per time period, such as a revolutions per minute, second, or any other desired time interval, of the one or more radially-adjustable couplings 124, and, when the measured revolutions per time period satisfy a threshold condition, causing the electric motor 122 to drive the one or more radially-adjustable couplings 124 rotationally coupled to the one or more pedals 110. The threshold condition may be configurable by the user and/or the physician. As long as the revolutions per time period are above a revolutions per time period threshold (e.g., revolutions threshold 1732) and the threshold condition is not satisfied, the electric motor 122 may be powered off while the user provides the driving force to the radially-adjustable couplings 124. When the revolutions per time period are less than the revolutions per minute threshold, then the threshold condition is satisfied and the electric motor 122 may be controlled to drive the radially-adjustable couplings 124 to maintain the revolutions per time period threshold.
The resistive mode may refer to the electric motor 122 providing resistance to rotation of the one or more radially-adjustable couplings 124 coupled to the one or more pedals 110. The resistive mode may increase the strength of the body part being rehabilitated by causing the muscle to exert force to move the pedals 110 against the resistance provided by the electric motor 122.
The active mode may refer to the electric motor 122 powering off to provide no driving force assistance to the radially-adjustable couplings 124. Instead, in this mode, the user, using their hands or feet, for example, provides the sole driving force of the radially-adjustable couplings.
During one or more of the modes, each of the pedals 110 may measure force exerted by a part of the body of the user on the pedal 110. For example, the pedals 110 may each contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the pedal 110. Further, the pedals 110 may each contain any suitable sensor for detecting whether the body part of the user separates from contact with the pedals 110. In some embodiments, the measured force may be used to detect whether the body part has separated from the pedals 110. The force detected may be transmitted via the network interface card of the pedal 110 to the control system (e.g., computing device 102 and/or motor controller 120). As described further below, the control system may, based on the measured force, modify a parameter of operating the electric motor 122. Further, the control system may perform one or more preventative actions (e.g., locking the electric motor 122 to stop the radially-adjustable couplings 124 from moving, slowing down the electric motor 122, presenting a notification to the user, etc.) when the body part is detected as separated from the pedals 110, among other things.
The goniometer 106 may be configured to measure angles of extension and/or bend of body parts and to transmit the measured angles to the computing device 102 and/or the computing device 114. The goniometer 106 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards. The goniometer 106 may be attached to the user's body, for example, to an upper leg and a lower leg. The goniometer 106 may be coupled to the user via a strap, an adhesive, a mechanical brace, or any other desired attachment. The goniometer 106 may be disposed in a cavity of the mechanical brace. The cavity of the mechanical brace may be located near a center of the mechanical brace where the mechanical brace affords to bend and extend. The mechanical brace may be configured to secure to an upper body part (e.g., arm, etc.) and a lower body part (e.g., leg, etc.) to measure the angles of bend as the body parts are extended away from one another or retracted closer to one another.
The wristband 108 may include a 3-axis accelerometer to track motion in the X, Y, and Z directions, an altimeter for measuring altitude, and/or a gyroscope to measure orientation and rotation. The accelerometer, altimeter, and/or gyroscope may be operatively coupled to a processing device in the wristband 108 and may transmit data to the processing device. The processing device may cause a network interface card to transmit the data to the computing device 102 and the computing device 102 may use the data representing acceleration, frequency, duration, intensity, and patterns of movement to track steps taken by the user over certain time periods (e.g., days, weeks, etc.). The computing device 102 may transmit the steps to the computing device 114 executing a clinical portal 126. Additionally, in some embodiments, the processing device of the wristband 108 may determine the steps taken and transmit the steps to the computing device 102. In some embodiments, the wristband 108 may use photoplethysmography (PPG) to measure heartrate that detects an amount of red light or green light on the skin of the wrist. For example, blood may absorb green light so when the heart beats, the blood flow may absorb more green light, thereby enabling the detection of heartrate. The heartrate may be sent to the computing device 102 and/or the computing device 114.
The computing device 102 may present the steps taken by the user and/or the heartrate via respective graphical elements on the user portal 118, as discussed further below. The computing device may also use the steps taken and/or the heart rate to control a parameter of operating the electromechanical device 104. For example, if the heartrate exceeds a target heartrate for a pedaling session, the computing device 102 may control the electric motor 122 to reduce resistance being applied to rotation of the radially-adjustable couplings 124. In another example, if the steps taken are below a step threshold for a day, the treatment plan may increase the amount of time for one or more modes in which the user is to operate the electromechanical device 104 to ensure the affected body part is getting sufficient movement by reaching or exceeding the step threshold.
In some embodiments, the cloud-based computing system 116 may include one or more servers 128 that form a distributed computing architecture. Each of the servers 128 may include one or more processing devices, memory devices, data storage, and/or network interface cards. The servers 128 may be in communication with one another via any suitable communication protocol. The servers 128 may store profiles for each of the users that use the electromechanical device 104. The profiles may include information about the users, such as respective treatment plans, the affected body parts, any procedures the users had performed on the affected body parts, health, age, race, measured data from the goniometer 106, measured data from the wristband 108, measured data from the pedals 110, user input received at the user portal 118 during operation of any of the modes of the treatment plan, a specification of a level of discomfort, comfort, or general patient satisfaction that the user experiences before and after any of the modes, before and after session images of the affected body part, and so forth.
In some embodiments, the cloud-based computing system 116 may include a training engine 130 capable of generating one or more machine learning models 132. The machine learning models 132 may be trained to generate treatment plans for the patients in response to receiving various inputs (e.g., a procedure performed on the patient, an affected body part on which the procedure was performed, other health characteristics or demographic attributes (e.g., age, race, fitness level, etc.)). The one or more machine learning models 132 may be generated by the training engine 130 and may be implemented in computer instructions executable by one or more processing devices of the training engine 130 and/or the servers 128. To generate the one or more machine learning models 132, the training engine 130 may train the one or more machine learning models 132. The training engine 130 may use a base data set of patient characteristics or attributes, treatment plans followed by the patient, and results of the treatment plans followed by the patients. The results may include information indicating whether a given treatment plan led to full recovery of the affected body part, partial recovery of the affected body part, or lack of recovery of the affected body part, and the degree to which such recovery was achieved. The training engine 130 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a camera, a video camera, a netbook, a desktop computer, a media center, an IoT device, or any combination of the above. The one or more machine learning models 132 may refer to model artifacts that are created by the training engine 130 using training data that includes training inputs and corresponding target outputs. The training engine 130 may find patterns in the training data that map the training input to the target output, and generate the machine learning models 132 that capture these patterns. Although depicted separately from the computing device 102, in some embodiments, the training engine 130 and/or the machine learning models 132 may reside on the computing device 102 and/or the computing device 114.
The machine learning models 132 may include one or more of a neural network, such as an image classifier, recurrent neural network, convolutional network, generative adversarial network, a fully connected neural network, or some combination thereof, for example. In some embodiments, the machine learning models 132 may be composed of a single level of linear or non-linear operations or may include multiple levels of non-linear operations. For example, the machine learning model 132 may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons. The rehabilitation system architecture 100 can include additional and/or fewer components and is not limited to those illustrated in
The electromechanical device 104 includes a rotary device such as radially-adjustable couplings 124 or a flywheel or flywheels or the like rotatably mounted such as by a central hub to a frame 200 or other support. The pedals 110 are configured for interacting with a patient to be rehabilitated and may be configured for use with lower body extremities such as the feet, legs, and the like, or with upper body extremities, such as the hands, arms, and the like. For example, the pedal 110 may be a bicycle pedal of the type having a foot support rotatably mounted onto an axle with bearings. To locate the pedal on the radially-adjustable coupling 12. the axle may or may not have exposed end threads for engaging a mount on the radially-adjustable coupling 124. The radially-adjustable coupling 124 may include an actuator configured to radially adjust the location of the pedal to various positions on the radially-adjustable coupling 124.
Alternatively, the radially-adjustable coupling 124 may be configured to have both pedals 110 on opposite sides of a single coupling 124. In some embodiments, as depicted, a pair of radially-adjustable couplings 124 may be spaced apart from one another but interconnected to the electric motor 122. In the depicted example, the computing device 102 may be mounted on the frame 200 and may be detachable and held by the user while the user operates the electromechanical device 104. The computing device 102 may present the user portal and control the operation of the electric motor 122, as described herein.
In some embodiments, as described in U.S. Pat. No. 10,173,094 (U.S. application Ser. No. 15/700,293), which is incorporated by reference herein in its entirety for all purposes, the electromechanical device 104 may take the form of a traditional exercise/rehabilitation device which is more or less non-portable and remains in a fixed location, such as a rehabilitation clinic or medical practice. This embodiment of the electromechanical device 104 may include a seat and is less portable than the electromechanical device 104 shown in
As discussed above, an electromechanical device may include one or more pedals coupled to one or more radially-adjustable couplings, an electric motor coupled to the one or more pedals via the one or more radially-adjustable couplings, and the control system including one or more processing devices operatively coupled to the electric motor. In some embodiments, the control system (e.g., computing device 102 and/or motor controller 120) may store instructions and one or more operations of the control system may be presented via the user portal. In some embodiments, the radially-adjustable couplings are configured for translating rotational motion of the electric motor to radial motion of the pedals.
At block 302, responsive to a first trigger condition occurring, the processing device may control the electric motor to operate in a passive mode by independently driving the one or more radially-adjustable couplings rotationally coupled to the one or more pedals. “Independently drive” may refer to the electric motor driving the one or more radially-adjustable couplings without the aid of another driving source (e.g., the user). The first trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device executing the clinical portal). While operating in the passive mode, the processing device may control the electric motor to independently drive the one or more radially-adjustable couplings rotationally coupled to the one or more pedals at a controlled speed specified in a treatment plan for a user operating the electromechanical device.
In some embodiments, the electromechanical device may be configured such that the processor controls the electric motor to individually drive the radially-adjustable couplings. For example, the processing device may control the electric motor to individually drive the left or right radially-adjustable coupling, while allowing the user to provide the force to drive the other radially-adjustable coupling. As another example, the processing device may control the electric motor to drive both the left and right radially-adjustable couplings but at different speeds. This granularity of control may be beneficial by controlling the speed at which a healing body part is moved (e.g., rotated, flexed, extended, etc.) to avoid tearing tendons or causing pain to the user.
At block 304, responsive to a second trigger condition occurring, the processing device may control the electric motor to operate in an active-assisted mode by measuring (block 306) revolutions per minute of the one or more radially-adjustable couplings, and causing (block 308) the electric motor to drive the one or more radially-adjustable couplings rotationally coupled to the one or more pedals when the measured revolutions per minute satisfy a threshold condition. The second trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device executing the clinical portal). The threshold condition may be satisfied when the measured revolutions per minute are less than a minimum revolutions per minute. In such an instance, the electric motor may begin driving the one or more radially-adjustable couplings to increase the revolutions per minute of the radially-adjustable couplings.
As with the passive mode, in the active-assisted mode, the processing device may control the electric motor to individually drive the one or more radially-adjustable couplings. For example, if just a right knee is being rehabilitated, the revolutions per minute of the right radially-adjustable coupling may be measured and the processing device may control the electric motor to individually drive the right radially-adjustable coupling when the measured revolutions per minute are less than the minimum revolutions per minute. In some embodiments, there may be different minimum revolutions per minute set for the left radially-adjustable coupling and the right radially-adjustable coupling, and the processing device may control the electric motor to individually drive the left radially-adjustable coupling and the right radially-adjustable coupling as appropriate to maintain the different minimum revolutions per minute.
At block 310, responsive to a third trigger condition occurring, the processing device may control the electric motor to operate in a resistive mode by providing resistance to rotation of the one or more radially-adjustable couplings coupled to the one or more pedals. The third trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device executing the clinical portal).
In some embodiments, responsive to a fourth trigger condition occurring, the processing device may be further configured to control the electric motor to operate in an active mode by powering off to enable another source (e.g., the user) to drive the one or more radially-adjustable couplings via the one or more pedals. In the active mode, the another source may drive the one or more radially-adjustable couplings at any desired speed via the one or more pedals.
In some embodiments, the processing device may control the electric motor to operate in each of the passive mode, the active-assisted mode, the resistive mode, and/or the active mode for a respective period of time during a pedaling session (e.g., based on a treatment plan for a user operating the electromechanical device). In some embodiments, the various modes and the respective periods of time may be selected by a clinician that sets up the treatment plan using the clinical portal. In some embodiments, the various modes and the respective periods of time may be selected by a machine learning model trained to receive parameters (e.g., procedure performed on the user, body part on which the procedure was performed, health of the user) and to output a treatment plan to rehabilitate the affected body part, as described above.
In some embodiments, the processing device may modify one or more positions of the one or more pedals on the one or more radially-adjustable couplings to change one or more diameters of ranges of motion of the one or more pedals during any of the passive mode, the active-assisted mode, the resistive mode, and/or the active mode throughout a pedaling session for a user operating the electromechanical device. The processing device may further be configured to modify the position of one of the one or more pedals on one of the one or more radially-adjustable couplings to change the diameter of the range of motion of the one of the one or more pedals while maintaining another position of another of the one or more pedals on another of the one or more radially-adjustable couplings to maintain another diameter of another range of motion of another pedal. In some embodiments, the processing device may cause both positions of the pedals to move to change the diameter of the range of motion for both pedals. The amount of movement of the positions of the pedals may be individually controlled in order to provide different diameters of ranges of motions of the pedals as desired.
In some embodiments, the processing device may receive, from the goniometer worn by the user operating the electromechanical device, at least one of an (i) angle of extension of a joint of the user during a pedaling session or an (ii) angle of bend of the joint of the user during the pedaling session. In some instances, the joint may be a knee or an elbow. The goniometer may be configured to measure the angles of bend and/or extension of the joint and to continuously, continually, or periodically transmit the angle measurements received by the processing device. The processing device may modify the positions of the pedals on the radially-adjustable couplings to change the diameters of the ranges of motion of the pedals based on the at least one of the angle of extension of the joint of the user or the angle of bend of the joint of the user.
In some embodiments, the processing device may receive, from the goniometer worn by the user, a set of angles of extension between an upper leg and a lower leg at a knee of the user as the user extends the lower leg away from the upper leg via the knee. In some embodiments, the goniometer may send the set of angles of extension between an upper arm, upper body, etc. and a lower arm, lower body, etc. The processing device may present, on a user interface of the control system, a graphical animation of the upper leg, the lower leg, and the knee of the user as the lower leg is extended away from the upper leg via the knee. The graphical animation may include the set of angles of extension as the set of angles of extension changes during the extension. The processing device may store, in a data storage of the control system, a lowest value of the set of angles of extension as an extension statistic for an extension session. A set of extension statistics may be stored for a set of extension sessions specified by the treatment plan. The processing device may present progress of the set of extension sessions throughout the treatment plan via a graphical element (e.g., line graph, bar chart, etc.) on the user interface presenting the set of extension statistics.
In some embodiments, the processing device may receive, from the goniometer worn by the user, a set of angles of bend or flex between an upper leg and a lower leg at a knee of the user as the user retracts the lower leg closer to the upper leg via the knee. In some embodiments, the goniometer may send the set of angles of bend between an upper arm, upper body, etc. and a lower arm, lower body, etc. The processing device may present, on a user interface of the control system, a graphical animation of the upper leg, the lower leg, and the knee of the user as the lower leg is retracted closer to the upper leg via the knee. The graphical animation may include the set of angles of bend as the set of angles of bend changes during the bending. The processing device may store, in a data storage of the control system, a highest value of the set of angles of bend as a bend statistic for a bend session. A set of bend statistics may be stored for a set of bend sessions specified by the treatment plan. The processing device may present progress of the set of bend sessions throughout the treatment plan via a graphical element (e.g., line graph, bar chart, etc.) on the user interface presenting the set of bend statistics.
