INSTRUMENTED ARTIFICIAL INTELLIGENCE (AI) DRIVEN MOTION TRACKING AND ALIGNMENT SYSTEMS AND METHODS FOR VARIOUS APPLICATIONS

Abstract

AI driven motion tracking and alignment systems configured to generate time-normalized three dimensional (e.g. frontal, sagittal, transverse) data associated with pronation and/or supination of lower extremities of a user are described. A semi-rigid foot orthotic can have sensors embedded in flexible regions. Data from the sensors is used to measure how the orthotic bends to determine forces from a lower extremity of the user acting on the orthotic. Data on the flexing, bending, and/or rotating of portions of the orthotic (including velocities, accelerations) may be compared to analyze in shoe pronation and/or supination. Other detectors can measure the temperature, body weight, heart rate, and timing of motion. An adjustment device can be used to adjust the orthotic. Data may also facilitate generation of animation that accurately depicts the positions and/or movements of a user's body; the use of augmented and virtual reality for avatar based applications; and presentation of information.

Description

FIELD

AI driven motion tracking and alignment devices, data collection, measurement of human motion, tracking limb movement, comparisons to other time normalized motion cycles, analysis of dynamic device performance, and other user motion analysis over time, along with facilitation of animation based on such information, and clinical analysis of orthopedics, sports medicine, physical therapy, and industrial evaluations is described.

BACKGROUND

Current foot orthotics have limited applicability for motion analysis. Orthotics designed to restrict and influence motion as well as improve dynamic alignment of the joints in the lower extremities of a user are being developed. These orthotics are often static (i.e., unable to be adjusted) and only useable for the specific purpose of treating orthopedic issues related to the foot of the user. Tracking patients in real time, and providing such data to clinicians would provide for better patient management with fewer complications.

SUMMARY

The system of health care in the United states is undergoing significant changes in recent years. These changes are expected to accelerate. In the past, individuals and small groups of physicians dominated the practice of medicine. Hospitals and surgery centers were independent forces delivering healthcare. However, now the healthcare system is experiencing dramatic changes. It is beginning to experience scaling as all other major industries have experienced in the past. The changes are seen industry wide, from small providers to large regional and national providers. These large providers are driven by two primary forces-cutting costs and improving patient outcomes.

The body is a very dynamic system, in constant motion. Humans typically put their bodies through tremendous movement related mechanical stress during their lifetimes. (In the past, mechanical capabilities (e.g., running, jumping, throwing, etc.) meant a greater chance for survival.)

Orthopedic medicine, sports medicine, and/or rehabilitation providers, have been slow to accumulate useful mechanical data because most of the orthopedic, sports, and/or rehabilitation data collected from patients is static data, and generated primarily by observation of movement (i.e., even if various sensors are used, the sensor usually track visually observable movement). Rich compelling data that can improve outcomes, such as kinematic data, data that can be used for classification models of body types, data that can be used for comparison of left and right limbs, data that shows loading characteristics during movement, data that can be used to determine ratios of different movement variables on different planes, etc., has not been collected. Until now, data collection has been focused on measuring the body when the body is not moving. Or at best, data collection has been based on watching a person move.

You would never buy a car based on how the car sits motionless in a parking lot, or even based on the look of the car as it drives by, because you want to experience the car when it is accelerating and turning. With the human body, for orthopedic medicine, sports medicine, rehabilitation, etc., there is no dynamic system capable of providing this data.

Billions of dollars are spent on undiagnosed mechanically induced problems of the foot, ankle, knee, hip, neck, and back. By using outmoded static data acquisition methods alone, most physicians have a very limited understanding of how the bones, muscles, and joints work when the body is moving. Large sums of money have been spent needlessly on less efficient services because the medical system is not capable of classifying patients into categories where data could be used to improve outcomes while reducing cost. These limitations are very costly and limit the medical system's ability to monitor true (especially) orthopedic, sports, and/or rehabilitation outcomes.

This is because a large percentage of orthopedic, sports, and/or rehabilitation problems and injuries are mechanically induced or aggravated by mechanical forces. We call these mechanically induced problems (MIP). These MIP are divided two categories-mechanically induced pain, and mechanically induced poor performance. MIP are best compared to driving in the car with a flat tire. Grabbing onto the steering wheel will do little to control the forces moving up the steering column. Indeed, the forces must be controlled by some other mechanical device, to manage the problem correctly.

In a similar manner, physicians do not have the tools to measure and control mechanical forces in humans. However, if useful data were generated with a system like that described below, a physician could manage mechanical and/or other problems in patients in earlier phases, which usually proves less expensive.

For example, in a commonly used gait assessment, as a representative example of a cyclic motion performed by a user—there is often a visual impression of the patient's gait patterns. Any captured gait data is typically isolated to single data points focusing on individual joint angles or pressures recorded under the foot at one part of the gait cycle. When assessing the data, one must ask the obvious question of how relevant is this data at helping the medical community improve outcomes and reduce costs? At this point the answer would be very limited. In order to cut costs and improve orthopedic, sports injury, rehabilitation, and/or other outcomes, rich data sets must be created that can be responsive to artificial intelligence (AI) and deep learning. By depending on just the standard assessment and individual data points alone, it is unlikely that this could be achieved.

In contrast (to measuring the body when the body is not moving, or data collection based on watching a person move), by accumulating and measuring patterns of movement using the systems and methods described herein, a robust data model may be created. To measure is to know.

AI driven motion tracking systems and methods are described herein. These systems and methods generate time normalized three dimensional (e.g., frontal, sagittal, and transverse) pronation, supination, and/or other data, measured over one or more cycles. The data may be used for measurement of human motion, tracking limb movement, comparisons to other time normalized motion cycles, analysis of dynamic device performance, and other user motion analysis over time, along with facilitation of animation based on such information, and clinical analysis of orthopedics, sports medicine, physical therapy, and industrial evaluations, among other things. For example, the present systems and methods may be used to determine net forces (i.e., more than just pressure) on a semi-rigid orthotic in a shoe (even though the semi-rigid orthotic is not visible outside the shoe) caused by a lower extremity (e.g., a foot, ankle, leg) of a user. The present systems and methods facilitate measurement of how the semi-rigid orthotic bends (e.g., how much, how fast (velocity and acceleration), etc.) in reaction to those forces. Until now, actually measuring in-shoe pronation, supination, and/or other similar data has not been possible. In some embodiments, sophisticated AI and/or mathematical tools may be used for analysis, to create metrics to improve medical and/or orthopedic outcomes. For example, Fourier, kinetic, kinematic, and/or other analyses may be used. This may facilitate separate of high and low frequency data, as one example, and/or other analyses.

Note that semi-rigid orthotics are used in various examples throughout this specification. One of ordinary skill in the art understands that other devices, operating under the principles described herein, may be used for similar purposes. A semi-rigid orthotic is a useful example because it can be placed in a user's shoe during various activities (e.g., standing, walking, running, skiing, cycling, golfing, jumping, kicking, lifting, hitting or moving an object, playing tennis, completing industrial tasks, etc.) for the multi-dimensional time normalized cyclic data measurement described herein. Also, a semi-rigid orthotic rotates, flexes, and/or bends under load in three dimensions. A semi-rigid orthotic is configured to temporarily bend under load to control, restrict, or reduce motion of a foot of a user during movement in a designated manner. A semi-rigid orthotic is constructed from semi-rigid materials that flex, bend, or rotate under load, and then are able to substantially return to their original shape. In contrast, an insole (or flexible orthotic) may be a piece of material inside a shoe on which a foot rests and is typically designed for accommodation of a pressure point or an anatomical variant. An insole is typically flexible and may merely incidentally or minimally control or restrict the motion of a foot. In addition, a rigid orthotic is not designed to bend or flex at all, and instead its purpose is to support a user's foot in a predetermined manner that does not change.

The present systems and methods may be used for evaluating data from full or partial movement cycles (e.g., a gait cycle; cyclic movement of a foot, ankle, leg, etc., during standing, walking, running, skiing, cycling, golfing, jumping, kicking, lifting, hitting or moving an object, playing tennis, completing industrial tasks, etc.) including several such cycles over time, classification of individual cycle (e.g., foot and/or gait) types, and measuring the change in force loads in different positions on a measuring device such as an orthotic, for example. This may be much the same as throwing different sized rocks at different speeds in a lake, and by measuring the ripples and determining the splash patterns, a data model capable of assessing both the speed and the size of the rocks thrown may be generated. The present systems and methods may be used for evaluating any cyclic event or task that creates a need for expertise. The present systems and methods may be used for any test of human motion that results in a rating, score, or classification.

The present systems and methods comprise motion capture sensors whose data is used to create unique metrics useable for analyzing how each individual creates a unique cycle during an event or task (e.g., standing, walking, running, skiing, cycling, golfing, jumping, kicking, lifting, hitting or moving an object, playing tennis, completing industrial tasks, etc.) The present systems and methods provide higher precision and accuracy tools for personalizing information to individual treatment plans and diagnosis. The data generated by the present systems and methods is a powerful tool for improving athletic performance as well as injury prevention.

Data from the present systems and methods facilitate generation of animation (e.g., computer generated imagery (CGI) animation) that accurately depicts the positions and/or movements of a user's foot, ankle, knee, leg, hip, etc.; the use of both augmented and virtual reality to allow for avatar based applications and training; tracking patients, athletes, etc., in real time (near real time, or at a later time), and providing such data to clinicians, coaches, etc., to provide for better patient, athlete, etc., management with fewer complications; as well as presentation of information in a much improved manner. Data from the present systems and methods may also be used for prolonged tracking of post op patients, as well as tracking for possible early assessment of mental health and neurological illnesses and/or other medical condition affected by and/or diagnosable by gait, additionally creating a data model for year by year tracking of children's movement over time, evaluating industrial movements and/or tasks, and/or for other uses where tracking motion is advantageous.

In some embodiments, the data captured by using the present systems and methods can be used to create a standard in orthopedic medicine, sports medicine, and/or rehabilitation, such as the FICO score, has been used in the credit markets—in essence a FICO score for the human body. This includes an artificial intelligence driven classification model of cyclic motion types to better personalize the information.

Great demand is expended for a proprietary database with the information generated by these systems and methods at least because large insurance companies and health providers are looking for data capable of being used to improve the quality of outcomes while reducing cost. This type of data currently is unavailable in the orthopedic, sports medicine, and/or rehabilitation market.