In some embodiments, the angles of extension and/or bend of the joint may be transmitted by the goniometer to a computing device executing a clinical portal. A clinician may operate the computing device executing the clinical portal. The clinical portal may present a graphical animation in real-time of the upper leg extending away from the lower leg and/or the upper leg bending closer to the lower leg during a pedaling session, extension session, and/or a bend session of the user. In some embodiments, the clinician may provide notifications to the computing device to present via the user portal. The notifications may indicate that the user has satisfied a target extension and/or bend angle. Other notifications may indicate that the user has extended or retracted a body part too far and should cease the extension and/or bend session. In some embodiments, the computing device executing the clinical portal may transmit a control signal to the control system to move a position of a pedal on the radially-adjustable coupling based on the angle of extension or angle of bend received from the goniometer. That is, the clinician can in real-time increase a diameter of range of motion for a body part of the user based on the measured angles of extension and/or bend during a pedaling session. This may enable the clinician to dynamically control the pedaling session to enhance the rehabilitation results of the pedaling session.
In some embodiments, the processing device may receive, from a wearable device (e.g., a wristband), a number of steps taken by a user over a certain time period (e.g., a day, a week, etc.). The processing device may calculate whether the number of steps satisfies a step threshold of a walking session of a treatment plan for the user. The processing device may be configured to present on a user interface of the control system the number of steps taken by the user and may be configured to present an indication of whether the number of steps satisfies the step threshold.
The wearable device, which is interchangeably described herein as a wristband, though a person having ordinary skill in the art will readily comprehend in light of having read the present disclosure that other varieties of wearable devices may also be used without departing from the scope and intent of the present disclosure, may also measure one or more vital statistics of the user, such as a heartrate, oxygen level, blood pressure, and the like. The measurements of the vital statistics may be performed at any suitable time, such as during a pedaling session, walking session, extension session, bend session, and/or any other desired session. The wristband may transmit the one or more vital statistics to the control system. The processing device of the control system may use the vital statistics to determine whether to reduce resistance the electric motor is providing for the purpose of lowering one of the vital statistics (e.g., heartrate) when that vital statistic is above a threshold, to determine whether the user is in pain when one of the vital statistics is elevated beyond a threshold, to determine whether to provide a notification indicating the user should take a break or increase the intensity of the appropriate session, and so forth.
In some embodiments, the processing device may receive a request to stop the one or more pedals from moving. The request may be received by a user selecting on the user portal of the control system a graphical icon representing “stop.” The processing device may cause the electric motor to lock and stop the one or more pedals from moving over a configured period of time (e.g., instantly, over 1 second, 2 seconds, 3 seconds, 5 seconds, 10 seconds, or any period of time less than those, more than those or in between those, etc.). One benefit of including an electric motor in the electromechanical device is that the motor can be configured to provide the ability to stop the movement of the pedals as soon as a user desires.
In some embodiments, the processing device may receive, from one or more force sensors operatively coupled to the one or more pedals and the one or more processing devices, one or more measurements of force on the one or more pedals. The force sensors may be operatively coupled to the one or more processing devices via a wireless connection (e.g., Bluetooth) enabled by wireless circuitry in the pedals. The processing device may determine, based on the one or more measurements of force, whether the user has fallen from the electromechanical device. Responsive to determining that the user has fallen from the electromechanical device, the processing device may lock the electric motor to stop the one or more pedals from moving.
Additionally or alternatively, the processing device may determine, based on the one or more measurements of force that the user's feet or hands have separated from the pedals. Responsive to determining that the feet or hands have separated from the pedals, the processing device may lock the electric motor to stop the one or more pedals from moving. Also, the processing device may present a notification on a user interface of the control system, such notification instructing the user to place their feet or hands in contact with the pedals.
In some embodiments, the processing device may receive, from the force sensors operatively coupled to the one or more pedals, the measurements of force exerted by a user on the pedals during a pedaling session. While the user pedals during the pedaling session, the processing device may present the respective measurements of force on each of the pedals on a separate respective graphical element on the user interface of the control system. Various graphical indicators may be presented on the user interface to indicate when the force is below a threshold target range, is within the threshold target range, and/or exceeds the threshold target range. Notifications may be presented to encourage the user to apply more force and/or less force to achieve the threshold target range of force. For example, the processing device may be configured to present a first notification on the user interface after the one or more measurements of force satisfy a pressure threshold and to present a second notification on the user interface after the one or more measurements do not satisfy the pressure threshold.
In addition, the processing device may provide an indicator to the user based on the one or more measurements of force. The indicator may include at least one of (1) providing haptic feedback in the pedals, handles, and/or seat of the electromechanical device, (2) providing visual feedback on the user interface (e.g., an alert, a light, a sign, etc.), (3) providing audio feedback via an audio subsystem (e.g., speaker) of the electromechanical device, or (4) illuminating a warning light of the electromechanical device.
In some embodiments, the processing device may receive, from an accelerometer of the control system, motor controller, pedal, or the like, a measurement of acceleration of movement of the electromechanical device. The processing device may determine whether the electromechanical device has moved excessively relative to a vertical axis (e.g., fallen over) based on the measurement of acceleration. Responsive to determining that the electromechanical device has moved excessively relative to the vertical axis based on the measurement of acceleration, the processing device may lock the electric motor to stop the one or more pedals from moving.
After a pedaling session is complete, the processing device may lock the electric motor to prevent the one or more pedals from moving a certain amount of time after the completion of the pedaling session. This may enable healing of the body part being rehabilitated and prevent strain on that body part by excessive movement. Upon expiration of the certain amount of time, the processing device may unlock the electric motor to enable movement of the pedals again.
The computing device can include a user portal. The user portal may provide an option to image the body part being rehabilitated. The user portal may include a display and a camera. For example, the user may place the body part within an image capture section, such as a camera, of the user portal and select an icon to capture an image of the body part. An icon, such as a camera icon, may be located on a display of the user portal. The user may select the camera icon to use the camera to capture an image or to take a photograph of a site of the body of the user. The site may be a body part such as a leg, arm, joint, such as a knee or an elbow, or any other desired site of the body of the user. The processing device can execute the instructions to store the image or photograph. The processing device may execute the instructions to transmit the image or photograph to a clinical portal. The images may be captured before and after a pedaling session, walking session, extension session, and/or bend session. These images may be sent to the cloud-based computing system to use as training data to enable the machine-learning model to determine the effects of the session. Further, the images may be sent to the computing device executing the clinical portal to enable the clinician to view the results of the sessions and modify the treatment plan if desired and/or provide notifications (e.g., reduce resistance, increase resistance, extend the joint further or less, etc.) to the user if desired.
At block 402, the processing device may receive configuration information for a pedaling session. The configuration information may be received via selection by the user on the user portal executing on the computing device, received from the computing device executing the clinical portal, downloaded from the cloud-based computing system, retrieved from a memory device of the computing device executing the user portal, or some combination thereof. For example, the clinician may select the configuration information for a pedaling session of a patient using the clinical portal and upload the configuration information from the computing device to a server of the cloud-based computing system.
The configuration information for the pedaling session may specify one or more modes in which the electromechanical device is to operate, and configuration information specific to each of the modes, an amount of time to operate each mode, and the like. For example, for a passive mode, the configuration information may specify a position for the pedal to be in on the radially-adjustable couplings and a speed at which to control the electric motor. For the resistive mode, the configuration information may specify an amount of resistive force the electric motor is to apply to rotate radially-adjustable couplings during the pedaling session, a maximum pedal force that is desired for the user to exert on each pedal of the electromechanical device during the pedaling session, and/or a revolutions per minute threshold for the radially-adjustable couplings. For the active-assisted mode, the configuration information may specify a minimum pedal force and a maximum pedal force desired for the user to exert on each pedal of the electromechanical device, a speed at which to operate the electric motor for driving one or both of the radially-adjustable couplings, and so forth.
In some embodiments, responsive to receiving the configuration information, the processing device may determine that a trigger condition has occurred. The trigger condition may include receiving a selection of a mode from a user, an amount of time elapsing, receiving a command from the computing device executing the clinical portal, or the like. The processing device may control, based on the trigger condition occurring, the electric motor to operate in a resistive mode by providing, based on the trigger condition, a resistance to rotation of the pedals.
At block 404, the processing device may set a resistance parameter and a maximum pedal force parameter based on the amount of resistive force and the maximum pedal force, respectively, included in the configuration information for the pedaling session. The resistance parameter and the maximum force parameter may be stored in a memory device of the computing device and used to control the electric motor during the pedaling session. For example, the processing device may transmit a control signal along with the resistance parameter and/or the maximum pedal force parameter to the motor controller, and the motor controller may drive the electric motor using at least the resistance parameter during the pedaling session.
At block 406, the processing device may measure force applied to pedals of the electromechanical device as a user operates (e.g., pedals) the electromechanical device. The electric motor of the electromechanical device may provide resistance during the pedaling session based on the resistance parameter. A force sensor disposed in each pedal and operatively coupled to the motor controller and/or the computing device executing the user portal may measure the force exerted on each pedal throughout the pedaling session. The force sensors may transmit the measured force to a processing device of the pedals, which in turn may cause a communication device to transmit the measured force to the processing device of the motor controller and/or the computing device.
At block 408, the processing device may determine whether the measured force exceeds the maximum pedal force parameter. To make this determination, the processing device may compare the measured force to the maximum pedal force parameter.
At block 410, responsive to determining that the measured force exceeds the maximum pedal force parameter, the processing device may reduce the resistance parameter to maintain the revolutions per minute threshold specified in the configuration information so the electric motor applies less resistance during the pedaling session. Reducing the resistance may enable the user to pedal faster, thereby increasing the revolutions per minute of the radially-adjustable couplings. Maintaining the revolutions per minute threshold may ensure that the patient is exercising the affected body part as rigorously as desired during the mode. Responsive to determining that the measured force does not exceed the maximum pedal force parameter, the processing device may, during the pedaling session, maintain the same maximum pedal force parameter specified by the configuration information.
In some embodiments, the processing device may determine that a second trigger condition has occurred. The second trigger condition may include receiving a selection of a mode from a user via the user portal, an amount of time elapsing, receiving a command from the computing device executing the clinical portal, or the like. The processing device may control, based on the trigger condition occurring, the electric motor to operate in a passive mode by independently driving one or more radially-adjustable couplings coupled to the pedals in a rotational fashion. The electric motor may drive the one or more radially-adjustable couplings at a speed specified in the configuration information without another driving source. Also, the electric motor may drive each of the one or more radially-adjustable couplings individually at different speeds.
In some embodiments, the processing device may determine that a third trigger condition has occurred. The third trigger condition may be similar to the other trigger conditions described herein. The processing device may control, based on the third trigger condition occurring, the electric motor to operate in an active-assisted mode by measuring revolutions per minute of the one or more radially-adjustable couplings coupled to the pedals and, when the measured revolutions per minute satisfy a threshold condition, causing the electric motor to drive, in a rotational fashion, the one or more radially-adjustable couplings coupled to the pedals.
In some embodiments, the processing device may receive, from a goniometer worn by the user operating the electromechanical device, a set of angles of extension between an upper leg and a lower leg at a knee of the user. The set of angles is measured as the user extends the lower leg away from the upper leg via the knee. In some embodiments, the angles of extension may represent angles between extending a lower arm away from an upper arm at an elbow. Further, the processing device may receive, from the goniometer, a set of angles of bend between the upper leg and the lower leg at the knee of the user. The set of angles of bend is measured as the user retracts the lower leg closer to the upper leg via the knee. In some embodiments, the angles of bend represent angles between bending a lower arm closer to an upper arm at an elbow.
The processing device may determine whether a range of motion threshold condition is satisfied based on the set of angles of extension and the set of angles of bend. Responsive to determining that the range of motion threshold condition is satisfied, the processing device may modify a position of one of the pedals on one of the radially-adjustable couplings to change a diameter of a range of motion of the one of the pedals. Satisfying the range of motion threshold condition may indicate that the affected body part is strong enough or flexible enough to increase the range of motion allowed by the radially-adjustable couplings.
At block 502, the processing device may receive a set of angles from the one or more goniometers. The goniometer may measure angles of extension and/or bend between an upper body part (leg, arm, torso, neck, head, etc.) and a lower body part (leg, arm, torso, neck head, hand, feet, etc.) as the body parts are extended and/or bent during various sessions (e.g., pedaling session, walking session, extension session, bend session, etc.). The set of angles may be received while the user is pedaling one or more pedals of the electromechanical device.
At block 504, the processing device may transmit the set of angles to a computing device controlling the electromechanical device, via one or more network interface cards. The electromechanical device may be operated by a user rehabilitating an affected body part. For example, the user may have recently had surgery to repair a tear of an anterior cruciate ligament (ACL). Accordingly, the goniometer may be secured proximate to the knee by the affected ACL around the upper and lower leg.
In some embodiments, transmitting the set of angles to the computing device controlling the electromechanical device may cause the computing device, based on the set of angles satisfying a range of motion threshold condition to adjust a position of one of one or more pedals on a radially-adjustable coupling. The range of motion threshold condition may be set based on configuration information for a treatment plan received from the cloud-based computing system or the computing device executing the clinical portal. The position of the pedal is adjusted to increase a diameter of a range of motion transited by an upper body part (e.g., an upper leg), lower body part (e.g., a lower leg), and a joint (e.g., knee) of the user as the user operates the electromechanical device. In some embodiments, the position of the pedal may be adjusted in real-time while the user is operating the electromechanical device. In some embodiments, the user portal may present a notification to the user indicating that the position of the pedal should be modified, and the user may modify the position of the pedal and resume operating the electromechanical device with the modified pedal position.
In some embodiments, transmitting the set of angles to the computing device may cause the computing device executing the user portal to present the set of angles in a graphical animation of the lower body part and the upper body part moving in real-time during the extension or the bend. In some embodiments, the set of angles may be transmitted to the computing device executing the clinical portal, and the clinical portal may present the set of angles in a graphical animation of the lower body part and the upper body part moving in real-time during the extension or the bend. In addition, the set of angles may be presented in one or more graphs or charts on the clinical portal and/or the user portal to depict progress of the extension or bend for the user.
For example,
A right pedal 110 couples to a right radially-adjustable coupling 124 via a right pedal arm assembly 600 disposed within a cavity of the right radially-adjustable coupling 124. The right radially-adjustable coupling 124 may be disposed in a circular opening of a right outer cover 608 and the right pedal arm assembly 600 may be secured to the drive sub-assembly 602. An internal volume may be defined when the left outer cover 601 and the right outer cover 608 are secured together around the frame sub-assembly 604. The left outer cover 601 and the right outer cover 608 may also make up the frame of the electromechanical device 104 when secured together. The drive sub-assembly 602, top support sub-assembly 606, and pedal arm assemblies 600 may be disposed within the internal volume upon assembly. A storage compartment 610 may be secured to the frame.
Further, a computing device arm assembly 612 may be secured to the frame and a computing device mount assembly 614 may be secured to an end of the computing device arm assembly 612. The computing device 102 may be attached or detached from the computing device mount assembly 614 as desired during operation of the electromechanical device 104.
The stepper motor 700 includes a barrel and pin inserted through a hole in a motor mount 702. A shaft coupler 704 and a bearing 706 include through holes that receive an end of a first end lead screw 708. The lead screw 708 is disposed in a lower cavity of a pedal arm 712. The pin of the electric motor may be inserted in the through holes of the shaft coupler 704 and the bearing 706 to secure to the first end of the lead screw 708. The motor mount 702 may be secured to a frame of the pedal arm 712. Another bearing 706 may be disposed on another end of the lead screw 708. An electric slip ring 710 may be disposed on the pedal arm 712.