As described herein, the present systems and methods are configured for measuring various motions, tasks, or events to evaluate the function of a user. The presents systems and methods are configured to generate data for AI applications in a rich data model. With these and other techniques, data can be measured and/or otherwise quantified over time (even over a period of years). AI based systems (e.g., generative models, large language models, neural networks, and/or other machine learning techniques) may be configured to use this data to make predictions for users (e.g., predict who might tear an ACL and/or be prone to another injury, predict which children might be more or less athletically inclined, predict an optimal piece of equipment and/or a setting for such equipment, predict stability in senior citizens, predict a type of sport or work that is best for a user, etc.), classify a user in a certain way (e.g., higher risk for injury, lower risk for injury, etc.), and/or generate other outputs.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a schematic representation of a system for AI driven motion tracking and alignment, data collection, measurement of human motion, tracking limb movement, comparisons to other time normalized motion cycles, analysis of dynamic device performance, and other user motion analysis over time, along with facilitation of animation based on such information, and clinical analysis of orthopedics, sports medicine, physical therapy, and industrial evaluations.

FIG. 2 shows an instrumented semi-rigid orthotic with embedded sensors and computing components.

FIG. 3 Illustrates points in the stance phase of a gait cycle.

FIG. 4 shows the underside of the instrumented semi-rigid orthotic of FIG. 2 in a schematic representation of the locations of sensors, a processing unit, and an electronic interconnect between electronic components, among other features.

FIG. 5a shows an example of bending, rotating, and/or flexing of the instrumented semi-rigid orthotic of FIG. 2.

FIG. 5b shows another example of bending, rotating, and/or flexing of the instrumented semi-rigid orthotic of FIG. 2.

FIG. 5c shows a third example of bending, rotating, and/or flexing of the instrumented semi-rigid orthotic of FIG. 2.

FIG. 5d shows a fourth example of bending, rotating, and/or flexing of the instrumented semi-rigid orthotic of FIG. 2.

FIG. 6 is a cut-away schematic view of a foot in a shoe with the instrumented semi-rigid orthotic of FIG. 2.

FIG. 7 shows a block diagram of example electronics of the system shown in FIG. 1, including a portion that may be embedded in the instrumented semi-rigid orthotic, and a portion in an external computing device.

FIG. 8 is a flowchart of steps in the operation of an example instrumented semi-rigid orthotic.

FIG. 9a shows a top view of an example of a variable instrumented semi-rigid orthotic (the instrumented semi-rigid orthotic shown in FIG. 2).

FIG. 9b shows an angled view of the example of the variable instrumented semi-rigid orthotic (the instrumented semi-rigid orthotic shown in FIG. 2).

FIG. 9c shows a first side view of the example of the variable instrumented semi-rigid orthotic (the instrumented semi-rigid orthotic shown in FIG. 2).

FIG. 9d shows a second side view of the example of the variable instrumented semi-rigid orthotic (the instrumented semi-rigid orthotic shown in FIG. 2).

FIG. 9e shows a bottom view of the example of the variable instrumented semi-rigid orthotic (the instrumented semi-rigid orthotic shown in FIG. 2).

FIG. 10 illustrates two views of graphed data generated by the instrumented semi-rigid orthotic shown in FIG. 2 and/or one or more additional components of the system shown in FIGS. 1, 7, or other figures.

FIG. 11 illustrates a contextual medical facility example of how the instrumented semi-rigid orthotic (e.g., the “In-Shoe Device” shown in FIG. 11) may be used by a patient (e.g., a user) and a doctor (e.g., a clinician). As several examples, clinicians may include primary care doctors, osteopaths, orthotists, chiropractors, athletic trainers, etc.

FIG. 12 illustrates example electrical components that may be embedded in and/or otherwise coupled to the instrumented semi-rigid orthotic.

DETAILED DESCRIPTION

By way of a brief introduction, and as described above, the present systems and methods may include and/or otherwise utilize a semi-rigid orthotic. A semi-rigid orthotic rotates, flexes, and/or bends under load in three dimensions. A semi-rigid orthotic is configured to temporarily bend under load to control, restrict, or reduce motion of a foot of a user during movement in a designated manner. A semi-rigid orthotic is configured to move in a specific way relative to a shoe, for example, temporarily bending or flexing relative to the shoe responsive to forces applied by a user's foot. To restrict and influence motion, a semi-rigid orthotic is constructed from semi-rigid materials that flex, bend, or rotate under load, and then are able to substantially return to their original shape. For example, when pressure is applied to the semi-rigid orthotic from a user's foot there is a temporary deformation of the semi-rigid orthotic. When pressure is removed, the deformation is partially or completely returned to the original shape of the semi-rigid orthotic.

In contrast, an insole is a piece of material inside a shoe on which a foot rests and is typically designed for accommodation of a pressure point or an anatomical variant, i.e., diabetic ulcer, painful callous. Insoles are not fitted to a particular user. Instead, an insole is generally a standard sole that can be generically placed inside any shoe. An insole is typically flexible and may merely incidentally or minimally control or restrict the motion of a foot. An insole is typically formed from compliant materials such as foam or cardboard, for example. An insole does not move relative to a shoe, other than minor side to side sliding movements or compression (“squishing”) of the insole material caused by pressure from the foot. A rigid orthotic is not configured to bend at all.

A clinician would not look to insoles or rigid orthotics when designing a semi-rigid orthotic device because, as explained above, the present semi-rigid orthotic is not the same as an insole, and not the same as a rigid orthotic. Even insoles or rigid orthotics that include pressure or other sensors that detect changes in pressure in or on an insole or rigid orthotic, or other sensors that detect other parameters in an insole or rigid orthotic, would not be of substantial value to a semi-rigid orthotic device designer. These sensors in an insole or rigid orthotic may be used to determine whether a patient is wearing the insole or rigid orthotic, or periods of no loading, loading, or extreme pressure. These sensors provide disconnected local information, not information about patterns of movement. Such devices are concerned with managing and controlling pressure in the foot (e.g., for treatment of diabetic ulcers and painful callouses). These devices are not concerned with measuring motion of a semi-rigid orthotic or generating motion data that permits comparisons of motion of different portions of a semi-rigid orthotic. Pressure is not the same as flexing, bending, or rotation. Pressure sensors do not generate data that can be used for determining a pattern of motion of a semi-rigid orthotic.

An instrumented, semi-rigid orthotic is described herein. One or more flexible portions of the semi-rigid orthotic can have one or more embedded or secured sensors configured to detect and measure flexing, bending, and/or rotating in three planes. The one or more sensors can include accelerometers, gyroscopes, magnetometers, strain gauges, force transducers, and/or other sensors at one or more locations on the instrumented semi-rigid orthotic. Data can be read from those sensors by a microprocessor that also can be embedded in or secured to the instrumented semi-rigid orthotic, or be included in a separate device. In some embodiments, processing of the data can be performed by the microprocessor and further processing can be optionally performed on an external computing device like a smartphone or tablet computer or cloud-based server, for example. The external device can be in wired or wireless communication with the microprocessor. Data can be displayed on the external device, for example. Data from the instrumented semi-rigid orthotic can be used to adjust the orthotic to align a user's foot, and also works for alignment up the entire kinetic chain including the user's knee, hips, and ankle, for example. Data from the instrumented semi-rigid orthotic can facilitate better data collection, facilitate measurement of human motion, facilitate determination of the effect of cyclic motion on the human body, facilitate tracking limb movement over time, facilitate comparisons to other normalized motion cycles, facilitate analysis of dynamic orthotic performance, be used to evaluate industrial movements and/or tasks, and/or have other uses. Advantageously, data from the instrumented semi-rigid orthotic is personalized to a user.

Processing of the raw data can include determining the direction, magnitude and timing of flexing, bending, and/or rotating of the instrumented semi-rigid orthotic leading to data about the motions of the subtalar and midtarsal joints, for example. Preprocessing can also extract the duration, position, velocity and acceleration of any flexing, bending, and/or rotating at multiple locations on the instrumented semi-rigid orthotic. As one example, the timing can be meaningfully expressed in terms of the point in the stance phase of a gait cycle (as one example of a cyclic motion performed by a user) that an event occurs. This allows measurement and recording of the bending and the motion at the same time. Comparison to data previously taken from that user or other users with the same or other instrumented semi-rigid orthotics can be made to determine the effectiveness of the instrumented semi-rigid orthotic, facilitate better data collection, facilitate measurement of human motion, facilitate determination of the effect of cyclic motion on the human body, facilitate tracking limb movement over time, facilitate comparisons to other normalized motion cycles, facilitate analysis of dynamic instrumented semi-rigid orthotic performance, and/or have other uses. Instrumented semi-rigid orthotic effectiveness, for example, is more than the user's subjective experience; it can also include achieving an optimal clinical alignment of the entire lower extremity. Further, recommendations for altering the geometry or rigidity of the instrumented semi-rigid orthotic can be a component of the results produced by the analysis. If the instrumented semi-rigid orthotic was one with variable tilt settings, the recommendation to alter the geometry could be recommending a new setting. Measuring how the foot and also the thigh move can help create an algorithm to use instrumented semi-rigid orthotic flexing, bending, and/or rotating to evaluate issues with foot, knee and ankle motion.

Each movement pattern the sensors pick up during walking, running, jumping, skiing, swinging a golf club or a tennis racket, a cycling stroke, completing an industrial task, or during any other movement tasks represent distinct patterns that can be grouped into categories, studied, and/or used in a model, for example, to draw conclusions about a particular user. With adjustable instrumented semi-rigid orthotics, cyclic patterns of motion such as gait patterns can be modified. Some applications will combine data from an instrumented semi-rigid orthotic with more traditional measurements of ground reaction forces or motion capture.

This is important because the ground reaction forces are transmitted up the entire lower extremity, often resulting in excessive loads and strains on the bones and joints of the lower extremity. These excessive loads on mal-alignment of the joints of the lower extremity can lead to injuries and poor performance in sports and other functional tasks.

Among other advantages, the use of the data analytics can create a database used to structurally classify and develop motion metrics for different structural variations of the lower extremity. The instrumented semi-rigid orthotic described herein allows a user to control their own body's alignment and/or a physician to make alignment corrections. The instrumented semi-rigid orthotic described herein facilitates customized alignment to the needs of each leg of a user (e.g., for each sport and/or other activity), letting a user find a unique optimal clinical alignment. The instrumented semi-rigid orthotic herein can help both pronated and high arch feet, and/or other user issues, for example.

System

FIG. 1 illustrates a system 10 for AI driven motion tracking and alignment, data collection, measurement of human motion, tracking limb movement, comparisons to other time normalized motion cycles, analysis of dynamic device performance, and other user motion analysis over time, along with facilitation of animation based on such information, and clinical analysis of orthopedics, sports medicine, physical therapy, industrial evaluations, and/or other applications. In FIG. 1 a user is shown examining on her smartphone 105, a rating (as one example of displayed information) determined based on data from an instrumented semi-rigid orthotic 200 she is wearing. In addition to the rating, the smartphone 105 can also stream and display, in real-time, sensor data and/or other information. As shown in FIG. 1, data may be transmitted 20 from instrumented semi-rigid orthotic 200 to smartphone 105 (e.g., via near field communication (NFC) techniques, Bluetooth®, a local area network, and/or other methods), and/or from smartphone 105 to an external computing device 150 (e.g., via a network such as the internet, a cellular network, etc.).

FIG. 2 shows the positions of one or more sensors and computing and communicating devices in an example embodiment of the instrumented semi-rigid orthotic 200. Instrumented semi-rigid orthotic 200 may be made from one or more thermoplastic elastomers, for example, and/or other materials. As one example material, instrumented semi-rigid orthotic 200 may be made from (in whole or in part) a thermoplastic polyester elastomer such as Hytrel (from DuPont) and/or other materials. These materials may have different durometers, and/or other characteristics. There are position, movement, and/or orientation sensors 202 in a flexible forefoot area 101 and near the arch 106. One or more sensors 202 at or near arch 106, for example, may be located in an area of orthotic 200 that may experience the largest amount of flexing, bending, and/or rotation compared to other areas of orthotic 200. In addition, this embodiment includes a pressure or force sensor 103 at the heel 111. Sensors can also include strain gauges and flex sensors, resistive, piezoelectric sensors, and/or other sensors. These and/or other sensors may be electrically connected to a computing device and/or microprocessor 104. The sensors and microprocessor 104 may be thin enough to be embedded in the orthotic 200 and not interfere with its function.

The sensors are not limited by location to the flexible forefoot area 101, the arch 106, and the heel 111. The placement and/or number of locations instrumented for measurement (e.g., the placement and/or the quantity of the one or more sensors) may vary. The sensors can be embedded anywhere in the instrumented semi-rigid orthotic 200 where position, movement, and orientation can be adequately measured.

Additionally, other detectors in the orthotic 200 can measure temperature (detector 210), body weight (detector 212), heart rate (detector 214), timing of motion (detector 216), and other user characteristics. Other sensors and/or detectors for monitoring human capabilities configured to generate data that may be used for interactive feedback and/or data collection permitting optimization of orthotic 200 and other data use are contemplated.

The temperature detector 210 measures the real-time body temperature of the user. The body weight detector 212 measures the body weight of the user, which can be stored to compare to previous body weight measurements. The heart rate detector 214 measures the real-time heart rate of the user. The timing of motion detector 216 measures the timing of motion between the heel and midfoot of the user. The detectors are not limited by location to the flexible forefoot area 101, the arch 106, and the heel 111. The detectors can be embedded anywhere in the instrumented semi-rigid orthotic 200 that is advantageous for their respective measurements. Similar to the sensors, and as described herein, data from these detectors may be used alone and/or in combination with other sensors and/or detectors to determine user movement characteristics and/or patterns, and/or other information. The data may be used for instrumented semi-rigid orthotic 200 adjustment, interactive user feedback, and/or other purposes, for example.

The detectors may be in the same locations as the position, movement, and/or orientation sensors in the instrumented semi-rigid orthotic 200 and/or or in different locations (e.g., as shown in the example in FIG. 2). The detectors 210-216 are embedded at the flexible forefoot area 101, the arch 106, the heel 111, or any location in or on the instrumented orthotic 200.

The one or more sensors and/or detectors are configured to generate one or more output signals comprising information about instrumented semi-rigid orthotic 200 (e.g., as described herein). An output signal from a sensor and/or detector may comprise an electronic signal comprising information indicative of the movement (e.g., bending, flexing, and/or rotating, etc.) of instrumented semi-rigid orthotic 200 and/or other information associated with each sensor and/or detector described above. In some embodiments, the one or more output signals comprise, and/or are used (e.g., by microprocessor 104 and/or other computing components such as smartphone 105 (FIG. 1) and/or an external computing device 150 (FIG. 1) described herein) to generate raw data, one or more charts and/or images indicative of movement and/or other characteristics of instrumented semi-rigid orthotic 200, and/or other information.

The one or more sensors and/or detectors may be configured to generate one or more output signals conveying information related to an orientation, movement, position, and/or other characteristics of instrumented semi-rigid orthotic 200. The information may include and/or be related to an angular position (e.g., a tilt), a spatial position (e.g., in three-dimensional space), a rotation, an acceleration, a velocity, and/or other parameters. The one or more sensors and/or detectors may be configured to operate continuously, at predetermined intervals, and/or at other times before, during, and/or after movement by a user. The one or more sensors and/or detectors may include chip based sensors, for example, included in a surface of and/or embedded into instrumented semi-rigid orthotic 200. The one or more sensors may include accelerometers, gyroscopes, GPS and/or other position sensors, force gages, and/or other sensors as described herein. This information may be used by microprocessor 104 and/or other computing components (described above and below) to determine a location, movement, and/or other information about instrumented semi-rigid orthotic 200.

Sensor and/or detector data may be streamed (in real-time and/or at other times), coming from any or all or a combination of the position, movement, and orientation sensors, and/or one or more of the detectors described herein, in a flexible forefoot area 100, near the arch 106, a pressure or force sensor 103 at the heel 111, detectors 210-216, sensors in other areas of orthotic 200 (e.g., not just in the flexible forefoot area), and/or other sources. These sensors and/or detectors can be individually tracked and displayed on the smartphone 105 (FIG. 1) and/or external computing device 150 (FIG. 1) and/or other computing devices. Alternatively, the data from the sensors and/or detectors can be combined to show a real-time representation of the instrumented semi-rigid orthotic or the user's foot on the smartphone 105, and/or external computing device 150, and/or the other computing devices. As a result, the user (and/or a clinician or other viewers) can view their foot placement during movement, for example. As several examples, clinicians may include primary care doctors, osteopaths, orthotists, chiropractors, athletic trainers, etc.

Furthermore, a comparison of the data from a given sensor and/or detector to the data from other sensors and/or detectors on the instrumented semi-rigid orthotic 200 can be analyzed and/or displayed. A comparison of the sensor and/or detector data from a given sensor and/or detector to previously taken data can be analyzed and/or displayed. A comparison of the sensor and/or detector data from a given sensor and/or detector to the motion and/or other characteristics of the shoe can be analyzed and/or displayed. Other comparisons can also be analyzed and/or displayed.

Real-time or near real-time data streaming may allow for real-time feedback and/or training for the user, for example. This may be personalized for a user. Data from instrumented semi-rigid orthotic 200 facilitates the use of both augmented and virtual reality to allow for avatar based applications and training, as well as presentation of information in a much improved manner, for example.

In some embodiments, data from instrumented semi-rigid orthotic 200 and/or other data may be synchronized with video data of a user. Data from instrumented semi-rigid orthotic 200 may be synchronized with video data from a phone, tablet, and/or computer; AI computer vision software; and/or other data. Data from instrumented semi-rigid orthotic 200 may be synchronized with EMG devices, an EEG, a pressure transducer, a marker-less motion tracking system, and/or other devices. This may provide a hybrid of marker-less and sensor data related to movements of the entire body of a user including joint angles, and/or other information. Data from instrumented semi-rigid orthotic 200 may be synchronized with data associated with a game engine software package, for example, to control and display data in an animated software or fitness experience, and/or various other virtual reality or Metaverse worlds. Data from instrumented semi-rigid orthotic 200 may be streamed into the game engine and/or other software and/or apps to provide counting of repetitions of fitness, measurements of positions, forces, accelerations, and/or other movements, for example. Similar sensors may be placed into the equipment, for example, to generate similar movement information for the equipment. Data from instrumented semi-rigid orthotic 200 may be streamed into the game engine and/or other software and/or apps to control game interactions (playing sports, activating weapons, increase running or walking speed, etc.). Data from instrumented semi-rigid orthotic 200 (in real-time or otherwise) may be used to help animators generate realistic CGI movement patterns for characters generated with GGI. In some embodiments, the electronic system of instrumented semi-rigid orthotic 200 (e.g., sensors, detectors, processors, etc.) is configured to generate or receive a timing pulse configured to facilitate coordination with these other technologies.

In some embodiments, a display (e.g., of smartphone 105 and/or other displays on other computing devices such as a watch, a tablet, a computer, or any device that can connect to the orthotic 200—e.g., on an external computing device 150 (FIG. 1)) can include an avatar configured to facilitate training of the user as well as cyclic motion (e.g., gait as one example) adjustments for the user including coaching and post-op and post-injury rehab. This feedback may be partially generated by real-time feedback from a data model and a database constructed from a chip-based device (such as instrumented semi-rigid orthotic 200 and/or other similar devices) and other means of data capture based on human motion. This might include specific proprietary tasks used in sports performance, work tasks, military tasks, and any other task involving human motion with or without equipment, for example. This real-time or near real-time avatar based instruction may result in an overall lowering of medical costs for a user, along with making instrumented semi-rigid orthotic 200 and/or similar devices available to more users at an affordable price.

FIG. 3 illustrates points of the gait cycle during the stance phase (view 300), and during the stance and swing phases (view 302). The stance phase is from heel strike to toe propulsion. The swing phase occurs after toe propulsion until the next heel strike. These are phases in which useful data may be obtained by instrumented orthotic 200 (see FIGS. 1 and 2) since the orthotic is under load by the user during the stance phase, and not under load (or under a different type of load) during the swing phase, for example. A gait cycle is used in FIG. 3 as one possible example of cyclic motion during walking or running by a user. A user may also experience cyclic motion (with instrumented semi-rigid orthotic 200 in their shoe, boot, etc.) when standing, skiing, cycling, golfing, jumping, kicking, lifting, hitting or moving an object, playing tennis, completing industrial tasks, etc. . . .

FIG. 4 shows a schematic representation of the instrumented semi-rigid orthotic 200 of FIG. 1 and FIG. 2. FIG. 4 shows a bottom view of instrumented semi-rigid orthotic 200. An example electrical interconnection 110 is depicted in this figure, along with some (but not all) of the other components of instrumented semi-rigid orthotic 200 shown in FIG. 2, for example. FIG. 4 illustrates sets of example sensors 107 and 109 (e.g., each of which may include one, some, or all of the sensors and/or detectors described above), a microprocessor 104 (e.g., a central processing unit or CPU), a separately illustrated pressure or force sensor 103, arch 106, and/or other aspects of orthotic 200.