A linear rail 714 is disposed in and secured to an upper cavity of the pedal arm 712. The linear rail 714 may be used to move the pedal to different positions as described further below. A number of linear bearing blocks 716 are disposed onto a top rib and a bottom rib of the linear rail 714 such that the bearing blocks 716 can slide on the ribs. A spindle carriage 718 is secured to each of the bearing blocks 716. A support bearing 720 is used to provide support. The lead screw 708 may be inserted in through hole 722 of the spindle carriage 718. A spindle 724 may be secured at an end of the through hole 722 to house an end of the lead screw 708. A spindle 724 may be attached to a hole of the spindle carriage 718. When the pedal arm assembly 600 is assembled, the end of the spindle 724 may protrude through a hole of a pedal arm cover 726. When the stepper motor 700 turns on, the lead screw 708 can be rotated, thereby causing the spindle carriage 718 to move radially along the linear rail 714. As a result, the spindle 724 may radially traverse the opening of the pedal arm cover 726 as desired.
A virtual tachometer 1706 is also presented that measures the revolutions per time period (e.g., per minute) of the radially-adjustable couplings and displays the current speed at which the user is pedaling. For example, the tachometer 1706 includes areas 1708 (between 0 and 10 revolutions per minute and between 20 and 30 revolutions per minute) that the user should avoid according to their treatment plan. In the depicted example, the treatment plan specifies that the user should maintain the speed at between 10 and 20 revolutions per minute. The electromechanical device 104 transmits the speed to the computing device 102 and the needle 1710 moves in real-time as the user operates the pedals. Notifications are presented near the tachometer 1706, wherein such notifications may indicate that the user should keep the speed above a certain revolutions threshold 1732 (e.g., 10 RPM). If the computing device 102 receives a speed from the electromechanical device 104 and the speed is below the revolutions threshold 1732, the computing device 102 may control the electric motor to drive the radially-adjustable couplings to maintain the revolutions threshold 1732. The computing device 102 may also be made capable of determining the state of the user in a particular exercise comprising the treatment plan, such that if the state is to maintain the revolutions per minute, the notification will be issued, but further, such that if the state is indicative of starting to exercise, ending an exercise, or transitioning between different parts of an exercise, and crossing an otherwise undesirable or forbidden threshold and/or range of revolutions per minute would, in these particular or otherwise similar indictive states, be neither undesirable nor forbidden, and the computing device 102 would, in those instances, not issue a notification. As will readily be appreciated by a person of ordinary skill of the art in light of having read the present disclosure, as used herein, actions described as being performed in real-time include actions performed in near-real-time without departing from the scope and intent of the present disclosure.
In some embodiments, user interface 1900 of the user portal 118 can present an adjustment confirmation control configured to solicit a response regarding the patient's comfort level with the position of the body part or the force exerted by the body part. The comfort level may be indicated by a binary selection (e.g., comfortable or not comfortable). In some embodiments, the comfort level may be an analog value that may be indicated numerically or with an analog input control, such as a slider or a rotary knob. In some embodiments, the comfort level may be indicated by one of several different comfort level values, such as an integer number from 1 to 5. In some embodiments, the comfort level may be indicated using controls for the patient to maintain a setting or for the patient to change the setting. More specifically, the control for the patient to change the setting may provide for the patient to change the setting in either of two or more directions. For example, the controls may allow the patient to maintain the value of a setting, to increase the value of the setting, or to decrease the value of the setting.
Further, the clinical portal may include an option to control aspects of operating the electromechanical device 104. For example, while the user is engaged in a pedaling session or when the user is not engaged in the pedaling session, the clinician may use the clinical portal 126 to adjust a position of a pedal 110 based on angles of extension/bend received from the computing device 102 and/or the goniometer 106 in real-time. In response to determining an amount of force exerted by the user exceeds a target force threshold, such as the force threshold 1730, the clinical portal 126 may enable the clinician to adjust the amount of resistance provided by the electric motor 122. The clinical portal 126 may enable the clinician to adjust the speed of the electric motor 122, and so forth. The user interfaces can include additional and/or fewer components and are not limited to those illustrated in
The computer system 3100 comprises a processing device 3102, a main memory 3104 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 3106 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 3108, which communicate with each other via a bus 3110.
Processing device 3102 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 3102 may comprise a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 3102 may also comprise one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 3102 is configured to execute instructions for performing any of the operations and steps discussed herein.
The computer system 3100 may further comprise a network interface device (NID) 3112. The computer system 3100 also may comprise a video display 3114 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), one or more input devices 3116 (e.g., a keyboard and/or a mouse), and one or more speakers 3118 (e.g., a speaker). In one illustrative example, the video display 3114 and the input device(s) 3116 may be combined into a single component or device (e.g., an LCD touch screen).
The data storage device 3108 may comprise a computer-readable storage medium 3120 on which the instructions 3122 (e.g., implementing control system, user portal, clinical portal, and/or any functions performed by any device and/or component depicted in the FIGURES and described herein) embodying any one or more of the methodologies or functions described herein are stored. The instructions 3122 may also reside, completely or at least partially, within the main memory 3104 and/or within the processing device 3102 during execution thereof by the computer system 3100. As such, the main memory 3104 and the processing device 3102 also constitute computer-readable media. The instructions 3122 may further be transmitted or received over a network via the network interface device 3112.
While the computer-readable storage medium 3120 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and which cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media. The computer system 3100 can include additional and/or fewer components and is not limited to those illustrated in
In one aspect, a system for rehabilitation may include a monitoring device comprising a memory device 938 storing instructions 3122 and a network interface card 940, wherein the monitoring device is configured to detect information from a user 2108. The monitoring device may be a goniometer 106 coupled to the user 2108, a wristband 108 coupled to the user 2108, or a force sensor 942 coupled to a pedal of the electromechanical device 104. The system for rehabilitation further includes one or more processing devices 944 operatively coupled to the monitoring device. The one or more processing devices 944 can be configured to execute the instructions 3122 to receive configuration information specified in a treatment plan 1302 for rehabilitating a body part of the user 2108. For example, the body part of the user 2108 can be a first body part 2112 (e.g., a lower leg or a forearm), a second body part 2114 (e.g., an upper leg or an upper arm), a joint 2116 (e.g., a knee or an elbow), or any other desired body part or combination thereof. The configuration information may be received from a server 128 that received the configuration information presented on the computing device 102 from a clinical portal 126. The server 128 may include one or more processing devices, memory devices, data storage, and/or network interface cards. The one or more processing devices 944 can be configured to execute the instructions 3122 to receive the information from the monitoring device. The one or more processing devices 944 can be further configured to execute the instructions 3122 to transmit the configuration information and the information to a computing device 102 controlling an electromechanical device 104. One or more network interface cards 940 can be used for transmitting the configuration information and the information to the computing device 102.
The electromechanical device 104 may further comprise one or more pedals 110 rotatably coupled to one or more radially-adjustable couplings 124. The electromechanical device 104 may further comprise an electric motor 122 coupled to the one or more pedals 110 via the one or more radially-adjustable couplings 124. The electromechanical device 104 may further comprise a control system comprising one or more processing devices 944 operatively coupled to the electric motor 122. Responsive to a first trigger condition occurring, the one or more processing devices 944 can be configured to control the electric motor 122 to operate in a passive mode 1304 For example, the one or more processing devices 944 can execute instructions for the electric motor 122 to independently drive the one or more radially-adjustable couplings 124. Responsive to a second trigger condition occurring, for example, by measuring revolutions per time period of the one or more radially-adjustable couplings 124, such that when the measured revolutions per time period satisfy a threshold condition, the satisfaction of that condition can cause the electric motor 122 to drive the one or more radially-adjustable couplings 124. The one or more processing devices 944 may also be configured to control the electric motor 122 to operate in an active-assisted mode 1306. Responsive to a third trigger condition occurring, by providing resistance to rotation of the one or more radially-adjustable couplings 124, the one or more processing devices 944 may be configured to control the electric motor 122 to operate in a resistive mode 1310.
During an exercise session, the information may include one or more angles of a range of motion of the body part. The angles may include angles of extension 2222 and/or angles of bend 2122 of the body part detected by the monitoring device. The exercise session can include one or more particular exercises comprising the treatment plan 1302. The threshold condition may comprise a diameter (or radius, or other measurement) of the range of motion for the body part. The threshold condition may comprise a predetermined angle of a range of motion of the body part. Responsive to a difference between the angle and the predetermined angle, the one or more processing devices 944 may be configured to increase or decrease the angle by adjusting a position of the pedals 110. For example, if the difference is greater than, equal to, or less than a predetermined measurement (e.g., a difference measured), the processing device 944 may increase or decrease the diameter of the pedals 110 to adjust the movement of the body part(s) of the user 2108. As the user 2108 operates at least one of the pedals 110, the diameter may be transited by the body part of the user 2108. In other words, if a user is not following his or her treatment plan while operating the electromechanical device 104, the diameter of the pedals 110 may be changed during the exercise session to avoid risk of injury to the body part of the user 2108 and/or to enhance the rehabilitation results of the exercise session.
For respective periods of time during an exercise session based on the treatment plan 1302 for the user 2108 operating the electromechanical device 104, the electric motor 122 may operate in each of the passive mode 1304, the active-assisted mode 1306, and the resistive mode 1310. The first trigger condition, the second trigger condition, and the third trigger condition may be set based on the treatment plan 1302. The treatment plan 1302 can be generated by one or more machine learning models trained, based on input related to at least one of a procedure the user 2108 underwent or an attribute of the user 2108, to output the treatment plan 1302. The attribute may be a measurable characteristic of the user 2108. While operating in the passive mode 1304, the one or more processing devices 944 can control the electric motor 122 to independently drive the one or more radially-adjustable couplings 124 at a controlled speed specified in the treatment plan 1302 for the user 2108 operating the electromechanical device 104.
The one or more processing devices 944 may be further configured to receive, from the monitoring device, a value of a vital sign of the user 2108 (e.g., beats per minute of a heartrate, systolic and diastolic measurements of blood pressure, millimeters of mercury (mm Hg) of an oxygen level, degrees of temperature, etc.) as the user 2108 may operate the electromechanical device 104. Responsive to determining that the value of the vital sign exceeds a target value of the vital sign, the one or more processing devices 944 may control the electric motor 122 to reduce the resistance provided to rotate the one or more radially-adjustable couplings 124. Responsive to determining that the value of the vital sign falls below or is less than the target value of the vital sign, the one or more processing devices 944 may control the electric motor 122 to increase the resistance provided to rotate the one or more radially-adjustable couplings 124. The target value can be a target heartrate, a target blood pressure, a target oxygen level, a target temperature, or any other desired target value for a vital sign of the user 2108. The one or more processing devices 944 may be further configured to transmit the information to a second computing device 114 to cause the second computing device 114 to present the information on a user interface of a clinical portal 126. The one or more processing devices 944 can transmit the information via the one or more network interface cards 940. The transmitting the information to the computing device 102 may cause the computing device 102 to present a range of motion of the body part in a graphical animation 2104 of the body part moving in real-time.
The one or more processing devices 944 may be further configured to present, on a user interface 1300, the graphical animation 2104 of the body part as the user 2108 operates the electromechanical device 104. The graphical animation 2104 can include a plurality of measurements. The plurality of measurements can include a plurality of measurements of revolutions per time period of the one or more radially-adjustable couplings 124, a plurality of measurements of force on the one or more pedals 110, or any other desired measurement. The one or more processing devices 944 may be further configured to store a lowest value or a highest value of the plurality of measurements as a statistic or a plurality of statistics (e.g., speed, revolutions per minute, positions of pedals, forces on the pedals, vital signs, numbers of steps taken by user, ranges of motion, etc.) for the exercise session. The plurality of statistics can be stored for a plurality of exercise sessions specified by the treatment plan 1302. The one or more processing devices 944 may be further configured to present progress of the plurality of exercise sessions throughout the treatment plan 1302 via a graphical element (e.g., line graph, bar chart, etc.) on the user interface presenting the plurality of statistics.
The one or more processing devices 944 may be further configured to receive, from the monitoring device, a number of steps taken by the user 2108 over a certain time period. The one or more processing devices 944 may be further configured to determine if the number of steps satisfies a step threshold of the treatment plan 1302 for the user 2108. The one or more processing devices 944 may be further configured to present, on a user interface, the number of steps taken by the user 2108 and an indication of whether the number of steps satisfies the step threshold. The one or more processing devices 944 may be further configured to receive one or more measurements of force exerted by the user 2108 on the one or more pedals 110 during an exercise session. The measurements of force can be received from one or more force sensors 942 operatively coupled to one or more pedals 110 of the electromechanical device 104. Based on the one or more measurements of force, the one or more processing devices 944 may be configured to determine whether the user 2108 has disengaged from the electromechanical device 104. The one or more processing devices 944 may be further configured to lock an electric motor 122 in response to determining that the user 2108 has disengaged from the electromechanical device 104. Locking the electric motor 122 may stop the one or more pedals 110 from moving. The one or more processing devices 944 may be further configured to receive, from one or more force sensors 942 operatively coupled to one or more pedals 110 of the electromechanical device 104, one or more measurements of force exerted during an exercise session by the user 2108 on the one or more pedals 110. While the user 2108 engages the one or more pedals 110 during the exercise session, the one or more processing devices 944 may be further configured to present on a user interface 1300 the respective one or more measurements of force on each of the one or more pedals 110. For example, the respective one or more measurements of force can be presented on a separate respective graphical element of the user interface 1300.
When the one or more measurements of force satisfy a pressure threshold, the one or more processing devices 944 may be configured to present a notification, such as a first notification, on the user interface 1300. When the one or more measurements do not satisfy the pressure threshold, the one or more processing devices 944 may be further configured to present a notification, such as a second notification, on the user interface 1300. If the one or more measurements satisfy and/or do not satisfy the pressure threshold, one or neither notification may be presented or both notifications may be presented. For example, if, during an exercise session, the one or more measurements satisfy the pressure threshold at a first time, the first notification may be presented on the user interface 1300. If, during the exercise session, the one or more measurements do not satisfy the pressure threshold at a second time, the second notification may be presented to the user interface 1300. If either of these conditions is not met, the respective notification may not be presented on the user interface 1300.
The one or more processing devices 944 may be further configured, based on the one or more measurements of force, to provide an indicator to the user 2108. The indicator may comprise at least one of following: (1) providing haptic feedback in the one or more pedals 110, the one or more handles, or a seat; (2) providing visual feedback on the user interface 1300; (3) providing audio feedback via an audio subsystem of the electromechanical device 104; and/or (4) illuminating a warning light on the electromechanical device 104.
In another aspect, a system for rehabilitation may include a monitoring device comprising a memory device 938 storing instructions 3122 and a network interface card 940, wherein the monitoring device is configured to detect a range of motion, such as the angles of extension and/or bend of a body part of a user 2108. The system for rehabilitation further includes one or more processing devices 944 operatively coupled to the monitoring device. The one or more processing devices 944 can be configured to execute the instructions 3122 to receive configuration information specified in a treatment plan 1302 for rehabilitating the body part of the user 2108. The one or more processing devices 944 can be configured to execute the instructions 3122 to receive configuration information specified in a treatment plan 1302 for receiving the information, such as the angles of extension and/or bend of a user, from the monitoring device. The one or more processing devices 944 can be further configured to execute the instructions 3122 to transmit the configuration information and the information, such as the angles, to a computing device 102 controlling an electromechanical device 104, via the network interface card 940. The transmitting the information, such as the angles, to the computing device 102 can cause the computing device 102 to present the information in a graphical animation 2104 of the body part moving in real-time.