Microprocessor 104 may be configured to provide information processing capabilities for instrumented semi-rigid orthotic 200. As such, microprocessor 104 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. In some embodiments, microprocessor 104 may be included in and/or otherwise operatively coupled with one or more of the sensors and/or detectors described above, a computing device such as a smartphone 105, a desktop computer, and/or a server (e.g., external computing device 150 shown in FIG. 1), and/or other components. Although microprocessor 104 is shown in FIG. 4 as a single entity, this is for illustrative purposes only. In some implementations, microprocessor 104 may include a plurality of processing units. These processing units may be physically located within the same device (e.g., instrumented semi-rigid orthotic 200), or microprocessor 104 may represent processing functionality of a plurality of devices operating in coordination (e.g., a processor located within instrumented semi-rigid orthotic 200 and a second/third/fourth/fifth/etc. processor located within a computing device such as smartphone 105, a desktop computer, a server (e.g., an external computing device 150), etc.). Microprocessor 104 may be configured to execute one or more computer program components. Microprocessor 104 may be configured to execute the computer program component by software; hardware; firmware; some combination of software, hardware, and/or firmware; and/or other mechanisms for configuring processing capabilities on microprocessor 104.

FIGS. 5a-5d show examples of possible flexing, bending, and/or rotating of instrumented semi-rigid orthotic 200. In attempting to control for over-pronation or over-supination, the forefoot area 101 (as one example area) of instrumented semi-rigid orthotic 200 may flex, bend, and/or rotate during points in the gait cycle (see FIG. 3) when weight is put on the forefoot area 101. This flexing, bending, and/or rotating is typically a complex motion in three dimensions. If known, the direction and degree of flexing, bending, and/or rotating can inform a clinician who is trying to achieve an optimal clinical alignment of a lower extremity of a user. Again, as several examples, clinicians may include primary care doctors, osteopaths, orthotists, chiropractors, athletic trainers, etc.

FIG. 6 shows a cut-away schematic view of instrumented semi-rigid orthotic 200. It is shown being worn within a shoe 600 and supporting a foot 602. While the portions of the instrumented semi-rigid orthotic under the toes and heel rest directly on the sole of the shoe 600 when weight is fully applied, the instrumented semi-rigid orthotic 200 can also move in relation to the sole of the shoe at various points during a cyclic movement cycle. Regions 650 and 652 have three-dimensional freedom of movement with respect to the sole 601 of the shoe 600, and ultimately to the ground. These motions can be sensed by the two sets of sensors 107, 109 in this example. Some alternative embodiments may have a wireless sensor 610 (e.g., comprising one or more of the sensors and/or detectors described above) in the shoe 600.

FIG. 7 shows a block diagram of electronics associated with microprocessor 104 and/or instrumented semi-rigid orthotic 200 (FIG. 2). As shown in FIG. 7, sensors 701 (a forefoot three axis accelerometer and/or gyroscope in this example, 703 (an arch region three axis accelerometer and/or gyroscope in this example), and 705 (a force transducer in this example) are in operable communication via electrical interconnection 110 with microprocessor 104 (e.g. comprising a CPU, an integrated digital signal processor (DSP), memory, and communication components), and a wireless transmitter 711. These components may be embedded in the instrumented semi-rigid orthotic (e.g., instrumented semi-rigid orthotic 200 described above), for example. Suitable example components may include the Intel R Quark™ SE microcontroller, said to be the heart of the Intel Curie. The Quark™ SE CPU would be connected to a 3-axis accelerometer, and 3-axis gyroscope, and 3-axis magnetometer IC, also embedded in the orthotic. The STMicroelectronics LSM9DS0 9DOF IMU IC may be a suitable component for this purpose. Strain gauges in two dimensions can also measure the flexing, or bending of the instrumented semi-rigid orthotic.

In some embodiments, electronics associated with microprocessor 104 comprise and/or form an Internet-Connected Device that has both Bluetooth capability and the ability to transmit data to a central database located on the Internet. Instead of bearing the cost and complexity of building and maintaining separate IOS, Android, and Desktop applications to perform this role, a Web Bluetooth API on a web page may be used. This means a single version of software that may run on virtually any modern device to act as the required “middleman” may be used.

In some embodiments, electronics associated with microprocessor 104 comprise an Arduino Nano 33 Bluetooth Low Energy (BLE) Sense Rev2 (Nano) device since it includes a BLE transmitter (with an antennae) and an on-board Accelerometer and Gyroscope. In some embodiments, data may be configured to be transmitted at 100 Hz. In some embodiments, a final transmission rate to a web page after waiting for sensor data to become available may be about 65 Hz. In some embodiments, electronics associated with microprocessor 104 comprise a BN0086 chip (by BOSCH) which comprises an accelerometer, a gyroscope, and a magnetometer (e.g., the one or more sensors described above) as well as sophisticated sensor fusion algorithms, step counting, and auto calibration. The BN0086 chip sensor fusion outputs, including a 1. gyro rotation vector: ˜1,000 Hz output (optimized rotation vector with fast response-suitable for head tracking), and 2. a rotation vector: ˜400 Hz output, as two examples.

The electronic system, overall, is configured to measure and record raw sensor and/or detector data, process it for external analysis and analyze the data. The sensors and/or detectors can also be used to measure gross foot/shoe movement to follow a movement (e.g., gait or other) cycle. Those skilled in the art will know how to determine the point in a cycle a person is in from the data provided by the sensors and/or detectors. FIG. 7 also illustrates communication to a smartphone 105 associated with a user (e.g., via a Bluetooth transceiver in this example), and a (e.g., remote) server 750 (e.g., an external computing device 150 as shown in FIG. 1).

Operation

FIG. 8 shows a flow chart of possible example steps associated with operation of the instrumented semi-rigid orthotic (e.g., as shown in FIG. 2 and described above). This example is for cyclic motion comprising a gait cycle, but similar examples could be generated for other examples of cyclic motion. After starting S99, steps S100 and S101 implement a loop continuously reading data from the sensors and/or detectors to track the gait phase. When a heel strike is detected S101, the data is read and is recorded S102. A rapid spike in deceleration around the heel strike indicates the start of the stance phase. This occurs until toe-off is detected in step S104, ending the stance phase. After several steps, processing is done that can organize and compress the data.

With the complete data for a stance phase, and optionally data from the swing phase, recorded, the operation extracts salient features of the data including flex, bend, or rotate direction, magnitude, timing, duration and the acceleration of flexing, bending, and/or rotating S105. The preprocessing and analysis in this embodiment includes taking the combination of raw data from the various sensors and/or detectors to create a normalized, coherent record of the motions and forces for each gait cycle.

In step S105.5 the relative timing point in the stance phase is aligned and associated with the movement data. The relative orthotic movement data can be aligned on a timeline with the stance phase of the gait cycle for analysis. A person may vary their speed even on a step-by-step basis; therefore the wall-clock time when a relevant data point is captured can be difficult to match for multiple steps from multiple persons. The more useful timing is the point in the gait cycle that a particular event occurs. Establishing a time normalized gait cycle permits comparison of multiple trials to each other.

In step S106 the salient data is compared to data from many trials with many users and many orthotic geometries to produce a rating of effectiveness S107 (and/or facilitate better data collection, facilitate measurement of human motion, facilitate determination of the effect of gait on the human body, facilitate tracking limb movement over time, facilitate comparisons to other normalized motion cycles, facilitate analysis of dynamic orthotic performance, etc.). Data from trials with individuals with known foot problems and known optimum orthotics, including trials with alternant, non-optimum orthotics, are used for comparison. Comparisons to data previously taken from other users or the current user with the same or other instrumented semi-rigid orthotics can be made to determine the effectiveness of the instrumented semi-rigid orthotic, facilitate better data collection, facilitate measurement of human motion, facilitate determination of the effect of gait (in this example) on the human body, facilitate tracking limb movement over time, facilitate comparisons to other normalized motion cycles, facilitate analysis of dynamic orthotic performance, and/or have other uses. Furthermore this data can be compared to data taken on bone and joint motion in a human performance lab. That data can be analyzed manually, with computerized algorithms, with artificial intelligence software, by classification and clustering techniques as taught in Selner U.S. Pat. No. 8,139,822, Designation of a Characteristic of a Physical Capability by Motion Analysis Systems and Methods (which is incorporated by reference in its entirety), and/or by other methods to produce a rating of the effectiveness of an orthotic for a wearer. It can also be used to construct a predictive model for improved treatment and/or have other uses. Data analysis is further described below.

The operation can optionally include step S108 and generate a recommendation for an improved instrumented semi-rigid orthotic for the tested user. In some embodiments, S108 comprises determining and/or recommending an adjustment to alter the geometry of the instrumented semi-rigid orthotic in an attempt to provide a better fit to a user and achieve an improved alignment, and/or for other purposes. This improved alignment may help the user in achieving optimal clinical alignment of the lower extremity (reducing the rotational and vertical forces being transmitted up the kinetic chain of the lower extremity as a result of ground reaction forces). Recommendations for adjustments to modify the geometry of the instrumented semi-rigid orthotic can include: suggesting a completely new orthotic or adding to or removing material on the present orthotic as traditionally done by podiatrists or orthotists. It could also include a setting change for a variable version of the instrumented semi-rigid orthotic, or it could provide information to make a 3D printing of an optimized orthotic. If the user is still walking and still in the trial, the process repeats, starting back at S100.

The instrumented semi-rigid orthotic 200 (FIG. 2) and the sole of the shoe 600 (see FIG. 6) have relative freedom of motion between them. As part of an analysis, the location of a floor or the ground can be determined by assuming the floor or ground is a plane generally tangent to the earth and knowing the location of the shoe at a heel strike. To calculate relative motion between the floor or ground and the instrumented semi-rigid orthotic by effectively doing a 3D comparing of the location, motion and orientation of the shoe from the same information for one or more regions of the instrumented semi-rigid orthotic, shoe-orthotic relative motion can be determined.

Location of Computation Steps

Some of the steps of FIG. 8 are carried out by an instrumented orthotic such as instrumented semi-rigid orthotic 200 shown in FIG. 2, for example by firmware and/or software executed by an embedded microprocessor 104. However, to keep the cost, size, and power dissipation low, many subsequent steps may be carried out by an application executing on a smartphone (e.g., 105 shown in FIG. 1), tablet computer or other external computing device (e.g., 150 shown in FIG. 1) including a remote server 750 (e.g., as shown in FIG. 7). In that case, raw, preprocessed or partially analyzed data may be transmitted from the instrumented semi-rigid orthotic to a nearby computing and display device. In some embodiments data may be sent from the phone to a server and database via the cloud and the information returned from the server may supply the final information to have the phone display to a user.

The embodiment shown in FIG. 7 has a wireless transceiver 711 for communicating with an external computing device 150 (FIG. 1) a smartphone 105 (FIG. 1) or tablet computer or cloud-based server 750 (FIG. 7), for example. After preliminary preprocessing by software executing on the embedded microprocessor 104, data may be wirelessly sent to the external device for further processing and analysis. Steps S99 through S102 or alternatively S99 through S104 described in FIG. 8 may be performed on the semi-rigid orthotics' electronics with the other steps performed on an external device or remote server in conjunction with data in a database, for example. In some embodiments, the microprocessor 104 (FIG. 7, FIG. 4, FIG. 2) may have sufficient computing power to perform more of, or all of, the steps of an analysis. If some data or all data is sent to a remote server (e.g., server 750 shown in FIG. 7) with a database, the server may analyze the current data in light of other data from the same user or in light of an aggregation of data including data from other users.

Variable Orthotics

Microprocessor 104 (FIG. 1), smartphone 105 (FIG. 1), a cloud-based server, and/or other computing devices 150 (FIG. 1) may be configured to determine and/or communicate one or more recommendations for an improved instrumented semi-rigid orthotic. An improvement may comprise an adjustment, a new and/or different geometry, and/or other improvements determined as described above. A recommendation for an adjustment may be determined based on the sensor and/or detector data and/or the processing of the sensor and/or detector data described above, and/or other information. A recommendation for an improved instrumented semi-rigid orthotic may include directing setting adjustments on a variable or adjustable orthotic, and/or other recommendations. Examples of adjustable orthotics are described in, for example, U.S. Pat. No. 8,749,115 to Pas, et al.; U.S. Pat. No. 5,345,701 to Smith; U.S. Pat. No. 8,490,301 to Selner; and U.S. Pat. No. 7,707,751 to Avent, et al.

FIGS. 9a-9e show views of an example variable version of instrumented semi-rigid orthotic 200. In this example, the variable orthotic is similar to and/or the same as a variable orthotic described by Selner U.S. Pat. No. 8,490,301, Adjustable Forefoot Posting for Orthotic, which is incorporated by reference in its entirety. FIG. 9a shows a top view of the example variable orthotic. FIG. 9b shows an angled view of the example variable orthotic. FIG. 9c shows a first side view of the example variable orthotic. FIG. 9d shows a second side view of the example variable orthotic. FIG. 9e shows a bottom view of the example variable orthotic. Tilt settings on this example variable orthotic are adjusted by turning a selector wheel 901 (some or all of which may or may not be visible in the different views). If an adjustable orthotic can have its setting changed electrically, the recommended change could be directly transmitted back to the orthotic to directly institute a geometry adjustment. This might be practical if done when there is no weight on a post position (e.g., see the circle at the center of the wheel) of selector wheel 901, for example.

Alternative adjustment mechanisms are contemplated. For example, in some embodiments, instrumented semi-rigid orthotic 200 (FIG. 2) may include an adjustable device such as a piezoelectric transducer 903. Piezoelectric transducer 903 is configured to generate mechanical displacement based on an electrical signal, for example. A piezoelectric transducer may be configured to receive an electronic signal (e.g., from microprocessor 104, a smartphone 105, a server 750, etc.) that conveys a determined change that should be made (e.g., determined based on sensor and/or detector data and/or other information as described above), which can then be used to alter instrumented semi-rigid orthotic 200 (e.g., via mechanical displacement of the piezoelectric transducer 903) and/or another adjustable device accordingly. The piezoelectric transducer 903 positioned in the instrumented semi-rigid orthotic 200 converts electrical energy into mechanical energy and/or mechanical displacement, which results in adjustments in mechanical alignment of the instrumented semi-rigid orthotic to the user's foot. As a result, the instrumented semi-rigid orthotic can more closely conform to the foot of the user. These adjustments (e.g., the adjustments caused by the piezoelectric transducer 903 and/or other adjustments described herein) may facilitate alignment and/or movement of the lower extremity of the user including the foot, leg, and thigh, for example. In some embodiments, the adjustment may be automated based on a signal from a processor and/or server, etc., such that user input is not required for such an adjustment.

In some embodiments, instrumented semi-rigid orthotic 200 may be adjusted by inserting and/or otherwise coupling modular components to instrumented semi-rigid orthotic 200. The adjustment may be based on a recommendation from the microprocessor, smartphone, server, etc., as described above. The number, geometry, location, and/or other characteristics of such modular components (e.g., an adjustment) may be determined based on the sensor and/or detector data described above, and/or other information. Based on the sensor and/or detector data, modular components added to the orthotic can tilt or elevate the instrumented semi-rigid orthotic 200 in different increments. For example, the modular components can elevate a heel of the foot incrementally. Modular components attached to the heel can also tilt the heel in a valgas or varus manner in different increments. When attached properly, the modular components will change the timing of motion of the heel and midfoot of the user. Other areas of the foot can be tilted or elevated as well, including the forefoot area and the arch. The modular components comprise geometries of different lengths, widths, and thicknesses. Rubber, thermoplastics, and/or other types of materials can also be used for the modular components.

With enough computing power embedded in instrumented semi-rigid orthotic 200 that can dynamically alter its geometry, an external electronic computing device may become optional. In some embodiments the external portable electronic device such as a smart cellphone, tablet, or special purpose unit can be used to display results, display recommended adjustments, and/or perform other operations.

Among other advantages, improving joint alignment by make adjustments like these, reduces the rotational and vertical joint forces resulting from ground reaction forces being transmitted up the kinetic chain of the lower extremities of a user. As described herein, to restrict and influence motion, the present orthotic devices are constructed from semi-rigid materials that flex, bend, or rotate under load. During the stance phase of the gait cycle (from heel strike, through mid-stance, to propulsion)—as one possible example of cyclic movement by a user-weight is placed on the instrumented semi-rigid orthotic causing a complex flexing, bending, and/or rotating in three planes—the sagittal, frontal, and transverse, at multiple regions on the instrumented semi-rigid orthotic. That movement occurs in the shoe, under the foot and thus is not information directly available to a clinician evaluating the effectiveness of the orthotic, wanting better data collection, trying to measure human motion, determine the effect of gait on the human body, track limb movement over time, compare data to other normalized motion cycles, analyze dynamic orthotic performance, etc. Although a clinician can carefully watch a person walking back and forth while wearing a trial orthotic, the users' reported subjective experience provides the primary clues to the clinician. The present systems and methods make fitting an orthotic to an individual and/or adjusting the orthotic data based, and less subjective (e.g., less based on a subjective impression of a clinician and/or a user). The present systems and methods reduce the need for a clinician in many cases, and lower the overall cost of getting effective orthotics on users' feet, among other advantages.

Other Variations

In some embodiments, one or more components of instrumented semi-rigid orthotic 200 may be connected wirelessly and/or by wires to an external computing device. The external computing device may be handheld and/or worn by a user (e.g., in the form of a watch and/or other devices), and/or have other forms.

In some embodiments, a flexible printed circuit board (PCB) with one or more of the electronic components described above soldered onto it may be provided in and/or coupled to instrumented semi-rigid orthotic 200. The flexible circuit board may be permanently or temporarily adhered to any embodiment of orthotic 200 such that instrumented semi-rigid orthotic 200 is an instrumented orthotic as described. In some embodiments, the PCB may include and/or be operatively coupled to an IMU Sensor such as the BOSCH BMI323 sensor, a Bluetooth module such as the Laird 453-00091C module, serial flash components such as a SST25VF512A microchip, a coin cell power source such as a CR2032, a light emitting diode (LED) that provides a status indicator for the PCB, and/or other components.

In some embodiments, the relative position and movement between the sole of the shoe and the instrumented semi-rigid orthotic 200 may be a useful measurement, and one or more wireless sensors (e.g., an additional sensor in the sets of sensors and/or detectors described above) may be embedded into the shoe and/or temporarily coupled to either the inside or outside of the sole as depicted in FIG. 6 as element (e.g., wireless sensor) 610. The same or similar sensors and computing described above as part of the instrumented semi-rigid orthotic 200 may be used to instrument the shoe, or a simpler, less power consuming electronic arrangement might be used for such a purpose.

In some embodiments, data from the one or more sensors and/or detectors in one or more locations on/in instrumented semi-rigid orthotic 200 may be transmitted directly to an external computing device or, saving power, may be stored in a memory located on instrumented semi-rigid orthotic 200, for example, for later download and/or analysis by an external computing system. There are many suitable wireless technologies that can be used to achieve this transmission, including near field communication (NFC) components.

In some embodiments, there may not be a local device performing significant computing, but being used primarily as an output display device. In some embodiments, there may not be a local computing device communicating with the instrumented orthotic. Data from the instrumented semi-rigid orthotic may be transmitted directly to the Internet and/or another destination via cellular communications, WIFI, and/or other technology. This information may be received by a remote computer, a server, for example, and/or other computing devices. In other embodiments there may be no server connection necessary.

Other Uses for the System

In some embodiments, similar sensors and/or detectors (and associated circuitry, processor(s), etc.), may be embedded in and/or otherwise coupled to shoes, boots, platforms, scales, floors, equipment associated with user standing and/or movement, and/or other objects. In some embodiments, the sensors and/or detectors (and associated circuitry, processor(s), etc.), may be used with and/or built into a shoe, a boot, a sandal, a high heel shoe, or other footwear. In some embodiments, the electronic system is used with and/or built into a specialized shoe and/or boot (e.g., a track shoe, a ski boot, etc.), a high heel shoe, or other footwear, medical equipment configured for testing industrial function, work out equipment, sports equipment, and/or other devices. Similar analysis and determinations may be made based on data from such systems.

Instrumented semi-rigid orthotic 200 can be used in research to collect data otherwise unavailable. In some embodiments, data collected by instrumented semi-rigid orthotic 200 can be compared to measurements taken in a human performance lab as a tool contributing to research, and/or in generating a predictive model that can help a clinician or consumer with a specific problem, for example. Data from instrumented semi-rigid orthotic 200 may also be used to determine and/or train one or more algorithms, a predictive model, and/or other resources to assist clinicians and/or end users to better treat abnormalities and/or prevent injuries, as well as improve athletic performance. Algorithms and/or predictive models can also be constructed to more precisely identify individuals with correctable mechanically induced problems, for example, and/or for other purposes.

For example, FIG. 10 illustrates two views 1000 and 1002 of graphed data generated by instrumented semi-rigid orthotic 200. This may be personalized for a user. Graphed data like this can be compared to corresponding data from many trials with the same or different users, and/or many orthotic geometries. Graphed data like this may be used to determine patterns, and/or other information, for example. (Note data from instrumented semi-rigid orthotic 200 need not actually be graphed but may instead and/or additionally be analyzed as raw data, filtered data, data in numerical form, etc.) In this example, different curves 1004 and 1006 may be generated compared, and/or 1008 and 1010 may be generated and compared, to each other, to data in a database of thousands of users and/or orthotic geometries, and/or to other data. In the example shown in view 1000 pronation or supination was determined during a phase of cyclic use motion—the stance phase of a gait cycle in this example (as one example of cyclic movement of a user). Curve 1004 was generated and analyzed, an assessment and/or adjustment was made (e.g., a geometric adjustment of orthotic 200), and then curve 1006 was generated for a user. In this example, pronation increased and supination decreased as a result of the assessment and/or adjustment. In the example shown in view 1002 pronation or supination was determined during the stance and swing phases of a gait cycle. Curve 1008 was generated and analyzed, an assessment and/or adjustment was made (e.g., a geometric adjustment of orthotic 200), and then curve 1010 was generated for a user. In this example, pronation increased and supination decreased during the stance phase, and fluctuation between the two decreased during the swing phase, as a result of the assessment and/or adjustment.