The information may comprise at least one of an angle of extension 2222 of the body part and an angle of bend 2122 of the body part. The information may be received while the user 2108 is engaging one or more pedals 110 of the electromechanical device 104. Based on the any of the information satisfying a threshold condition, the transmitting the information to the computing device 102 may cause the computing device 102 to adjust a position of a pedal coupled to a radially-adjustable coupling of the electromechanical device 104. For example, if a value of the angle of extension 2222 (e.g., 45 degrees) is less than a predetermined value of the threshold condition (e.g., 50 degrees), the computing device 102 may adjust a position of the pedal to cause the body part to extend at greater angle (e.g., greater than 45 degrees but less than or equal to 50 degrees). If a value of the angle of extension 2222 (e.g., 55 degrees) is greater than a predetermined value of the threshold condition (e.g., 50 degrees), the computing device 102 may adjust a position of the pedal to cause the body part to extend at smaller angle (e.g., less than 55 degrees but greater than or equal to 50 degrees).
In yet another aspect, a system for rehabilitation may include a monitoring device comprising a memory device 938 storing instructions 3122 and a network interface card 940, wherein the monitoring device is configured to detect a range of motion of a body part of a user 2108. The system for rehabilitation may further include an electromechanical device 104 comprising one or more pedals 110 coupled to one or more radially-adjustable couplings 124. The system for rehabilitation may further include an electric motor 122 coupled to the one or more pedals 110 via the one or more radially-adjustable couplings 124. The system for rehabilitation may further include a control system comprising one or more processing devices 944 operatively coupled to both the monitoring device and the electric motor 122. The one or more processing devices 944 can be configured to receive configuration information for an exercise session, set a resistance parameter and a maximum pedal force parameter based on the configuration information for the exercise session, and receive the range of motion from the monitoring device. The one or more processing devices 944 can be further configured to transmit the configuration information and the range of motion to a computing device 102 controlling the electromechanical device 104, via the network interface card 940. The one or more processing devices 944 can have one or more network interface cards 940 to transmit the configuration information and the range of motion to a computing device 102. The one or more processing devices 944 can be further configured to measure force applied to pedals 110 of the electromechanical device 104 as the user 2108 pedals 110 the electromechanical device 104. Based on the resistance parameter, the electric motor 122 can provide resistance during the exercise session. The one or more processing devices 944 can be further configured to determine whether the measured force exceeds the maximum pedal force parameter. During the exercise session and responsive to determining that the measured force exceeds the maximum pedal force parameter, the one or more processing devices 944 can be further configured to reduce the resistance parameter so that the electric motor 122 applies less resistance to maintain a revolutions per time period threshold. During the exercise session and responsive to determining that the measured force is less than the maximum pedal force parameter, the one or more processing devices 944 can be further configured to increase the resistance parameter so that the electric motor 122 applies more resistance to maintain the revolutions per time period threshold.
Clause 1. A system for rehabilitation, comprising:
Clause 2. The system of any preceding clause, wherein the electromechanical device further comprises:
Clause 3. The system of any preceding clause, wherein during an exercise session, the information includes an angle of a range of motion of the body part detected by the monitoring device;
Clause 4. The system of any preceding clause, wherein, during an exercise session based on the treatment plan for the user operating the electromechanical device, the electric motor operates for respective periods of time in each of the passive mode, the active-assisted mode, and the resistive mode.
Clause 5. The system of any preceding clause, wherein the first trigger condition, the second trigger condition, and the third trigger condition are set based on the treatment plan, wherein the treatment plan is generated based on input related to at least one of a procedure the user underwent or an attribute of the user by one or more machine learning models trained to output the treatment plan.
Clause 6. The system of any preceding clause, while operating in the passive mode, the one or more processing devices are configured to control the electric motor to independently drive the one or more radially-adjustable couplings at a controlled speed specified in the treatment plan for the user operating the electromechanical device.
Clause 7. The system of any preceding clause, wherein the one or more processing devices are further configured to:
Clause 8. The system of any preceding clause, wherein, to cause the second computing device to present the information on a user interface of a clinical portal, the one or more processing devices are further configured to transmit, via the network interface card, the information to a second computing device.
Clause 9. The system of any preceding clause, wherein the transmitting the information to the computing device causes the computing device to present a range of motion of the body part in a graphical animation of the body part moving in real-time.
Clause 10. The system of any preceding clause, wherein the one or more processing devices are further configured to:
Clause 11. The system of any preceding clause, wherein the one or more processing devices are further configured to:
Clause 12. The system of any preceding clause, wherein the one or more processing devices are further configured to:
Clause 13. The system of any preceding clause, wherein the one or more processing devices are further configured to:
Clause 14. The system of any preceding clause, wherein the one or more processing devices are further configured to present a first notification on the user interface when the one or more measurements of force satisfy a pressure threshold and present a second notification on the user interface when the one or more measurements do not satisfy the pressure threshold.
Clause 15. The system of any preceding clause, wherein the one or more processing devices are further configured to provide an indicator to the user based on the one or more measurements of force, wherein the indicator comprises at least one of (1) providing haptic feedback in the one or more pedals, one or more handles, or a seat, (2) providing visual feedback on the user interface, (3) providing audio feedback via an audio subsystem of the electromechanical device, and (4) illuminating a warning light on the electromechanical device.
Clause 16. The system of any preceding clause, wherein the configuration information is received from a server that received the configuration information presented on the computing device from a clinical portal.
Clause 17. The system of any preceding clause, wherein the monitoring device is a goniometer coupled to the user, a wristband coupled to the user, or a force sensor coupled to a pedal of the electromechanical device.
Clause 18. A system for rehabilitation, comprising:
Clause 19. The system of any preceding clause, wherein the information comprises at least one of an angle of extension of the body part and an angle of bend of the body part; and wherein the information is received while the user is engaging one or more pedals of the electromechanical device.
Clause 20. The system of any preceding clause, wherein, based on any of the information satisfying a threshold condition, the transmitting the information to the computing device causes the computing device to adjust a position of a pedal coupled to a radially-adjustable coupling of the electromechanical device.
Clause 21. A system for rehabilitation, comprising:
Clause 22. The system of any preceding clause, wherein the one or more processing devices are further configured to:
Clause 23. The system of any preceding clause, wherein the one or more processing devices are further configured to:
No part of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle.
The foregoing description, for purposes of explanation, use specific nomenclature to provide a thorough understanding of the described embodiments. However, it should be apparent to one skilled in the art that the specific details are not required in order to practice the described embodiments. Thus, the foregoing descriptions of specific embodiments are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It should be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings.
The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Once the above disclosure is fully appreciated, numerous variations and modifications will become apparent to those skilled in the art. It is intended that the following claims be interpreted to embrace all such variations and modifications.
This application claims priority to and the benefit of U.S. Provisional Application Patent Ser. No. 62/816,503, filed Mar. 11, 2019, the entire disclosure of which is hereby incorporated by reference.
Number | Name | Date | Kind |
---|---|---|---|
59915 | Lallement | Nov 1866 | A |
363522 | Knous | May 1887 | A |
446671 | Elliott | Feb 1891 | A |
610157 | Campbell | Aug 1898 | A |
631276 | Bulova | Aug 1899 | A |
823712 | Uhlmann | Jun 1906 | A |
1149029 | Clark | Aug 1915 | A |
1227743 | Burgedorfp | May 1917 | A |
1784230 | Freeman | Dec 1930 | A |
3081645 | Bergfors | Mar 1963 | A |
3100640 | Weitzel | Aug 1963 | A |
3137014 | Meucci | Jun 1964 | A |
3143316 | Shapiro | Aug 1964 | A |
3713438 | Knutsen | Jan 1973 | A |
3744480 | Gause et al. | Jul 1973 | A |
3888136 | Lapeyre | Jun 1975 | A |
4079957 | Blease | Mar 1978 | A |
4408613 | Relyea | Oct 1983 | A |
4436097 | Cunningham | Mar 1984 | A |
4446753 | Nagano | May 1984 | A |
4477072 | DeCloux | Oct 1984 | A |
4499900 | Petrofsky et al. | Feb 1985 | A |
4509742 | Cones | Apr 1985 | A |
4606241 | Fredriksson | Aug 1986 | A |
4611807 | Castillo | Sep 1986 | A |
4616823 | Yang | Oct 1986 | A |
4648287 | Preskitt | Mar 1987 | A |
4673178 | Dwight | Jun 1987 | A |
4822032 | Whitmore et al. | Apr 1989 | A |
4824104 | Bloch | Apr 1989 | A |
4850245 | Feamster et al. | Jul 1989 | A |
4858942 | Rodriguez | Aug 1989 | A |
4869497 | Stewart et al. | Sep 1989 | A |
4915374 | Watkins | Apr 1990 | A |
4930768 | Lapcevic | Jun 1990 | A |
4932650 | Bingham et al. | Jun 1990 | A |
4961570 | Chang | Oct 1990 | A |
5137501 | Mertesdorf | Aug 1992 | A |
5161430 | Febey | Nov 1992 | A |
5202794 | Schnee et al. | Apr 1993 | A |
5240417 | Smithson et al. | Aug 1993 | A |
5247853 | Dalebout | Sep 1993 | A |
5256115 | Scholder et al. | Oct 1993 | A |
5256117 | Potts et al. | Oct 1993 | A |
5282748 | Little | Feb 1994 | A |
5284131 | Gray | Feb 1994 | A |
5316532 | Butler | May 1994 | A |
5318487 | Golen | Jun 1994 | A |
5324241 | Artigues et al. | Jun 1994 | A |
5336147 | Sweeney, III | Aug 1994 | A |
5338272 | Sweeney, III | Aug 1994 | A |
5356356 | Hildebrandt | Oct 1994 | A |
5361649 | Slocum, Jr. | Nov 1994 | A |
D353421 | Gallivan | Dec 1994 | S |
D359777 | Hildebrandt | Jun 1995 | S |
5429140 | Burdea et al. | Jul 1995 | A |
5458022 | Mattfeld et al. | Oct 1995 | A |
5487713 | Butler | Jan 1996 | A |
5566589 | Buck | Oct 1996 | A |
5580338 | Scelta et al. | Dec 1996 | A |
5676349 | Wilson | Oct 1997 | A |
5685804 | Whan-Tong et al. | Nov 1997 | A |
5738636 | Saringer et al. | Apr 1998 | A |
5860941 | Saringer et al. | Jan 1999 | A |
5950813 | Hoskins et al. | Sep 1999 | A |
6007459 | Burgess | Dec 1999 | A |
D421075 | Hildebrandt | Feb 2000 | S |