In FIG. 10, the graphed data is shown on a 0, 25%, 50%, 75%, and 100% time scale. The graphed data has been time normalized for a gait cycle (in this example) of the user. Time normalization may facilitate comparison of data for movement cycles of different lengths (e.g., from the same user, between users, between populations of users, etc.) For example, if the gait cycle of user 1 is 1.4 seconds, and the gait cycle of user 2 is 1.8 seconds, normalizing each gait cycle on a 0-100% scale facilitates comparisons at different points in the gait cycles for each user. Advantageously, instead of, for example, measuring these curves and concluding that one peak pronation or supination for each user was a certain value, time normalized curves facilitate comparisons of patterns. For just the first 25% of the gait cycles these two curves may be mathematically compared to determine similarities and/or differences. Other examples of pattern recognition as opposed to analysis of discreet points are contemplated.

In some embodiments, data like this and/or other data generated by instrumented semi-rigid orthotic 200 (FIG. 2) may be used to generate an artificial intelligence (AI) driven pattern recognition algorithm and/or model. Processor(s) 104, smartphone 105, server 750, and/or other portions of the present systems and methods may be configured to execute such algorithms and/or models. Keeping with the example data shown in FIG. 10, processor(s) 104, smartphone 105, server 750, and/or other portions of the present systems and methods may be configured by machine-readable instructions to use one or more pattern recognition algorithms, and time normalized data from the one or more sensors (e.g., as shown in the graphed curves 1004-1010), to detect variation in graphical depictions of movement cycle shapes (e.g., curves 1004-1010). Fourier analysis, root mean square (RMS) analysis, and/or other techniques may be utilized to compare curves (e.g., 1004 to 1006, or 1008 to 1010) at different points, for example, and/or for other operations. For example, curve comparison after time normalization using root mean square and/or other methods may be performed. Instrumented semi-rigid orthotic 200 may be perturbed and curves may be compared again. Peak velocity and acceleration may be evaluated as well as all reversal of movement points, for example. A Fourier frequency analysis may be used to measure and quantify a closed kinetic chain range of motion (ROM), for example.

In some embodiments, instrumented semi-rigid orthotic 200 may be used to measure movement of each limb during cyclic motion. Forward acceleration of the foot and shoe may be determined, for example. A comparison between limbs may be made, comparisons before and after injury recovery may be made, and/or other comparisons may be made. In some embodiments, data from instrumented semi-rigid orthotic 200 may be used to track a swing phase's 3D motion of the foot-shoe complex. Data from instrumented semi-rigid orthotic 200 may be used to develop a classification and/or other models. Data from instrumented semi-rigid orthotic 200 may be used to determine a score for pre and post-surgery evaluations and/or other scores. As described herein, data from instrumented semi-rigid orthotic 200 may be used to show an animated foot moving to visually represent a particular percentage of a movement cycle. Data from instrumented semi-rigid orthotic 200 may be used to determine foot morphology for software that may create 3D models of feet for classification and/or other purposes. Data from instrumented semi-rigid orthotic 200 may be compared to other sensors around a user's body (e.g., above the knee), for example. In some embodiments, a device similar to and/or the same as instrumented semi-rigid orthotic 200 (and/or the sensor/detector arrangement of instrumented semi-rigid orthotic 200) may be included in a body configured to couple to the knee or other body parts of a user). In some embodiments, data from instrumented semi-rigid orthotic 200 may be used to determine propulsive acceleration and velocity control, navicular motion, tibial motion, ratios of multiple plane motions like sagital to transverse, frontal plane knee torques, centers of gravity, multiple plane motions, kinetic chain variables, loading characteristics, and/or other information.

An algorithm and/or model (e.g., an AI algorithm and/or AI model) may be used to classify people into different types and track there data for future comparison, for example. As described above, a predictive model may help a clinician or consumer with a specific problem. Data from instrumented semi-rigid orthotic 200 may be used to determine and/or train one or more algorithms, a predictive model, and/or other resources to assist clinicians and/or end users to better treat abnormalities and/or prevent injuries, as well as improve athletic performance. Algorithms and/or predictive models can also be constructed to predict and/or more precisely identify individuals with correctable mechanically induced problems, for example, and/or for other purposes. For example, processor(s) 104, smartphone 105, server 750, and/or other portions of the present systems and methods (e.g., system 10) may be configured to use data from instrumented semi-rigid orthotic 200 for measuring time normalized cyclic motion of a user year by year (and/or at more frequent time intervals) to determine whether changes over time (or lack thereof) indicate potential problems (e.g., early neurological and/or mental health problems, possible potential injuries, post op orthopedic problems, sports medicine problems, rehabilitation problems, etc.). Generative models, large language models, neural networks, and/or other machine learning techniques may be configured to use this data to make predictions for users (e.g., predict who might tear an ACL and/or be prone to another injury, predict which children might be more or less athletically inclined, predict an optimal piece of equipment and/or a setting for such equipment, predict stability in senior citizens, predict a type of sport or work that is best for a user, etc.), classify a user in a certain way (e.g., higher risk for injury, lower risk for injury, etc.), and/or generate other outputs.

The presents systems and methods are configured to generate data for AI applications in a rich data model. With these and other techniques, data can be measured and/or otherwise quantified over time (even over a period of years). The present systems and methods may be used for evaluating any cyclic event or task that creates a need for expertise. The present systems and methods may be used for any test of human motion that results in a rating, score, or classification. The present systems and methods are configured to generate data used to create unique metrics useable for analyzing how each individual creates a unique cycle during an event or task (e.g., standing, walking, running, skiing, cycling, golfing, jumping, kicking, lifting, hitting or moving an object, playing tennis, completing industrial tasks, etc.)

With the flexibility provided by instrumented semi-rigid orthotic 200, this data may be obtained remotely, for example, while data from instrumented semi-rigid orthotic 200 is generated and transmitted from the user's shoes during daily activities to a computing device associated with a remotely located clinician. As another example, processor(s) 104, smartphone 105, server 750, and/or other portions of the present systems and methods may be configured to use data from instrumented semi-rigid orthotic 200 to track a user's cyclic motion and determine that the user's shoes should be replaced. In this example, a trained machine learning model may receive data from instrumented semi-rigid orthotic 200 as input and be trained (e.g., using prior data from that user, and/or database data from thousands of other users) to predict and/or otherwise determine that the user's shoes should be replaced. As another example, in some embodiments, microprocessor 104 (see FIG. 4 and FIG. 6 and the description above), a smartphone 105 (see FIG. 1), a server 750 (see FIG. 7), and/or other computing components are configured to use one or more artificial intelligence algorithms and/or models to generate and/or provide feedback and/or coaching such as motion adjustments and/or post op and post injury rehabilitation instructions. Note that these are just a few representative examples of many different possible examples.

In some embodiments, microprocessor 104, a smartphone 105, a server 750, and/or other computing components are configured to execute one or more artificial intelligence algorithms and/or models such as a trained machine learning model based on machine readable instructions. In some embodiments, microprocessor 104, a smartphone 105, a server 750, and/or other computing components are configured to cause a machine learning model, for example, to be trained using training information. In some embodiments, the machine learning model is trained by providing the training information as input to the machine learning model. In some embodiments, the machine learning model may be and/or include mathematical equations, algorithms, plots, charts, a large language model (LLM), networks (e.g., neural networks), and/or other tools and machine learning model components. For example, the machine learning model may be and/or include one or more neural networks having an input layer, an output layer, and one or more intermediate or hidden layers. In some embodiments, the one or more neural networks may be and/or include deep neural networks (e.g., neural networks that have one or more intermediate or hidden layers between the input and output layers).

As an example, neural networks may be based on a large collection of neural units (or artificial neurons). Neural networks may loosely mimic the manner in which a biological brain works (e.g., via large clusters of biological neurons connected by axons). Each neural unit of a neural network may be connected with many other neural units of the neural network. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some embodiments, each individual neural unit may have a summation function that combines the values of all its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function such that a signal must surpass the threshold before it is allowed to propagate to other neural units. These neural network systems may be self-learning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. In some embodiments, neural networks may include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some embodiments, back propagation techniques may be utilized by the neural networks, where forward stimulation is used to reset weights on the “front” neural units. In some embodiments, stimulation and inhibition for neural networks may be more free flowing, with connections interacting in a more chaotic and complex fashion.

As described above, the trained neural network may comprise one or more intermediate or hidden layers. The intermediate layers of the trained neural network include one or more convolutional layers, one or more recurrent layers, and/or other layers of the trained neural network. Individual intermediate layers receive information from another layer as input and generate corresponding outputs. In some embodiments, the trained neural network may comprise a deep neural network comprising a stack of convolution neural networks, followed by a stack of long short term memory (LSTM) elements, for example. The convolutional neural network layers may be thought of as filters, and the LSTM layers may be thought of as memory elements that keep track of data history, for example. The deep neural network may be configured such that there are max pooling layers which reduce dimensionality between the convolutional neural network layers. In some embodiments, the deep neural network comprises optional scalar parameters before the LSTM layers. In some embodiments, the deep neural network comprises dense layers, on top of the convolutional layers and recurrent layers. In some embodiments, the deep neural network may comprise additional hyper-parameters, such as dropouts or weight-regularization parameters, for example.

In some embodiments, the trained machine learning model is trained by obtaining and providing prior sensor output signals associated with one or more users to the machine learning model. Portions of the prior sensor output signals may be labeled with relevant information (e.g., related to alignment or misalignment, worn out shoes, neurological disorders, etc., and/or data related to other possible operations).

In some embodiments, the same and/or similar sensors and/or detectors (and associated circuitry, processor(s), etc.), may comprise and/or be associated with an animation data generation system; tracking patients, athletes, etc., in real time (near real time, or at a later time), and providing such data to clinicians, coaches, etc., to provide for better patient, athlete, etc., management with fewer complications; industrial applications and/or evaluations to monitor and/or reduce and/or prevent injuries in the work place; sequential evaluation of time normalized cyclic motion patterns as children grow; and/or other applications. This disclosure contemplates other as yet undetermined applications for this emerging technology. As one possible example application, an animation data generation system may comprise one or more sensors (e.g., sensors similar to and/or the same as the sensors and/or detectors described above) configured to be located at or near one or more locations on a foot of a user. The one or more sensors are configured to generate output signals conveying information related to position, movement, and orientation of one or more different regions of the foot. The one or more sensors may be used with or without shoes and/or other footwear, for example. The one or more sensors may be configured to be removably inserted into a shoe (and/or other footwear) of the user with the foot. The foot may temporarily flex, bend, and/or rotate in sagittal, frontal, and transverse planes during movement throughout a motion cycle (e.g., as described above).