6053847 | Stearns et al. | Apr 2000 | A |
6077201 | Cheng | Jun 2000 | A |
6102834 | Chen | Aug 2000 | A |
6110130 | Kramer | Aug 2000 | A |
6155958 | Goldberg | Dec 2000 | A |
6162189 | Girone et al. | Dec 2000 | A |
6182029 | Friedman | Jan 2001 | B1 |
6253638 | Bermudez | Jul 2001 | B1 |
6267735 | Blanchard et al. | Jul 2001 | B1 |
6273863 | Avni et al. | Aug 2001 | B1 |
6371891 | Speas | Apr 2002 | B1 |
6413190 | Wood et al. | Jul 2002 | B1 |
6430436 | Richter | Aug 2002 | B1 |
6436058 | Krahner et al. | Aug 2002 | B1 |
6450923 | Vatti | Sep 2002 | B1 |
6474193 | Farney | Nov 2002 | B1 |
6491649 | Ombrellaro | Dec 2002 | B1 |
6514085 | Slattery et al. | Feb 2003 | B2 |
6535861 | OConnor et al. | Mar 2003 | B1 |
6543309 | Heim | Apr 2003 | B2 |
D475424 | Lee | Jun 2003 | S |
6589139 | Butterworth | Jul 2003 | B1 |
6601016 | Brown et al. | Jul 2003 | B1 |
6602191 | Quy | Aug 2003 | B2 |
6613000 | Reinkensmeyer et al. | Sep 2003 | B1 |
6626800 | Casler | Sep 2003 | B1 |
6626805 | Lightbody | Sep 2003 | B1 |
6640122 | Manoli | Oct 2003 | B2 |
D482416 | Yang | Nov 2003 | S |
6640662 | Baxter | Nov 2003 | B1 |
6652425 | Martin et al. | Nov 2003 | B1 |
D484931 | Tsai | Jan 2004 | S |
6820517 | Farney | Nov 2004 | B1 |
6865969 | Stevens | Mar 2005 | B2 |
6890312 | Priester et al. | May 2005 | B1 |
6895834 | Baatz | May 2005 | B1 |
6902513 | McClure | Jun 2005 | B1 |
7058453 | Nelson et al. | Jun 2006 | B2 |
7063643 | Arai | Jun 2006 | B2 |
7156665 | OConnor et al. | Jan 2007 | B1 |
7156780 | Fuchs | Jan 2007 | B1 |
7169085 | Killin et al. | Jan 2007 | B1 |
7204788 | Andrews | Apr 2007 | B2 |
7209886 | Kimmel | Apr 2007 | B2 |
7226394 | Johnson | Jun 2007 | B2 |
RE39904 | Lee | Oct 2007 | E |
7406003 | Burkhardt et al. | Jul 2008 | B2 |
D575836 | Hsiao | Aug 2008 | S |
7507188 | Nurre | Mar 2009 | B2 |
7594879 | Johnson | Sep 2009 | B2 |
7628730 | Watterson et al. | Dec 2009 | B1 |
D610635 | Hildebrandt | Feb 2010 | S |
7726034 | Wixey | Jun 2010 | B2 |
7778851 | Schoenberg et al. | Aug 2010 | B2 |
7809601 | Shaya et al. | Oct 2010 | B2 |
7815551 | Merli | Oct 2010 | B2 |
7833135 | Radow et al. | Nov 2010 | B2 |
7837472 | Elsmore et al. | Nov 2010 | B1 |
7955219 | Birrell et al. | Jun 2011 | B2 |
7969315 | Ross et al. | Jun 2011 | B1 |
7974689 | Volpe et al. | Jul 2011 | B2 |
7988599 | Ainsworth et al. | Aug 2011 | B2 |
8012107 | Einav et al. | Sep 2011 | B2 |
8021270 | D'Eredita | Sep 2011 | B2 |
8038578 | Olrik et al. | Oct 2011 | B2 |
8079937 | Bedell | Dec 2011 | B2 |
8083651 | Lu | Dec 2011 | B2 |
8113991 | Kutliroff | Feb 2012 | B2 |
8177732 | Einav et al. | May 2012 | B2 |
8287434 | Zavadsky et al. | Oct 2012 | B2 |
8298123 | Hickman | Oct 2012 | B2 |
8371990 | Shea | Feb 2013 | B2 |
8419593 | Ainsworth et al. | Apr 2013 | B2 |
8465398 | Lee et al. | Jun 2013 | B2 |
8506458 | Dugan | Aug 2013 | B2 |
8515777 | Rajasenan | Aug 2013 | B1 |
8540515 | Williams et al. | Sep 2013 | B2 |
8540516 | Williams et al. | Sep 2013 | B2 |
8556778 | Dugan | Oct 2013 | B1 |
8607465 | Edwards | Dec 2013 | B1 |
8613689 | Dyer et al. | Dec 2013 | B2 |
8672812 | Dugan | Mar 2014 | B2 |
8751264 | Beraja et al. | Jun 2014 | B2 |
8784273 | Dugan | Jul 2014 | B2 |
8823448 | Shen | Sep 2014 | B1 |
8845493 | Watterson et al. | Sep 2014 | B2 |
8849681 | Hargrove et al. | Sep 2014 | B2 |
8864628 | Boyette | Oct 2014 | B2 |
8893287 | Gjonej et al. | Nov 2014 | B2 |
8911327 | Boyette | Dec 2014 | B1 |
8979711 | Dugan | Mar 2015 | B2 |
9004598 | Weber | Apr 2015 | B2 |
9167281 | Petrov et al. | Oct 2015 | B2 |
9248071 | Benda et al. | Feb 2016 | B1 |
9272185 | Dugan | Mar 2016 | B2 |
9283434 | Wu | Mar 2016 | B1 |
9311789 | Gwin | Apr 2016 | B1 |
9312907 | Auchinleck et al. | Apr 2016 | B2 |
9367668 | Flynt et al. | Jun 2016 | B2 |
9409054 | Dugan | Aug 2016 | B2 |
9443205 | Wall | Sep 2016 | B2 |
9474935 | Abbondanza et al. | Oct 2016 | B2 |
9480873 | Chuang | Nov 2016 | B2 |
9481428 | Gros et al. | Nov 2016 | B2 |
9514277 | Hassing et al. | Dec 2016 | B2 |
9566472 | Dugan | Feb 2017 | B2 |
9579056 | Rosenbek et al. | Feb 2017 | B2 |
9629558 | Yuen et al. | Apr 2017 | B2 |
9640057 | Ross | May 2017 | B1 |
9707147 | Levital et al. | Jul 2017 | B2 |
D793494 | Mansfield et al. | Aug 2017 | S |
D794142 | Zhou | Aug 2017 | S |
9717947 | Lin | Aug 2017 | B2 |
9737761 | Govindarajan | Aug 2017 | B1 |
9757612 | Weber | Sep 2017 | B2 |
9782621 | Chiang et al. | Oct 2017 | B2 |
9802076 | Murray et al. | Oct 2017 | B2 |
9802081 | Ridgel et al. | Oct 2017 | B2 |
9813239 | Chee et al. | Nov 2017 | B2 |
9826908 | Wu | Nov 2017 | B2 |
9827445 | Marcos et al. | Nov 2017 | B2 |
9849337 | Roman et al. | Dec 2017 | B2 |
9868028 | Shin | Jan 2018 | B2 |
9872087 | DelloStritto et al. | Jan 2018 | B2 |
9872637 | Kording et al. | Jan 2018 | B2 |
9914053 | Dugan | Mar 2018 | B2 |
9919198 | Romeo et al. | Mar 2018 | B2 |
9937382 | Dugan | Apr 2018 | B2 |
9939784 | Berardinelli | Apr 2018 | B1 |
9977587 | Mountain | May 2018 | B2 |
9993181 | Ross | Jun 2018 | B2 |
10004946 | Ross | Jun 2018 | B2 |
D826349 | Oblamski | Aug 2018 | S |
10055550 | Goetz | Aug 2018 | B2 |
10058473 | Oshima et al. | Aug 2018 | B2 |
10074148 | Cashman et al. | Sep 2018 | B2 |
10089443 | Miller et al. | Oct 2018 | B2 |
10111643 | Shulhauser et al. | Oct 2018 | B2 |
10130298 | Mokaya et al. | Nov 2018 | B2 |
10137328 | Baudhuin | Nov 2018 | B2 |
10143395 | Chakravarthy et al. | Dec 2018 | B2 |
10155134 | Dugan | Dec 2018 | B2 |
10159872 | Sasaki et al. | Dec 2018 | B2 |
10173094 | Gomberg et al. | Jan 2019 | B2 |
10173095 | Gomberg et al. | Jan 2019 | B2 |
10173096 | Gomberg et al. | Jan 2019 | B2 |
10173097 | Gomberg et al. | Jan 2019 | B2 |
10198928 | Ross et al. | Feb 2019 | B1 |
10226663 | Gomberg et al. | Mar 2019 | B2 |
10231664 | Ganesh | Mar 2019 | B2 |
10244990 | Hu et al. | Apr 2019 | B2 |
10254804 | Dusan | Apr 2019 | B2 |
10258823 | Cole | Apr 2019 | B2 |
10325070 | Beale et al. | Jun 2019 | B2 |
10327697 | Stein et al. | Jun 2019 | B1 |
10369021 | Zoss et al. | Aug 2019 | B2 |
10380866 | Ross et al. | Aug 2019 | B1 |
10413238 | Cooper | Sep 2019 | B1 |
10424033 | Romeo | Sep 2019 | B2 |
10430552 | Mihai | Oct 2019 | B2 |
D866957 | Ross et al. | Nov 2019 | S |
10468131 | Macoviak et al. | Nov 2019 | B2 |
10475323 | Ross | Nov 2019 | B1 |
10475537 | Purdie et al. | Nov 2019 | B2 |
10492977 | Kapure et al. | Dec 2019 | B2 |
10507358 | Kinnunen et al. | Dec 2019 | B2 |
10542914 | Forth et al. | Jan 2020 | B2 |
10546467 | Luciano, Jr. et al. | Jan 2020 | B1 |
10569122 | Johnson | Feb 2020 | B2 |
10572626 | Balram | Feb 2020 | B2 |
10576331 | Kuo | Mar 2020 | B2 |
10581896 | Nachenberg | Mar 2020 | B2 |
10625114 | Ercanbrack | Apr 2020 | B2 |
10646746 | Gomberg et al. | May 2020 | B1 |
10660534 | Lee et al. | May 2020 | B2 |
10678890 | Bitran et al. | Jun 2020 | B2 |
10685092 | Paparella et al. | Jun 2020 | B2 |
10705619 | Johri | Jul 2020 | B2 |
10777200 | Will et al. | Sep 2020 | B2 |
D899605 | Ross et al. | Oct 2020 | S |
10792495 | Izvorski et al. | Oct 2020 | B2 |
10867695 | Neagle | Dec 2020 | B2 |
10874905 | Belson et al. | Dec 2020 | B2 |
D907143 | Ach et al. | Jan 2021 | S |
10881911 | Kwon et al. | Jan 2021 | B2 |
10918332 | Belson et al. | Feb 2021 | B2 |
10931643 | Neumann | Feb 2021 | B1 |
10987176 | Poltaretskyi et al. | Apr 2021 | B2 |
10991463 | Kutzko et al. | Apr 2021 | B2 |
11000735 | Orady et al. | May 2021 | B2 |
11040238 | Colburn | Jun 2021 | B2 |
11045709 | Putnam | Jun 2021 | B2 |
11065170 | Yang et al. | Jul 2021 | B2 |
11065527 | Putnam | Jul 2021 | B2 |
11069436 | Mason et al. | Jul 2021 | B2 |
11071597 | Posnack et al. | Jul 2021 | B2 |
11075000 | Mason et al. | Jul 2021 | B2 |
D928635 | Hacking et al. | Aug 2021 | S |
11087865 | Mason et al. | Aug 2021 | B2 |
11101028 | Mason et al. | Aug 2021 | B2 |
11107591 | Mason | Aug 2021 | B1 |
11139060 | Mason et al. | Oct 2021 | B2 |
11185735 | Arn et al. | Nov 2021 | B2 |
D939096 | Lee | Dec 2021 | S |
D939644 | Ach et al. | Dec 2021 | S |
D940797 | Ach et al. | Jan 2022 | S |
D940891 | Lee | Jan 2022 | S |
11229727 | Tatonetti | Jan 2022 | B2 |
11270795 | Mason et al. | Mar 2022 | B2 |
11272879 | Wiedenhoefer et al. | Mar 2022 | B2 |
11278766 | Lee | Mar 2022 | B2 |
11282599 | Mason et al. | Mar 2022 | B2 |
11282604 | Mason et al. | Mar 2022 | B2 |
11282608 | Mason et al. | Mar 2022 | B2 |
11284797 | Mason et al. | Mar 2022 | B2 |
D948639 | Ach et al. | Apr 2022 | S |
11295848 | Mason et al. | Apr 2022 | B2 |
11298284 | Bayerlein | Apr 2022 | B2 |
11309085 | Mason et al. | Apr 2022 | B2 |
11317975 | Mason et al. | May 2022 | B2 |
11325005 | Mason et al. | May 2022 | B2 |
11328807 | Mason et al. | May 2022 | B2 |
11337648 | Mason | May 2022 | B2 |
11348683 | Guaneri et al. | May 2022 | B2 |
11376470 | Weldemariam | Jul 2022 | B2 |
11404150 | Guaneri et al. | Aug 2022 | B2 |
11410768 | Mason et al. | Aug 2022 | B2 |
11422841 | Jeong | Aug 2022 | B2 |
11495355 | McNutt et al. | Nov 2022 | B2 |
11508258 | Nakashima et al. | Nov 2022 | B2 |
11508482 | Mason et al. | Nov 2022 | B2 |
11515021 | Mason | Nov 2022 | B2 |
11515028 | Mason | Nov 2022 | B2 |
11524210 | Kim et al. | Dec 2022 | B2 |
11527326 | McNair et al. | Dec 2022 | B2 |
11532402 | Farley et al. | Dec 2022 | B2 |
11534654 | Silcock et al. | Dec 2022 | B2 |
D976339 | Li | Jan 2023 | S |
11541274 | Hacking | Jan 2023 | B2 |
11636944 | Hanrahan et al. | Apr 2023 | B2 |
11663673 | Pyles | May 2023 | B2 |
11701548 | Posnack et al. | Jul 2023 | B2 |
20010044573 | Manoli | Nov 2001 | A1 |
20020072452 | Torkelson | Jun 2002 | A1 |
20020143279 | Porter et al. | Oct 2002 | A1 |
20020160883 | Dugan | Oct 2002 | A1 |
20020183599 | Castellanos | Dec 2002 | A1 |
20030013072 | Thomas | Jan 2003 | A1 |
20030036683 | Kehr et al. | Feb 2003 | A1 |
20030045402 | Pyle | Mar 2003 | A1 |
20030064863 | Chen | Apr 2003 | A1 |
20030083596 | Kramer et al. | May 2003 | A1 |
20030092536 | Romanelli et al. | May 2003 | A1 |
20030109814 | Rummerfield | Jun 2003 | A1 |
20030181832 | Carnahan et al. | Sep 2003 | A1 |
20040102931 | Ellis et al. | May 2004 | A1 |
20040106502 | Sher | Jun 2004 | A1 |
20040147969 | Mann et al. | Jul 2004 | A1 |
20040172093 | Rummerfield | Sep 2004 | A1 |
20040194572 | Kim | Oct 2004 | A1 |
20040204959 | Moreano et al. | Oct 2004 | A1 |
20050020411 | Andrews | Jan 2005 | A1 |
20050043153 | Krietzman | Feb 2005 | A1 |
20050049122 | Vallone et al. | Mar 2005 | A1 |
20050085346 | Johnson | Apr 2005 | A1 |
20050085353 | Johnson | Apr 2005 | A1 |
20050115561 | Stahmann | Jun 2005 | A1 |
20050274220 | Reboullet | Dec 2005 | A1 |
20060003871 | Houghton | Jan 2006 | A1 |
20060046905 | Doody, Jr. | Mar 2006 | A1 |
20060058648 | Meier | Mar 2006 | A1 |
20060064136 | Wang | Mar 2006 | A1 |
20060064329 | Abolfathi et al. | Mar 2006 | A1 |
20060199700 | LaStayo | Sep 2006 | A1 |
20060247095 | Rummerfield | Nov 2006 | A1 |
20070042868 | Fisher et al. | Feb 2007 | A1 |
20070118389 | Shipon | May 2007 | A1 |
20070137307 | Gruben et al. | Jun 2007 | A1 |
20070173392 | Stanford | Jul 2007 | A1 |
20070184414 | Perez | Aug 2007 | A1 |
20070194939 | Alvarez et al. | Aug 2007 | A1 |
20070219059 | Schwartz | Sep 2007 | A1 |
20070287597 | Cameron | Dec 2007 | A1 |
20080021834 | Holla et al. | Jan 2008 | A1 |
20080082356 | Friedlander et al. | Apr 2008 | A1 |
20080096726 | Riley et al. | Apr 2008 | A1 |
20080153592 | James-Herbert | Jun 2008 | A1 |
20080161166 | Lo | Jul 2008 | A1 |
20080161733 | Einav et al. | Jul 2008 | A1 |
20080221485 | Lissek et al. | Sep 2008 | A1 |
20080281633 | Burdea et al. | Nov 2008 | A1 |
20080300914 | Karkanias et al. | Dec 2008 | A1 |
20090011907 | Radow et al. | Jan 2009 | A1 |
20090046056 | Rosenberg et al. | Feb 2009 | A1 |
20090058635 | LaLonde et al. | Mar 2009 | A1 |
20090070138 | Langheier et al. | Mar 2009 | A1 |
20090211395 | Mul'e | Aug 2009 | A1 |
20090270227 | Ashby et al. | Oct 2009 | A1 |
20090287503 | Angell et al. | Nov 2009 | A1 |
20090299766 | Friedlander et al. | Dec 2009 | A1 |
20100048358 | Tchao et al. | Feb 2010 | A1 |
20100076786 | Dalton et al. | Mar 2010 | A1 |
20100121160 | Stark et al. | May 2010 | A1 |
20100173747 | Chen et al. | Jul 2010 | A1 |
20100216168 | Heinzman et al. | Aug 2010 | A1 |
20100248899 | Bedell et al. | Sep 2010 | A1 |
20100248905 | Lu | Sep 2010 | A1 |
20100268304 | Matos | Oct 2010 | A1 |
20100298102 | Bosecker et al. | Nov 2010 | A1 |
20100326207 | Topel | Dec 2010 | A1 |
20110010188 | Yoshikawa et al. | Jan 2011 | A1 |
20110047108 | Chakrabarty et al. | Feb 2011 | A1 |
20110119212 | De Bruin et al. | May 2011 | A1 |
20110172059 | Watterson et al. | Jul 2011 | A1 |
20110195819 | Shaw | Aug 2011 | A1 |
20110218814 | Coats | Sep 2011 | A1 |
20110275483 | Dugan | Nov 2011 | A1 |
20110306846 | Osorio | Dec 2011 | A1 |
20120041771 | Cosentino et al. | Feb 2012 | A1 |
20120065987 | Farooq et al. | Mar 2012 | A1 |
20120116258 | Lee | May 2012 | A1 |
20120167709 | Chen et al. | Jul 2012 | A1 |
20120183939 | Aragones et al. | Jul 2012 | A1 |
20120190502 | Paulus et al. | Jul 2012 | A1 |
20120232438 | Cataldi et al. | Sep 2012 | A1 |
20120259648 | Mallon et al. | Oct 2012 | A1 |
20120295240 | Walker et al. | Nov 2012 | A1 |
20120296455 | Ohnemus et al. | Nov 2012 | A1 |
20120310667 | Altman et al. | Dec 2012 | A1 |
20130123667 | Komatireddy et al. | May 2013 | A1 |
20130137550 | Skinner et al. | May 2013 | A1 |
20130178334 | Brammer | Jul 2013 | A1 |
20130211281 | Ross et al. | Aug 2013 | A1 |
20130253943 | Lee et al. | Sep 2013 | A1 |
20130274069 | Watterson et al. | Oct 2013 | A1 |
20130296987 | Rogers et al. | Nov 2013 | A1 |
20130318027 | Almogy et al. | Nov 2013 | A1 |
20130332616 | Landwehr | Dec 2013 | A1 |
20130345025 | van der Merwe | Dec 2013 | A1 |
20140006042 | Keefe et al. | Jan 2014 | A1 |
20140011640 | Dugan | Jan 2014 | A1 |
20140073486 | Ahmed et al. | Mar 2014 | A1 |
20140089836 | Damani et al. | Mar 2014 | A1 |
20140113768 | Lin et al. | Apr 2014 | A1 |
20140155129 | Dugan | Jun 2014 | A1 |
20140172442 | Broderick | Jun 2014 | A1 |
20140172460 | Kohli | Jun 2014 | A1 |
20140188009 | Lange et al. | Jul 2014 | A1 |
20140194250 | Reich et al. | Jul 2014 | A1 |
20140194251 | Reich et al. | Jul 2014 | A1 |
20140207264 | Quy | Jul 2014 | A1 |
20140207486 | Carty et al. | Jul 2014 | A1 |
20140228649 | Rayner et al. | Aug 2014 | A1 |
20140246499 | Proud et al. | Sep 2014 | A1 |
20140256511 | Smith | Sep 2014 | A1 |
20140257837 | Walker et al. | Sep 2014 | A1 |
20140274565 | Boyette et al. | Sep 2014 | A1 |
20140274622 | Leonhard | Sep 2014 | A1 |
20140303540 | Baym | Oct 2014 | A1 |
20140309083 | Dugan | Oct 2014 | A1 |
20140315689 | Vauquelin et al. | Oct 2014 | A1 |
20140322686 | Kang | Oct 2014 | A1 |
20140371816 | Matos | Dec 2014 | A1 |
20150025816 | Ross | Jan 2015 | A1 |
20150045700 | Cavanagh et al. | Feb 2015 | A1 |
20150073814 | Linebaugh | Mar 2015 | A1 |
20150088544 | Goldberg | Mar 2015 | A1 |
20150094192 | Skwortsow et al. | Apr 2015 | A1 |
20150099458 | Weisner et al. | Apr 2015 | A1 |
20150099952 | Lain et al. | Apr 2015 | A1 |
20150112230 | Iglesias | Apr 2015 | A1 |
20150130830 | Nagasaki | May 2015 | A1 |
20150141200 | Murray et al. | May 2015 | A1 |
20150149217 | Kaburagi | May 2015 | A1 |
20150151162 | Dugan | Jun 2015 | A1 |
20150158549 | Gros et al. | Jun 2015 | A1 |
20150161331 | Oleynik | Jun 2015 | A1 |
20150196805 | Koduri | Jul 2015 | A1 |
20150257679 | Ross | Sep 2015 | A1 |
20150265209 | Zhang | Sep 2015 | A1 |
20150290061 | Stafford et al. | Oct 2015 | A1 |
20150339442 | Oleynik | Nov 2015 | A1 |
20150341812 | Dion et al. | Nov 2015 | A1 |
20150351664 | Ross | Dec 2015 | A1 |
20150351665 | Ross | Dec 2015 | A1 |
20150360069 | Marti et al. | Dec 2015 | A1 |
20150379232 | Mainwaring et al. | Dec 2015 | A1 |
20150379430 | Dirac et al. | Dec 2015 | A1 |
20160007885 | Basta et al. | Jan 2016 | A1 |
20160023081 | Popa-Simil | Jan 2016 | A1 |
20160045170 | Migita | Feb 2016 | A1 |
20160096073 | Rahman et al. | Apr 2016 | A1 |
20160117471 | Belt et al. | Apr 2016 | A1 |
20160140319 | Stark | May 2016 | A1 |
20160143593 | Fu et al. | May 2016 | A1 |
20160151670 | Dugan | Jun 2016 | A1 |
20160166833 | Bum | Jun 2016 | A1 |
20160166881 | Ridgel et al. | Jun 2016 | A1 |
20160193306 | Rabovsky et al. | Jul 2016 | A1 |
20160213924 | Coleman | Jul 2016 | A1 |
20160275259 | Nolan et al. | Sep 2016 | A1 |
20160287166 | Tran | Oct 2016 | A1 |
20160302721 | Wiedenhoefer et al. | Oct 2016 | A1 |
20160317869 | Dugan | Nov 2016 | A1 |
20160322078 | Bose et al. | Nov 2016 | A1 |
20160325140 | Wu | Nov 2016 | A1 |
20160332028 | Melnik | Nov 2016 | A1 |
20160354636 | Jang | Dec 2016 | A1 |
20160361597 | Cole et al. | Dec 2016 | A1 |
20160373477 | Moyle | Dec 2016 | A1 |
20170004260 | Moturu et al. | Jan 2017 | A1 |
20170014671 | Burns, Sr. | Jan 2017 | A1 |
20170033375 | Ohmori et al. | Feb 2017 | A1 |
20170042467 | Herr et al. | Feb 2017 | A1 |
20170046488 | Pereira | Feb 2017 | A1 |
20170065851 | Deluca et al. | Mar 2017 | A1 |
20170080320 | Smith | Mar 2017 | A1 |
20170095670 | Ghaffari et al. | Apr 2017 | A1 |
20170095692 | Chang et al. | Apr 2017 | A1 |
20170095693 | Chang et al. | Apr 2017 | A1 |
20170100637 | Princen et al. | Apr 2017 | A1 |
20170106242 | Dugan | Apr 2017 | A1 |
20170113092 | Johnson | Apr 2017 | A1 |
20170128769 | Long et al. | May 2017 | A1 |
20170132947 | Maeda et al. | May 2017 | A1 |
20170136296 | Barrera et al. | May 2017 | A1 |
20170143261 | Wiedenhoefer et al. | May 2017 | A1 |
20170147752 | Toru | May 2017 | A1 |
20170147789 | Wiedenhoefer et al. | May 2017 | A1 |
20170148297 | Ross | May 2017 | A1 |
20170168555 | Munoz et al. | Jun 2017 | A1 |
20170181698 | Wiedenhoefer et al. | Jun 2017 | A1 |
20170190052 | Jaekel et al. | Jul 2017 | A1 |
20170202724 | De Rossi | Jul 2017 | A1 |
20170209766 | Riley et al. | Jul 2017 | A1 |
20170220751 | Davis | Aug 2017 | A1 |
20170235882 | Orlov et al. | Aug 2017 | A1 |
20170235906 | Dorris et al. | Aug 2017 | A1 |
20170243028 | LaFever et al. | Aug 2017 | A1 |
20170262604 | Francois | Sep 2017 | A1 |
20170265800 | Auchinleck et al. | Sep 2017 | A1 |
20170266501 | Sanders et al. | Sep 2017 | A1 |
20170270260 | Shetty | Sep 2017 | A1 |
20170278209 | Olsen et al. | Sep 2017 | A1 |
20170282015 | Wicks et al. | Oct 2017 | A1 |
20170283508 | Demopulos et al. | Oct 2017 | A1 |
20170286621 | Cox | Oct 2017 | A1 |
20170300654 | Stein et al. | Oct 2017 | A1 |
20170304024 | Nobrega | Oct 2017 | A1 |
20170312614 | Tran et al. | Nov 2017 | A1 |
20170323481 | Tran et al. | Nov 2017 | A1 |
20170329917 | McRaith et al. | Nov 2017 | A1 |
20170329933 | Brust | Nov 2017 | A1 |
20170333755 | Rider | Nov 2017 | A1 |
20170337033 | Duyan et al. | Nov 2017 | A1 |
20170337334 | Stanczak | Nov 2017 | A1 |
20170344726 | Duffy et al. | Nov 2017 | A1 |
20170347923 | Roh | Dec 2017 | A1 |
20170360586 | Dempers et al. | Dec 2017 | A1 |
20170368413 | Shavit | Dec 2017 | A1 |
20180017806 | Wang et al. | Jan 2018 | A1 |
20180036593 | Ridgel et al. | Feb 2018 | A1 |
20180052962 | Van Der Koijk et al. | Feb 2018 | A1 |
20180056104 | Cromie et al. | Mar 2018 | A1 |
20180060494 | Dias et al. | Mar 2018 | A1 |
20180071565 | Gomberg et al. | Mar 2018 | A1 |
20180071566 | Gomberg et al. | Mar 2018 | A1 |
20180071569 | Gomberg et al. | Mar 2018 | A1 |
20180071570 | Gomberg et al. | Mar 2018 | A1 |
20180071571 | Gomberg et al. | Mar 2018 | A1 |
20180071572 | Gomberg | Mar 2018 | A1 |
20180075205 | Moturu et al. | Mar 2018 | A1 |
20180078843 | Tran et al. | Mar 2018 | A1 |
20180096111 | Wells et al. | Apr 2018 | A1 |
20180102190 | Hogue et al. | Apr 2018 | A1 |
20180116741 | Garcia Kilroy et al. | May 2018 | A1 |
20180140927 | Kito | May 2018 | A1 |
20180146870 | Shemesh | May 2018 | A1 |
20180177612 | Trabish et al. | Jun 2018 | A1 |
20180178061 | O'larte et al. | Jun 2018 | A1 |
20180199855 | Odame et al. | Jul 2018 | A1 |
20180200577 | Dugan | Jul 2018 | A1 |
20180220935 | Tadano et al. | Aug 2018 | A1 |
20180228682 | Bayerlein et al. | Aug 2018 | A1 |
20180240552 | Tuyl et al. | Aug 2018 | A1 |
20180253991 | Tang et al. | Sep 2018 | A1 |
20180256079 | Yang et al. | Sep 2018 | A1 |
20180263530 | Jung | Sep 2018 | A1 |
20180263535 | Cramer | Sep 2018 | A1 |
20180263552 | Graman et al. | Sep 2018 | A1 |
20180264312 | Pompile et al. | Sep 2018 | A1 |
20180271432 | Auchinleck et al. | Sep 2018 | A1 |
20180272184 | Vassilaros et al. | Sep 2018 | A1 |
20180280784 | Romeo et al. | Oct 2018 | A1 |
20180296143 | Anderson et al. | Oct 2018 | A1 |
20180296157 | Bleich et al. | Oct 2018 | A1 |
20180326243 | Badi et al. | Nov 2018 | A1 |
20180330058 | Bates | Nov 2018 | A1 |
20180330810 | Gamarnik | Nov 2018 | A1 |
20180330824 | Athey et al. | Nov 2018 | A1 |
20180290017 | Fung | Dec 2018 | A1 |
20180353812 | Lannon et al. | Dec 2018 | A1 |
20180360340 | Rehse et al. | Dec 2018 | A1 |
20180373844 | Ferrandez-Escamez et al. | Dec 2018 | A1 |
20190009135 | Wu | Jan 2019 | A1 |
20190019163 | Batey et al. | Jan 2019 | A1 |
20190019573 | Lake et al. | Jan 2019 | A1 |
20190019578 | Vaccaro | Jan 2019 | A1 |
20190030415 | Volpe, Jr. | Jan 2019 | A1 |
20190031284 | Fuchs | Jan 2019 | A1 |
20190035043 | Jones et al. | Jan 2019 | A1 |
20190046794 | Goodall et al. | Feb 2019 | A1 |
20190060708 | Fung | Feb 2019 | A1 |
20190065970 | Bonutti et al. | Feb 2019 | A1 |
20190066832 | Kang et al. | Feb 2019 | A1 |
20190076701 | Dugan | Mar 2019 | A1 |
20190080802 | Ziobro et al. | Mar 2019 | A1 |
20190088356 | Oliver et al. | Mar 2019 | A1 |
20190090744 | Mahfouz | Mar 2019 | A1 |
20190091506 | Gatelli et al. | Mar 2019 | A1 |
20190111299 | Radcliffe et al. | Apr 2019 | A1 |
20190115097 | Macoviak et al. | Apr 2019 | A1 |
20190117128 | Chen et al. | Apr 2019 | A1 |
20190118038 | Tana et al. | Apr 2019 | A1 |
20190126099 | Hoang | May 2019 | A1 |
20190132948 | Longinotti-Buitoni et al. | May 2019 | A1 |
20190134454 | Mahoney et al. | May 2019 | A1 |
20190137988 | Cella et al. | May 2019 | A1 |
20190167988 | Shahriari | Jun 2019 | A1 |
20190172587 | Park et al. | Jun 2019 | A1 |
20190175988 | Volterrani et al. | Jun 2019 | A1 |
20190183715 | Kapure et al. | Jun 2019 | A1 |
20190200920 | Tien et al. | Jul 2019 | A1 |
20190209891 | Fung | Jul 2019 | A1 |
20190223797 | Tran | Jul 2019 | A1 |
20190228856 | Leifer | Jul 2019 | A1 |
20190240103 | Hepler et al. | Aug 2019 | A1 |
20190240541 | Denton et al. | Aug 2019 | A1 |
20190244540 | Errante et al. | Aug 2019 | A1 |
20190251456 | Constantin | Aug 2019 | A1 |
20190262084 | Roh | Aug 2019 | A1 |
20190269343 | Ramos Murguialday et al. | Sep 2019 | A1 |
20190274523 | Bates et al. | Sep 2019 | A1 |
20190275368 | Maroldi | Sep 2019 | A1 |
20190304584 | Savolainen | Oct 2019 | A1 |
20190307983 | Goldman | Oct 2019 | A1 |
20190314681 | Yang | Oct 2019 | A1 |
20190344123 | Rubin et al. | Nov 2019 | A1 |
20190354632 | Mital et al. | Nov 2019 | A1 |
20190362242 | Pillai et al. | Nov 2019 | A1 |
20190366146 | Tong et al. | Dec 2019 | A1 |
20190388728 | Wang et al. | Dec 2019 | A1 |
20200005928 | Daniel | Jan 2020 | A1 |
20200038703 | Cleary et al. | Feb 2020 | A1 |
20200051446 | Rubinstein et al. | Feb 2020 | A1 |
20200066390 | Svendrys et al. | Feb 2020 | A1 |
20200085300 | Kwatra et al. | Mar 2020 | A1 |
20200093418 | Kluger et al. | Mar 2020 | A1 |
20200143922 | Chekroud et al. | May 2020 | A1 |
20200151595 | Jayalath | May 2020 | A1 |
20200151646 | De La Fuente Sanchez | May 2020 | A1 |
20200152339 | Pulitzer et al. | May 2020 | A1 |
20200160198 | Reeves et al. | May 2020 | A1 |
20200285322 | Johri | May 2020 | A1 |
20200170876 | Kapure et al. | Jun 2020 | A1 |
20200176098 | Lucas et al. | Jun 2020 | A1 |
20200197744 | Schweighofer | Jun 2020 | A1 |
20200221975 | Basta et al. | Jul 2020 | A1 |
20200237291 | Raja | Jul 2020 | A1 |
20200267487 | Siva | Aug 2020 | A1 |
20200275886 | Mason | Sep 2020 | A1 |
20200289046 | Hacking et al. | Sep 2020 | A1 |
20200289878 | Arn et al. | Sep 2020 | A1 |
20200289879 | Hacking et al. | Sep 2020 | A1 |
20200289880 | Hacking et al. | Sep 2020 | A1 |
20200289881 | Hacking et al. | Sep 2020 | A1 |
20200289889 | Hacking et al. | Sep 2020 | A1 |
20200293712 | Potts et al. | Sep 2020 | A1 |
20200303063 | Sharma et al. | Sep 2020 | A1 |
20200334972 | Gopalakrishnan | Oct 2020 | A1 |
20200357299 | Patel et al. | Nov 2020 | A1 |
20200365256 | Hayashitani et al. | Nov 2020 | A1 |
20200395112 | Ronner | Dec 2020 | A1 |
20200401224 | Cotton | Dec 2020 | A1 |
20200410385 | Otsuki | Dec 2020 | A1 |