One or more processors (e.g., similar to and/or the same as a processing component such as microprocessor(s) 104, and/or other components described herein) may be configured to determine, based on the information in the output signals, a pattern of motion of the foot, and tracking of that motion, and/or other information. The pattern of motion and/or the tracking may comprise a timing, direction, and degree of flexing, bending, and/or rotating of the different regions of the foot at multiple points during movement throughout the movement cycle of the user, for example. The pattern of motion and/or tracking of the foot is configured to be used to generate animation that accurately depicts positions and/or movements of the user's foot, ankle, knee, leg, and/or hip, and/or other animation. As another example, the pattern of motion and/or tracking of the foot is configured to be used to evaluate movements of the user's foot, ankle, knee, leg, and/or hip, and/or other evaluation. In some embodiments, the one or more processors are operatively coupled to a Bluetooth low energy transmitter having an antennae (e.g., as described above).

In some embodiments, the animation example comprises computer generated imagery (CGI) and/or other animation. Data from the one or more sensors and/or the one or more processors is configured to be used to help animators generate realistic CGI movement patterns for characters generated with GGI.

As described herein, the one or more sensors (e.g., sensors 202, detectors 210-216, etc.) may comprise one or more accelerometers, gyroscopes, magnetometers, strain gauges, force transducers, pressure transducers, temperature sensors, weight sensors, timing sensors, location sensors, and/or other sensors and detectors. The one or more processors may be configured to use one or more pattern recognition algorithms, and time normalized data from the one or more sensors, to determine a pattern of motion, and then generate the animation, pattern recognition (e.g., using artificial intelligence (AI), deep learning, and/or other tools), motion tracking, etc. In some embodiments, data may be collected and, using AI and/or deep learning, to facilitate classification models and a database to allow more personalized applications of the data (e.g., similar to and/or the same as the analysis described in other paragraphs herein).

In some embodiments, the one or more sensors (e.g., sensors 202, detectors 210-216, etc.) are configured to be directly coupled to the foot of the user at or near the one or more locations (e.g., via adhesive, tape, etc.). In some embodiments, a foot covering configured to be worn on the foot of the user is configured to carry the one or more sensors and locate the one or more sensors at or near the one or more locations on the foot of a user. For example, the one or more sensors may be coupled to the foot covering at locations on the foot covering that will eventually correspond to the one or more locations on the foot when the user wears the foot covering.

In some embodiments, the foot covering comprises a sock, a strap, multiple straps, a strap system, and/or other foot coverings. For example, the foot covering may comprise a sock with the strap, multiple straps, and/or strap system integrally incorporated into the sock (e.g., via sewing and/or some other incorporation method). As another example, the foot covering may comprise the strap, multiple straps, and/or the strap system. The strap(s) and/or strap system may or may not be configured for mechanical control of the foot. In some embodiments, the foot covering comprises just the strap. The one or more sensors may be sewn into or onto the strap, glued to the strap, clamped to the strap, etc. The strap may be configured to be coupled to the foot at a first end (of the strap) on a side of an arch of the foot, wrap under the arch and over a top of the foot so that a second end of the strap is configured to be coupled to the foot and/or the first end on the side of the arch. In some embodiments, the foot covering comprises multiple straps, and/or the strap system. The multiple straps and/or the strap system may comprise a first strap (e.g., with the one or more sensors coupled to the strap) configured to be wrapped around an arch of the foot, and a second strap (e.g., with one or more additional sensors) configured to be wrapped around a heel of the foot, for example. These locations and example foot coverings are not intended to be limiting. The present systems and methods contemplate any type of foot covering that is able to position the one or more sensors at or near the one or more locations on the foot, so that the system can function as described herein for animation, tracking, motion analysis, classification, and/or other purposes.

FURTHER DESCRIPTION OF FIGURES

Returning to FIGS. 1, 2, 4, 6, and 7, instrumented semi-rigid orthotic 200 may be operatively coupled to one or more computing devices such as a smartphone 105, a laptop computer, a tablet, a desktop computer, a gaming device, a server 750 and/or other networked external computing devices 150, which may have a display, a user input device (e.g., buttons, keys, voice recognition, or a single or multi-touch touchscreen), memory (such as a tangible, machine-readable, non-transitory memory), a network interface, an energy source (e.g., a battery), and a processor such as a microprocessor 104 (a term which, as used herein, includes one or more processors) coupled to each of these components. Memory may store instructions that when executed by the associated processor provide an operating system and various applications, including a web browser or a native mobile application, for example. In addition, a computing device may include a user interface, which may include a monitor; a keyboard; a mouse; a touchscreen; etc. . . . A user interface may be operative to provide a graphical user interface associated with orthotic 200 that communicates with the sensors and/or detectors described above, microprocessor(s) 104, and/or other components, and facilitates user interaction with data from the sensors and/or detectors (e.g., sensors 202, detectors 210-216, etc.).

A user interface is configured to provide an interface between instrumented semi-rigid orthotic 200 and users (e.g., the user shown in FIG. 1, a clinician, etc.) through which users may provide information to and receive information from instrumented semi-rigid orthotic 200. This enables data, results, and/or instructions, and any other communicable items, collectively referred to as “information,” to be communicated between the users and one or more of the sensors, detectors, microprocessor(s) 104, a computing device, external resources, and/or other components. Examples of interface devices suitable for inclusion in a user interface include a keypad, buttons, switches, a keyboard, knobs, levers, a display screen, a touch screen, speakers, a microphone, an indicator light, an audible alarm, a printer, and/or other interface devices. In one embodiment, a user interface includes a plurality of separate interfaces (e.g., an interface on instrumented semi-rigid orthotic 200, an interface in a computing device, etc.). It is to be understood that many communication techniques, either hard-wired or wireless, between one or more components of instrumented semi-rigid orthotic 200 and/or the described computing device(s) are contemplated by the present disclosure. Other exemplary input devices and techniques adapted for use with orthotic 200 as a user interface include, but are not limited to, an RS-232 port, RF link, an IR link, modem (telephone, cable or other). In short, any technique for communicating information with instrumented semi-rigid orthotic 200 is contemplated by the present disclosure as a user interface.

In some embodiments, instrumented semi-rigid orthotic 200 and/or an associated computing device may include electronic storage. Electronic storage comprises electronic storage media that electronically stores information. The electronic storage media may include one or both of system storage that is provided integrally (i.e., substantially non-removable) with instrumented semi-rigid orthotic 200 and/or a computing device, and/or removable storage that is removably connectable to orthotic 200 via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. Electronic storage may store software algorithms, information determined by microprocessor(s) 104, information received via a user interface, and/or other information that enables instrumented semi-rigid orthotic 200 to function properly. Electronic storage may be (in whole or in part) a separate component within instrumented semi-rigid orthotic 200 and/or a computing device, or electronic storage may be provided (in whole or in part) integrally with one or more other components of system 10 (FIG. 1). In some embodiments, instrumented semi-rigid orthotic 200, a computing device, and/or

other components may be operative coupled to one or more external resources. External resources, in some embodiments, include sources of information such as databases, websites, etc.; external entities participating with instrumented semi-rigid orthotic 200 (e.g., systems or networks associated with instrumented semi-rigid orthotic 200), one or more outside servers, a network (e.g., the internet), electronic storage, equipment related to Wi-Fi™ technology, equipment related to Bluetooth R technology, data entry devices, or other resources. In some implementations, some or all of the functionality attributed herein to external resources may be provided by resources included in instrumented semi-rigid orthotic 200 and/or an associated computing device. External resources may be configured to communicate with one or more components of instrumented semi-rigid orthotic 200 via wired and/or wireless connections, via a network (e.g., a local area network and/or the internet), via cellular technology, via Wi-Fi technology, and/or via other resources.

In some embodiments, a network (see cloud element in FIG. 1 and FIG. 7, for example) may include the internet, a Wi-Fi network, Bluetooth® technology, and/or other wireless technology. In some embodiments, instrumented semi-rigid orthotic 200, one or more microprocessors 104, a computing device (e.g., smartphone 105), a server (e.g., server 750), external computing devices 150, other external resources, and/or other components communicate via near field communication, Bluetooth, and/or radio frequency; via the network (e.g., a network such as a Wi-Fi network, a cellular network, and/or the internet); and/or by other communication methods.

In the figures, instrumented semi-rigid orthotic 200, one or more microprocessors 104, one or more computing devices (e.g., smartphone 105), and/or other components are shown as separate entities. This is not intended to be limiting. Some and/or all of these components and/or other components may be grouped into one or more singular devices. Some of these and/or other components may be included in a wearable worn by a user. The wearable may be a watch, for example, and/or other wearables. Such a wearable may include means to deliver output (e.g., a display screen) to a user.

FIG. 11 illustrates a contextual medical facility example of how instrumented semi-rigid orthotic 200 (e.g., the “In-Shoe Device” shown in FIG. 11 is the same as instrumented semi-rigid orthotic 200 shown in FIG. 2) may be used by a patient (e.g., a user) and a doctor (e.g., a clinician). As shown in FIG. 11, instrumented semi-rigid orthotic 200 may be worn in a patient's shoe (In-Shoe Device). The device will collect sensor data related to the patient's foot position throughout their motion cycle. By collecting and analyzing this data, powerful and actionable diagnostic information may be provided to the doctor. As described above, the In-Shoe Device (instrumented semi-rigid orthotic 200) is a custom adjustable orthotic that the patient (user) wears, comprising a small circuit board embedded and/or otherwise coupled to (e.g., a bottom side of) instrumented semi-rigid orthotic 200. The electronics may include chip(s) to measure various sensor signals and a Bluetooth Low Energy (BLE) transmitter to offload the sensor data to a central database for processing and/or analysis (e.g., as described above).

Bluetooth devices do not connect directly to the Internet and, therefore, require an Internet-connected device (e.g., smartphone 105 and/or other computing devices shown in FIG. 1 as described above) that has both Bluetooth capability and the ability to transmit data to a central database located on the Internet, acting as a “middleman”. Data from every In-Shoe Device running anywhere in the world (represented by the various “Medical Facility” icons in FIG. 11) may send data to this central database via an Internet-Connected Device, providing the ability to process and analyze all sensor data that has been collected as a whole, or in various desired segments (only certain facilities, doctors, patients, etc.).