20200411162 | Lien et al. | Dec 2020 | A1 |
20210005224 | Rothschild et al. | Jan 2021 | A1 |
20210005319 | Otsuki et al. | Jan 2021 | A1 |
20210074178 | Ilan et al. | Mar 2021 | A1 |
20210076981 | Hacking et al. | Mar 2021 | A1 |
20210077860 | Posnack et al. | Mar 2021 | A1 |
20210098129 | Neumann | Apr 2021 | A1 |
20210101051 | Posnack et al. | Apr 2021 | A1 |
20210113890 | Posnack et al. | Apr 2021 | A1 |
20210127974 | Mason et al. | May 2021 | A1 |
20210128080 | Mason et al. | May 2021 | A1 |
20210128255 | Mason et al. | May 2021 | A1 |
20210128978 | Gilstrom et al. | May 2021 | A1 |
20210134412 | Guaneri et al. | May 2021 | A1 |
20210134425 | Mason et al. | May 2021 | A1 |
20210134428 | Mason et al. | May 2021 | A1 |
20210134430 | Mason et al. | May 2021 | A1 |
20210134432 | Mason et al. | May 2021 | A1 |
20210134456 | Posnack et al. | May 2021 | A1 |
20210134457 | Mason et al. | May 2021 | A1 |
20210134458 | Mason et al. | May 2021 | A1 |
20210134463 | Mason et al. | May 2021 | A1 |
20210138304 | Mason et al. | May 2021 | A1 |
20210142875 | Mason et al. | May 2021 | A1 |
20210142893 | Guaneri et al. | May 2021 | A1 |
20210142898 | Mason et al. | May 2021 | A1 |
20210142903 | Mason et al. | May 2021 | A1 |
20210144074 | Guaneri et al. | May 2021 | A1 |
20210186419 | Van Ee et al. | Jun 2021 | A1 |
20210202090 | ODonovan et al. | Jul 2021 | A1 |
20210202103 | Bostic et al. | Jul 2021 | A1 |
20210244998 | Hacking et al. | Aug 2021 | A1 |
20210245003 | Turner | Aug 2021 | A1 |
20210251562 | Jain | Aug 2021 | A1 |
20210272677 | Barbee | Sep 2021 | A1 |
20210338469 | Dempers | Nov 2021 | A1 |
20210343384 | Purushothaman et al. | Nov 2021 | A1 |
20210345879 | Mason et al. | Nov 2021 | A1 |
20210345975 | Mason et al. | Nov 2021 | A1 |
20210350888 | Guaneri et al. | Nov 2021 | A1 |
20210350898 | Mason et al. | Nov 2021 | A1 |
20210350899 | Mason et al. | Nov 2021 | A1 |
20210350901 | Mason et al. | Nov 2021 | A1 |
20210350902 | Mason et al. | Nov 2021 | A1 |
20210350914 | Guaneri et al. | Nov 2021 | A1 |
20210350926 | Mason et al. | Nov 2021 | A1 |
20210361514 | Choi et al. | Nov 2021 | A1 |
20210366587 | Mason et al. | Nov 2021 | A1 |
20210383909 | Mason et al. | Dec 2021 | A1 |
20210391091 | Mason | Dec 2021 | A1 |
20210398668 | Chock et al. | Dec 2021 | A1 |
20210407670 | Mason et al. | Dec 2021 | A1 |
20210407681 | Mason et al. | Dec 2021 | A1 |
20220000556 | Casey et al. | Jan 2022 | A1 |
20220015838 | Posnack et al. | Jan 2022 | A1 |
20220016480 | Bissonnette et al. | Jan 2022 | A1 |
20220016482 | Bissonnette | Jan 2022 | A1 |
20220016485 | Bissonnette et al. | Jan 2022 | A1 |
20220016486 | Bissonnette | Jan 2022 | A1 |
20220020469 | Tanner | Jan 2022 | A1 |
20220044806 | Sanders et al. | Feb 2022 | A1 |
20220047921 | Bissonnette et al. | Feb 2022 | A1 |
20220079690 | Mason et al. | Mar 2022 | A1 |
20220080256 | Arn et al. | Mar 2022 | A1 |
20220080265 | Watterson | Mar 2022 | A1 |
20220105384 | Hacking et al. | Apr 2022 | A1 |
20220105385 | Hacking et al. | Apr 2022 | A1 |
20220105390 | Yuasa | Apr 2022 | A1 |
20220115133 | Mason et al. | Apr 2022 | A1 |
20220118218 | Bense et al. | Apr 2022 | A1 |
20220126169 | Mason | Apr 2022 | A1 |
20220133576 | Choi et al. | May 2022 | A1 |
20220148725 | Mason et al. | May 2022 | A1 |
20220158916 | Mason et al. | May 2022 | A1 |
20220176039 | Lintereur et al. | Jun 2022 | A1 |
20220181004 | Zilca et al. | Jun 2022 | A1 |
20220193491 | Mason et al. | Jun 2022 | A1 |
20220230729 | Mason et al. | Jul 2022 | A1 |
20220238223 | Mason et al. | Jul 2022 | A1 |
20220262483 | Rosenberg et al. | Aug 2022 | A1 |
20220262504 | Bratty et al. | Aug 2022 | A1 |
20220266094 | Mason et al. | Aug 2022 | A1 |
20220270738 | Mason et al. | Aug 2022 | A1 |
20220273985 | Jeong et al. | Sep 2022 | A1 |
20220273986 | Mason | Sep 2022 | A1 |
20220288460 | Mason | Sep 2022 | A1 |
20220288461 | Ashley et al. | Sep 2022 | A1 |
20220288462 | Ashley et al. | Sep 2022 | A1 |
20220293257 | Guaneri et al. | Sep 2022 | A1 |
20220300787 | Wall et al. | Sep 2022 | A1 |
20220304881 | Choi et al. | Sep 2022 | A1 |
20220304882 | Choi | Sep 2022 | A1 |
20220305328 | Choi et al. | Sep 2022 | A1 |
20220314075 | Mason et al. | Oct 2022 | A1 |
20220323826 | Khurana | Oct 2022 | A1 |
20220327714 | Cook et al. | Oct 2022 | A1 |
20220327807 | Cook et al. | Oct 2022 | A1 |
20220328181 | Mason et al. | Oct 2022 | A1 |
20220330823 | Janssen | Oct 2022 | A1 |
20220331663 | Mason | Oct 2022 | A1 |
20220338761 | Maddahi et al. | Oct 2022 | A1 |
20220339052 | Kim | Oct 2022 | A1 |
20220339501 | Mason et al. | Oct 2022 | A1 |
20220384012 | Mason | Dec 2022 | A1 |
20220392591 | Guaneri et al. | Dec 2022 | A1 |
20220395232 | Locke | Dec 2022 | A1 |
20220401783 | Choi | Dec 2022 | A1 |
20220415469 | Mason | Dec 2022 | A1 |
20220415471 | Mason | Dec 2022 | A1 |
20230001268 | Bissonnette et al. | Jan 2023 | A1 |
20230013530 | Mason | Jan 2023 | A1 |
20230014598 | Mason et al. | Jan 2023 | A1 |
20230029639 | Roy | Feb 2023 | A1 |
20230048040 | Hacking et al. | Feb 2023 | A1 |
20230051751 | Hacking et al. | Feb 2023 | A1 |
20230058605 | Mason | Feb 2023 | A1 |
20230060039 | Mason | Feb 2023 | A1 |
20230072368 | Mason | Mar 2023 | A1 |
20230078793 | Mason | Mar 2023 | A1 |
20230119461 | Mason | Apr 2023 | A1 |
20230190100 | Stump | Jun 2023 | A1 |
20230201656 | Hacking et al. | Jun 2023 | A1 |
20230207097 | Mason | Jun 2023 | A1 |
20230207124 | Walsh et al. | Jun 2023 | A1 |
20230215539 | Rosenberg et al. | Jul 2023 | A1 |
20230215552 | Khotilovich et al. | Jul 2023 | A1 |
20230245747 | Rosenberg et al. | Aug 2023 | A1 |
20230245748 | Rosenberg et al. | Aug 2023 | A1 |
20230245750 | Rosenberg et al. | Aug 2023 | A1 |
20230245751 | Rosenberg et al. | Aug 2023 | A1 |
20230253089 | Rosenberg et al. | Aug 2023 | A1 |
20230255555 | Sundaram et al. | Aug 2023 | A1 |
20230263428 | Hull et al. | Aug 2023 | A1 |
20230274813 | Rosenberg et al. | Aug 2023 | A1 |
20230282329 | Mason et al. | Sep 2023 | A1 |
20230364472 | Posnack | Nov 2023 | A1 |
20230368886 | Rosenberg | Nov 2023 | A1 |
20230377711 | Rosenberg | Nov 2023 | A1 |
20230377712 | Rosenberg | Nov 2023 | A1 |
20230386639 | Rosenberg | Nov 2023 | A1 |
20230395231 | Rosenberg | Dec 2023 | A1 |
20230395232 | Rosenberg | Dec 2023 | A1 |
20240029856 | Rosenberg | Jan 2024 | A1 |
Number | Date | Country |
---|---|---|
2698078 | Mar 2010 | CA |
3193419 | Mar 2022 | CA |
112603295 | Feb 2003 | CN |
2885238 | Apr 2007 | CN |
101964151 | Feb 2011 | CN |
201889024 | Jul 2011 | CN |
102670381 | Sep 2012 | CN |
103263336 | Aug 2013 | CN |
103390357 | Nov 2013 | CN |
103473631 | Dec 2013 | CN |
103488880 | Jan 2014 | CN |
103501328 | Jan 2014 | CN |
103721343 | Apr 2014 | CN |
203677851 | Jul 2014 | CN |
104335211 | Feb 2015 | CN |
105683977 | Jun 2016 | CN |
103136447 | Aug 2016 | CN |
105894088 | Aug 2016 | CN |
105930668 | Sep 2016 | CN |
205626871 | Oct 2016 | CN |
106127646 | Nov 2016 | CN |
106236502 | Dec 2016 | CN |
106510985 | Mar 2017 | CN |
106621195 | May 2017 | CN |
107066819 | Aug 2017 | CN |
107430641 | Dec 2017 | CN |
107551475 | Jan 2018 | CN |
107736982 | Feb 2018 | CN |
107930021 | Apr 2018 | CN |
207220817 | Apr 2018 | CN |
108078737 | May 2018 | CN |
208224811 | Dec 2018 | CN |
109191954 | Jan 2019 | CN |
109363887 | Feb 2019 | CN |
208573971 | Mar 2019 | CN |
110148472 | Aug 2019 | CN |
110201358 | Sep 2019 | CN |
110215188 | Sep 2019 | CN |
110322957 | Oct 2019 | CN |
110808092 | Feb 2020 | CN |
110931103 | Mar 2020 | CN |
110993057 | Apr 2020 | CN |
111084618 | May 2020 | CN |
111105859 | May 2020 | CN |
111111110 | May 2020 | CN |
111370088 | Jul 2020 | CN |
111460305 | Jul 2020 | CN |
112071393 | Dec 2020 | CN |
212141371 | Dec 2020 | CN |
112289425 | Jan 2021 | CN |
212624809 | Feb 2021 | CN |
112603295 | Apr 2021 | CN |
213190965 | May 2021 | CN |
113384850 | Sep 2021 | CN |
113499572 | Oct 2021 | CN |
214388673 | Oct 2021 | CN |
215136488 | Dec 2021 | CN |
113885361 | Jan 2022 | CN |
114049961 | Feb 2022 | CN |
114203274 | Mar 2022 | CN |
216258145 | Apr 2022 | CN |
114694824 | Jul 2022 | CN |
114898832 | Aug 2022 | CN |
114983760 | Sep 2022 | CN |
217472652 | Sep 2022 | CN |
110270062 | Oct 2022 | CN |
218420859 | Feb 2023 | CN |
115954081 | Apr 2023 | CN |
102018202497 | Aug 2018 | DE |
102018211212 | Jan 2019 | DE |
102019108425 | Aug 2020 | DE |
0383137 | Aug 1990 | EP |
1159989 | Dec 2001 | EP |
1968028 | Sep 2008 | EP |
1909730 | Apr 2014 | EP |
2815242 | Dec 2014 | EP |
2869805 | May 2015 | EP |
2997951 | Mar 2016 | EP |
2688472 | Apr 2016 | EP |
3264303 | Jan 2018 | EP |
3323473 | May 2018 | EP |
3627514 | Mar 2020 | EP |
3671700 | Jun 2020 | EP |
3688537 | Aug 2020 | EP |
3731733 | Nov 2020 | EP |
3984508 | Apr 2022 | EP |
3984509 | Apr 2022 | EP |
3984510 | Apr 2022 | EP |
3984511 | Apr 2022 | EP |
3984512 | Apr 2022 | EP |
3984513 | Apr 2022 | EP |
4054699 | Sep 2022 | EP |
4112033 | Jan 2023 | EP |
3127393 | Mar 2023 | FR |
2512431 | Oct 2014 | GB |
2591542 | Mar 2022 | GB |
201811043670 | Jul 2018 | IN |
2000005339 | Jan 2000 | JP |
2003225875 | Aug 2003 | JP |
2005227928 | Aug 2005 | JP |
2005227928 | Aug 2005 | JP |
2009112336 | May 2009 | JP |
2013515995 | May 2013 | JP |
3193662 | Oct 2014 | JP |
3198173 | Jun 2015 | JP |
5804063 | Nov 2015 | JP |
2018102842 | Jul 2018 | JP |
2019028647 | Feb 2019 | JP |
2019134909 | Aug 2019 | JP |
6573739 | Sep 2019 | JP |
6659831 | Mar 2020 | JP |
6710357 | Jun 2020 | JP |
6775757 | Oct 2020 | JP |
2021027917 | Feb 2021 | JP |
6871379 | May 2021 | JP |
2022521378 | Apr 2022 | JP |
3238491 | Jul 2022 | JP |
7198364 | Dec 2022 | JP |
7202474 | Jan 2023 | JP |
7231750 | Mar 2023 | JP |
7231751 | Mar 2023 | JP |
7231752 | Mar 2023 | JP |
20020009724 | Feb 2002 | KR |
200276919 | May 2002 | KR |
20020065253 | Aug 2002 | KR |
100582596 | May 2006 | KR |
101042258 | Jun 2011 | KR |
20110099953 | Sep 2011 | KR |
101258250 | Apr 2013 | KR |
101325581 | Nov 2013 | KR |
20140128630 | Nov 2014 | KR |
20150017693 | Feb 2015 | KR |
20150078191 | Jul 2015 | KR |
101580071 | Dec 2015 | KR |
101647620 | Aug 2016 | KR |
20160093990 | Aug 2016 | KR |
20170038837 | Apr 2017 | KR |
20180004928 | Jan 2018 | KR |
20190029175 | Mar 2019 | KR |
101988167 | Jun 2019 | KR |
101969392 | Aug 2019 | KR |
102055279 | Dec 2019 | KR |
102088333 | Mar 2020 | KR |
20200025290 | Mar 2020 | KR |
20200029180 | Mar 2020 | KR |
102116664 | May 2020 | KR |
102116968 | May 2020 | KR |
20200056233 | May 2020 | KR |
102120828 | Jun 2020 | KR |
102121586 | Jun 2020 | KR |
102142713 | Aug 2020 | KR |
102162522 | Oct 2020 | KR |
20200119665 | Oct 2020 | KR |
102173553 | Nov 2020 | KR |
102180079 | Nov 2020 | KR |
102188766 | Dec 2020 | KR |
102196793 | Dec 2020 | KR |
20210006212 | Jan 2021 | KR |
102224188 | Mar 2021 | KR |
102224618 | Mar 2021 | KR |
102246049 | Apr 2021 | KR |
102246050 | Apr 2021 | KR |
102246051 | Apr 2021 | KR |
102246052 | Apr 2021 | KR |
102264498 | Jun 2021 | KR |
102352602 | Jan 2022 | KR |
102352603 | Jan 2022 | KR |
102352604 | Jan 2022 | KR |
20220004639 | Jan 2022 | KR |
102387577 | Apr 2022 | KR |
102421437 | Jul 2022 | KR |
20220102207 | Jul 2022 | KR |
102427545 | Aug 2022 | KR |
102467495 | Nov 2022 | KR |
102467496 | Nov 2022 | KR |
102469723 | Nov 2022 | KR |
102471990 | Nov 2022 | KR |
20220145989 | Nov 2022 | KR |
20220156134 | Nov 2022 | KR |
102502744 | Feb 2023 | KR |
20230019349 | Feb 2023 | KR |
20230019350 | Feb 2023 | KR |
20230026556 | Feb 2023 | KR |
20230026668 | Feb 2023 | KR |
20230040526 | Mar 2023 | KR |
20230050506 | Apr 2023 | KR |
20230056118 | Apr 2023 | KR |
102528503 | May 2023 | KR |
102531930 | May 2023 | KR |
102532766 | May 2023 | KR |
102539190 | Jun 2023 | KR |
2014131288 | Feb 2016 | RU |
2607953 | Jan 2017 | RU |
M474545 | Mar 2014 | TW |
M638437 | Mar 2023 | TW |
0149235 | Jul 2001 | WO |
0151083 | Jul 2001 | WO |
2001050387 | Jul 2001 | WO |
2001056465 | Aug 2001 | WO |
02093312 | Nov 2002 | WO |
2020200891 | Feb 2003 | WO |
2003043494 | May 2003 | WO |
2005018453 | Mar 2005 | WO |
2006004430 | Jan 2006 | WO |
2007102709 | Sep 2007 | WO |
2008114291 | Sep 2008 | WO |
2011025322 | Mar 2011 | WO |
2012128801 | Sep 2012 | WO |
2013002568 | Jan 2013 | WO |
2023164292 | Mar 2013 | WO |
2013122839 | Aug 2013 | WO |
2014011447 | Jan 2014 | WO |
2014039567 | Mar 2014 | WO |
2014163976 | Oct 2014 | WO |
2015026744 | Feb 2015 | WO |
2015065298 | May 2015 | WO |
2015082555 | Jun 2015 | WO |
2016154318 | Sep 2016 | WO |
2018132999 | Jan 2017 | WO |
2017030781 | Feb 2017 | WO |
2017091691 | Jun 2017 | WO |
2017165238 | Sep 2017 | WO |
2018081795 | May 2018 | WO |
2018171853 | Sep 2018 | WO |
2019022706 | Jan 2019 | WO |
2019204876 | Apr 2019 | WO |
2019143940 | Jul 2019 | WO |
2020185769 | Mar 2020 | WO |
2020075190 | Apr 2020 | WO |
2020130979 | Jun 2020 | WO |
2020149815 | Jul 2020 | WO |
2020245727 | Dec 2020 | WO |
2020249855 | Dec 2020 | WO |
2020252599 | Dec 2020 | WO |
2020256577 | Dec 2020 | WO |
2021021447 | Feb 2021 | WO |
2021038980 | Mar 2021 | WO |
2021055427 | Mar 2021 | WO |
2021055491 | Mar 2021 | WO |
2021061061 | Apr 2021 | WO |
2021081094 | Apr 2021 | WO |
2021090267 | May 2021 | WO |
2021138620 | Jul 2021 | WO |
2021216881 | Oct 2021 | WO |
2021236542 | Nov 2021 | WO |
2021236961 | Nov 2021 | WO |
2021262809 | Dec 2021 | WO |
2022047006 | Mar 2022 | WO |
2022092493 | May 2022 | WO |
2022092494 | May 2022 | WO |
2022212883 | Oct 2022 | WO |
2022212921 | Oct 2022 | WO |
2022216498 | Oct 2022 | WO |
2022251420 | Dec 2022 | WO |
2023008680 | Feb 2023 | WO |
2023008681 | Feb 2023 | WO |
2023022319 | Feb 2023 | WO |
2023022320 | Feb 2023 | WO |
2023052695 | Apr 2023 | WO |
2023091496 | May 2023 | WO |
2023215155 | Nov 2023 | WO |
2023230075 | Nov 2023 | WO |
Entry |
---|
Website for “Pedal Exerciser”, p. 1, retrieved on Sep. 9, 2022 from https://www.vivehealth.com/collections/physical-therapy-equipment/products/pedalexerciser. |
Website for “Functional Knee Brace with ROM”, p. 1, retrieved on Sep. 9, 2022 from http://medicalbrace.gr/en/product/functional-knee-brace-with-goniometer-mbtelescopicknee/. |
Website for “ComfySplints Goniometer Knee”, pp. 1-5, retrieved on Sep. 9, 2022 from https://www.comfysplints.com/product/knee-splints/. |
Website for “BMI FlexEze Knee Corrective Orthosis (KCO)”, pp. 1-4, retrieved on Sep. 9, 2022 from https://orthobmi.com/products/bmi-flexeze%C2%AE-knee-corrective-orthosis-kco. |
Website for “Neoprene Knee Brace with goniometer—Patella ROM MB.4070”, pp. 1-4, retrieved on Sep. 9, 2022 from https://www.fortuna.com.gr/en/product/neoprene-knee-brace-with-goniometer-patella-rom-mb-4070/. |
Kuiken et al., “Computerized Biofeedback Knee Goniometer: Acceptance and Effect on Exercise Behavior in Post-total Knee Arthroplasty Rehabilitation,” Biomedical Engineering Faculty Research and Publications, 2004, pp. 1-10. |
Ahmed et al., “Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine,” Database, 2020, pp. 1-35. |
Davenport et al., “The potential for artificial intelligence in healthcare,” Digital Technology, Future Healthcare Journal, 2019, pp. 1-5, vol. 6, No. 2. |
Website for “OxeFit XS1”, pp. 1-3, retrieved on Sep. 9, 2022 from https://www.oxefit.com/xs1. |
Website for “Preva Mobile”, pp. 1-6, retrieved on Sep. 9, 2022 from https://www.precor.com/en-us/resources/introducing-preva-mobile. |
Website for “J-Bike”, pp. 1-3, retrieved on Sep. 9, 2022 from https://www.magneticdays.com/en/cycling-for-physical-rehabilitation. |
Website for “Excy”, pp. 1-12, retrieved on Sep. 9, 2022 from https://excy.com/portable-exercise-rehabilitation-excy-xcs-pro/. |
Website for “OxeFit XP1”, p. 1, retrieved on Sep. 9, 2022 from https://www.oxefit.com/xp1. |
Booklet of Products, Clairs Reflex, Claris Healthcare, Vancouver, BC. |
Barrett et al., “Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care,” EPMA Journal (2019), pp. 445-464. |
Oerkild et al., “Home-based cardiac rehabilitation is an attractive alternative to no cardiac rehabilitation for elderly patients with coronary heart disease: results from a randomised clinical trial,” BMJ Open Accessible Medical Research, Nov. 22, 2012, pp. 1-9. |
Bravo-Escobar et al., “Effectiveness and safety of a home-based cardiac rehabilitation programme of mixed surveillance in patients with ischemic heart disease at moderate cardiovascular risk: A randomised, controlled clinical trial,” BMC Cardiovascular Disorders, 2017, pp. 1-11, vol. 17:66. |
Thomas et al., “Home-Based Cardiac Rehabilitation,” Circulation, 2019, pp. e69-e89, vol. 140. |
Thomas et al., “Home-Based Cardiac Rehabilitation,” Journal of the American College of Cardiology, Nov. 1, 2019, pp. 133-153, vol. 74. |
Thomas et al., “Home-Based Cardiac Rehabilitation,” HHS Public Access, Oct. 2, 2020, pp. 1-39. |
Dittus et al., “Exercise-Based Oncology Rehabilitation: Leveraging the Cardiac Rehabilitation Model,” Journal of Cardiopulmonary Rehabilitation and Prevention, 2015, pp. 130-139, vol. 35. |
Chen et al., “Home-based cardiac rehabilitation improves quality of life, aerobic capacity, and readmission rates in patients with chronic heart failure,” Medicine, 2018, pp. 1-5 vol. 97:4. |
Lima de Melo Ghisi et al., “A systematic review of patient education in cardiac patients: Do they increase knowledge and promote health behavior change?,” Patient Education and Counseling, 2014, pp. 1-15. |
Fang et al., “Use of Outpatient Cardiac Rehabilitation Among Heart Attack Survivors—20 States and the District of Columbia, 2013 and Four States, 2015,” Morbidity and Mortality Weekly Report, vol. 66, No. 33, Aug. 25, 2017, pp. 869-873. |
Beene et al., “AI and Care Delivery: Emerging Opportunities for Artificial Intelligence to Transform How Care Is Delivered,” Nov. 2019, American Hospital Association, pp. 1-12. |
Davenport et al., “The Potential for Artificial Intelligence in Healthcare”, 2019, Future Healthcare Journal 2019, vol. 6, No. 2: Year: 2019, pp. 1-5. |
Ahmed et al., “Artificial Intelligence With Multi-Functional Machine Learning Platform Development for Better Healthcare and Precision Medicine”, 2020, Database (Oxford), 2020:baaa010. doi: 10.1093/database/baaa010 (Year: 2020), pp. 1-35. |
Ruiz Ivan et al., “Towards a physical rehabilitation system using a telemedicine approach”, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 8, No. 6, Jul. 28, 2020, pp. 671-680, XP055914810. |
De Canniere Helene et al., “Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation”, Sensors, vol. 20, No. 12, Jun. 26, 2020, XP055914617, pp. 1-15. |
Boulanger Pierre et al., “A Low-cost Virtual Reality Bike for Remote Cardiac Rehabilitation”, Dec. 7, 2017, Advances in Biometrics: International Conference, ICB 2007, Seoul, Korea, pp. 155-166. |
Yin Chieh et al., “A Virtual Reality-Cycling Training System for Lower Limb Balance Improvement”, BioMed Research International, vol. 2016, pp. 1-10. |
International Searching Authority, Search Report and Written Opinion for International Application No. PCT/US2021/038617, dated Oct. 15, 2021, 12 pages. |
Jennifer Bresnick, “What is the Role of Natural Language Processing in Healthcare?”, pp. 1-7, published Aug. 18, 2016, retrieved on Feb. 1, 2022 from https://healthitanalytics.com/ featu res/what-is-the-role-of-natural-language-processing-in-healthcare. |
Alex Bellec, “Part-of-Speech tagging tutorial with the Keras Deep Learning library,” pp. 1-16, published Mar. 27, 2018, retrieved on Feb. 1, 2022 from https://becominghuman.ai/part-of-speech-tagging-tutorial-with-the-keras-deep-learning-library-d7f93fa05537. |
Kavita Ganesan, All you need to know about text preprocessing for NLP and Machine Learning, pp. 1-14, published Feb. 23, 2019, retrieved on Feb. 1, 2022 from https:// towardsdatascience.com/all-you-need-to-know-about-text-preprocessing-for-nlp-and-machine-learning-bcl c5765ff67. |
Badreesh Shetty, “Natural Language Processing (NPL) for Machine Learning,” pp. 1-13, published Nov. 24, 2018, retrieved on Feb. 1, 2022 from https://towardsdatascience. com/natural-language-processing-nlp-for-machine-learning-d44498845d5b. |
Claris Healthcare Inc.; Claris Reflex@ Patient Rehabilitation System Brochure, https://clarisreflex.com/, accessed on Oct. 2, 2019. |
International Searching Authority, Search Report and Written Opinion for International Application No. PCT/US2021/032807, dated Sep. 6, 2021, 11 pages. |
Jeong et al., “Computer-assisted upper extremity training using interactive biking exercise (iBikE) platform,” Sep. 2012, pp. 1-5, 34th Annual International Conference of the IEEE EMBS. |
Malloy, Online Article “AI-enabled EKGs find difference between numerical age and biological age significantly affects health, longevity”, Website: https://newsnetwork.mayoclinic.org/discussion/ai-enabled-ekgs-find-difference-between-numerical-age-and-biological-age-significantly-affects-health-longevity/, Mayo Clinic News Network, May 20, 2021, retrieved: Jan. 23, 2023, p. 1-4. |
International Search Report and Written Opinion for PCT/US2023/014137, dated Jun. 9, 2023, 13 pages. |
Website for “Esino 2022 Physical Therapy Equipments Arm Fitness Indoor Trainer Leg Spin Cycle Machine Exercise Bike for Elderly,” https://www.made-in-china.com/showroom/esinogroup/product-detailYdZtwGhCMKVR/China-Esino-2022-Physical-Therapy-Equipments-Arm-Fitness-Indoor-Trainer-Leg-Spin-Cycle-Machine-Exercise-Bike-for-Elderly.html, retrieved on Aug. 29, 2023, 5 pages. |
Abedtash, “An Interoperable Electronic Medical Record-Based Platform for Personalized Predictive Analytics”, ProQuest LLC, Jul. 2017, 185 pages. |
Alcaraz et al., “Machine Learning as Digital Therapy Assessment for Mobile Gait Rehabilitation,” 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, 2018, 6 pages. |
Androutsou et al., “A Smartphone Application Designed to Engage the Elderly in Home-Based Rehabilitation,” Frontiers in Digital Health, Sep. 2020, vol. 2, Article 15, 13 pages. |
Silva et al., “SapoFitness: A mobile health application for dietary evaluation,” 2011 IEEE 13th International Conference on U e-Health Networking, Applications and Services, Columbia, MO, USA, 2011, 6 pages. |
Wang et al., “Interactive wearable systems for upper body rehabilitation: a systematic review,” Journal of NeuroEngineering and Rehabilitation, 2017, 21 pages. |
Marzolini et al., “Eligibility, Enrollment, and Completion of Exercise-Based Cardiac Rehabilitation Following Stroke Rehabilitation: What Are the Barriers?,” Physical Therapy, vol. 100, No. 1, 2019, 13 pages. |
Nijjar et al., “Randomized Trial of Mindfulness-Based Stress Reduction in Cardiac Patients Eligible for Cardiac Rehabilitation,” Scientific Reports, 2019, 12 pages. |
Lara et al., “Human-Robot Sensor Interface for Cardiac Rehabilitation,” IEEE International Conference on Rehabilitation Robotics, Jul. 2017, 8 pages. |
Ishraque et al., “Artificial Intelligence-Based Rehabilitation Therapy Exercise Recommendation System,” 2018 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 2018, 5 pages. |
Zakari et al., “Are There Limitations to Exercise Benefits in Peripheral Arterial Disease?,” Frontiers in Cardiovascular Medicine, Nov. 2018, vol. 5, Article 173, 12 pages. |
You et al., “Including Blood Vasculature into a Game-Theoretic Model of Cancer Dynamics,” Games 2019, 10, 13, 22 pages. |
Jeong et al., “Computer-assisted upper extremity training using interactive biking exercise (iBikE) platform,” Sep. 2012, 34th Annual International Conference of the IEEE EMBS, 5 pages. |
Gerbild et al., “Physical Activity to Improve Erectile Dysfunction: A Systematic Review of Intervention Studies,” Sexual Medicine, 2018, 15 pages. |
Chrif et al., “Control design for a lower-limb paediatric therapy device using linear motor technology,” Article, 2017, pp. 119-127, Science Direct, Switzerland. |
Robben et al., “Delta Features From Ambient Sensor Data are Good Predictors of Change in Functional Health,” Article, 2016, pp. 2168-2194, vol. 21, No. 4, IEEE Journal of Biomedical and Health Informatics. |
Kantoch et al., “Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk,” Article, 2018, 17 pages, Sensors, Poland. |
Warburton et al., “International Launch of the Par-⋅Q+ and ePARmed-⋅X+ Validation of the PAR-⋅Q+ and ePARmed⋅⋅X+,” Health & Fitness Journal of Canada, 2011, 9 pages, vol. 4, No. 2. |
Number | Date | Country | |
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20200289045 A1 | Sep 2020 | US |
Number | Date | Country | |
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62816503 | Mar 2019 | US |