FIG. 12 illustrates example electrical components in system 10 (also see FIG. 1, FIG. 7, etc.) that may be embedded in and/or otherwise coupled to instrumented semi-rigid orthotic 200 (FIG. 2). In some embodiments, as shown in FIG. 12, a PCB may include and/or be operatively coupled to an IMU Sensor such as the BOSCH BMI323 sensor, a Bluetooth module such as the Laird 453-00091C module, serial flash components such as a SST25VF512A microchip, a coin cell power source such as a CR2032, a light emitting diode (LED) that provides a status indicator for the PCB, and/or other components. In some embodiments the power source may include a self-charging battery, for example, and/or other power sources. In some embodiments, the self-charging battery may include and/or be associated with one or more piezoelectric components configured to convert mechanical energy into electrical energy that can be used to charge the battery. The piezoelectric component may be included in and/or coupled to instrumented semi-rigid orthotic 200 such that as instrumented semi-rigid orthotic 200 moves (bends, flexes, rotates, etc.), so does the piezoelectric component. The piezoelectric component converts the mechanical energy from the motion into electrical energy used to recharge the battery. In some embodiments, instrumented semi-rigid orthotic 200 comprises one or more components configured to facilitate wireless charging, and/or other technologies that become more and more common to such devices.

Various embodiments of the present systems and methods are disclosed in the subsequent list of numbered clauses. In the following, further features, characteristics, and exemplary technical solutions of the present disclosure will be described in terms of clauses that may be optionally claimed in any combination:

1. An instrumented semi-rigid orthotic adjustment system comprising: a) a semi-rigid orthotic of a foot-conforming shape having at least a portion that is semi-flexible during movement of a user, the orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes; b) an electronic system embedded in the semi-rigid orthotic comprising: one or more sensors in one or more locations on the semi-rigid orthotic configured to generate output signals conveying information related to a position, movement, and orientation of different regions in the semi-flexible portion; and a CPU and memory in electronic communication with the one or more sensors where the memory comprises a program that reads sensor data in the output signals, and the CPU is configured to (1) process the sensor data to determine a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a direction, magnitude, and timing, of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at different points during the movement of the user, and (2) compare the sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; and c) a piezoelectric transducer for altering an adjustable device of the semi-rigid orthotic to conform the semi-rigid orthotic more closely to the foot of the user based on the sensor data.2. The instrumented semi-rigid orthotic adjustment system of clause 1, wherein altering the adjustable device of the semi-rigid orthotic changes an alignment of the semi-rigid orthotic to the foot of the user, and is automated based on a signal from the one or more sensors and/or the CPU such that user input is not required for an adjustment.3. The instrumented semi-rigid orthotic adjustment system of any of the previous clauses, wherein the adjustable device can be used with modular components to adjust the semi-rigid orthotic.4. An instrumented semi-rigid orthotic adjustment system comprising: a) a semi-rigid orthotic of a foot-conforming shape having at least a portion that is semi-flexible during movement of a user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes; b) an electronic system embedded in the semi-rigid orthotic comprising: one or more sensors in one or more locations on the semi-rigid orthotic configured to generate output signals conveying information related to a position, movement, and orientation of different regions in the semi-flexible portion; and a CPU and memory in electronic communication with the one or more sensors where the memory comprises a program that reads sensor data in the output signals, and the CPU is configured to (1) pre-process the sensor data to determine a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a direction, magnitude, and timing, of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at different points during the movement or the gait cycle of the user, and (2) compare the sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; and c) modular components in the semi-rigid orthotic configured to tilt or elevate the semi-rigid orthotic in different increments based on the sensor data.5. The instrumented semi-rigid orthotic adjustment system of any of the previous clauses, wherein the modular components elevate a heel of the foot in different increments.6. The instrumented semi-rigid orthotic adjustment system of any of the previous clauses, wherein the modular components comprise geometries of different lengths, widths, and thicknesses.7. The instrumented semi-rigid orthotic adjustment system of any of the previous clauses, wherein the modular components change timing of motion of a heel and a midfoot of the user.8. The instrumented semi-rigid orthotic adjustment system of any of the previous clauses, wherein the modular components can be used with an adjustable device to adjust the semi-rigid orthotic.9. The instrumented semi-rigid orthotic adjustment system of any of the previous clauses, wherein the modular components tilt the heel in a valgas manner.10. The instrumented semi-rigid orthotic adjustment system of any of the previous clauses, wherein the modular components tilt the heel in a varus manner.11. An instrumented semi-rigid orthotic system comprising: a) a semi-rigid orthotic of a foot-conforming shape having at least a portion that is semi-flexible during movement of a user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement or the gait cycle, the orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes; and b) an electronic system coupled to the semi-rigid orthotic comprising: one or more sensors in one or more locations on the semi-rigid orthotic configured to generate output signals conveying information related to a position, movement, and orientation of different regions in the semi-flexible portion; detectors on the semi-rigid orthotic that measure temperature, body weight, heart rate, and timing of motion; and a CPU and memory in electronic communication with the one or more sensors and detectors, wherein the memory comprises a program that reads sensor data and detector data in the output signals, and the CPU is configured to (1) process the sensor data to determine a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a direction, magnitude, and timing, of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at different points during the movement of the user, and (2) compare the sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; and (3) compare the detector data to previously taken detector data.12. The instrumented semi-rigid orthotic system of any of the previous clauses, wherein a temperature detector measures a real-time body temperature of the user.13. The instrumented semi-rigid orthotic system of any of the previous clauses, wherein a body weight detector measures a body weight of the user, which is stored to compare to previous body weight data.14. The instrumented semi-rigid orthotic system of any of the previous clauses, wherein a heart rate detector measures a real-time heart rate of the user.15. The instrumented semi-rigid orthotic system of any of the previous clauses, wherein a timing of motion detector measures a timing of motion between a heel and a midfoot of the foot of the user.16. The instrumented semi-rigid orthotic system of any of the previous clauses, wherein data collected by the detectors on the orthotic are used to optimize the instrumented semi-rigid orthotic system.17. An instrumented semi-rigid orthotic evaluation and display system comprising one or more processors configured by machine-readable instructions to: a) receive output signals from an electronic system coupled to a semi-rigid orthotic worn by a user, the electronic system comprising one or more sensors in one or more locations on the semi-rigid orthotic configured to generate the output signals, the output signals conveying information related to position, movement, and orientation of a different regions of the orthotic, the semi-rigid orthotic having a foot-conforming shape and having at least a portion that is semi-flexible during movement or a gait cycle of the user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement or the gait cycle, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes; b) determine, based on the information in the output signals, a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a timing, direction, and degree of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at multiple points during movement of the user; c) compare sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; and d) stream and display, in real-time, a comparison of the sensor data from the given sensor to the data from other sensors on the semi-rigid orthotic, to the previously taken data, and/or to the motion of the shoe, wherein the one or more processors are part of one or more of the semi-rigid orthotic, a phone, a watch, a tablet, or a computer.18. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, wherein the data is displayed on a smartphone.19. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, wherein the display includes avatar training of the user.20. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, wherein the display includes feedback and coaching comprising motion adjustments, post op, and/or post injury rehab.21. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, further configured by the machine-readable instructions to determine, based on the comparison, that the shoe of the user should be replaced.22. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, further configured by the machine-readable instructions to determine, based on the comparison, that the motion of the user indicates a neurological disorder or depression in the user.23. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, further configured by the machine-readable instructions to use one or more pattern recognition algorithms, and time normalized data from the one or more sensors, to detect variation in graphical depictions of movement cycle shapes.24. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, wherein the electronic system is used with and/or built into a shoe, a boot, a sandal, a high heel shoe, or other footwear, scales, floors, and/or equipment associated with user standing and/or movement, medical equipment configured for testing industrial function, work out equipment, and/or sports equipment.25. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, wherein the electronic system is configured to generate or receive a timing pulse configured to facilitate coordination with other technologies.26. The instrumented semi-rigid orthotic evaluation and display system of any of the previous clauses, wherein data from the electronic system is configured to be used to help animators generate realistic CGI movement patterns for characters generated with GGI.27. A data generation system, comprising: one or more sensors configured to be located at or near one or more locations on a foot of a user, the one or more sensors configured to generate output signals conveying information related to position, movement, and orientation of one or more different regions of the foot, the one or more sensors configured to be removably inserted into a shoe of the user with the foot, the foot configured to temporarily flex, bend, and/or rotate in sagittal, frontal, and transverse planes during movement throughout a movement cycle; and one or more processors configured to determine, based on the information in the output signals, a pattern of motion of the foot, the pattern of motion comprising a timing, direction, and degree of flexing, bending, and/or rotating of the different regions of the foot at multiple points during movement throughout the gait cycle of the user, wherein the pattern of motion of the foot is configured to be used to generate animation, motion tracking, motion analysis, and/or movement related classification, that accurately depicts positions and/or movements of the user's foot, ankle, knee, leg, and/or hip.28. The system of any of the previous clauses, wherein animation comprises computer generated imagery (CGI).29. The system of any of the previous clauses, wherein data from the one or more sensors and/or the one or more processors is configured to be used to help animators generate realistic CGI movement patterns for characters generated with GGI.30. The system of any of the previous clauses, wherein the one or more sensors comprise one or more accelerometers, gyroscopes, magnetometers, strain gauges, force transducers, pressure transducers, temperature sensors, weight sensors, timing sensors, and/or location sensors.31. The system of any of the previous clauses, wherein the one or more processors are configured to use one or more pattern recognition algorithms, and time normalized data from the one or more sensors, to determine the pattern of motion.32. The system of any of the previous clauses, wherein the one or more sensors are configured to be directly coupled to the foot of the user at or near the one or more locations.33. The system of any of the previous clauses, further comprising a foot covering configured to be worn on the foot of the user, the foot covering configured to carry the one or more sensors and locate the one or more sensors at or near the one or more locations on the foot of a user.34. The system of any of the previous clauses, wherein the foot covering comprises a sock, a strap, multiple straps, and/or a strap system.35. The system of any of the previous clauses, wherein the foot covering comprises a sock with the strap, the multiple straps, and/or strap system integrally incorporated into the sock.36. The system of any of the previous clauses, wherein the foot covering comprises the strap, the multiple straps, and/or the strap system, and wherein the strap, the multiple straps, and/or strap system is configured for mechanical control of the foot.37. The system of any of the previous clauses, wherein the foot covering comprises the strap, the multiple straps, and/or the strap system, and wherein the strap, the multiple straps, and/or strap system is not configured for mechanical control of the foot.38. The system of any of the previous clauses, wherein the foot covering comprises the strap, the strap configured to be coupled to the foot at a first end on a side of an arch of the foot, wrap under the arch and over a top of the foot so that a second end of the strap is configured to be coupled to the foot and/or the first end on the side of the arch.39. The system of any of the previous clauses, wherein the foot covering comprises the multiple straps, and/or the strap system, the, the multiple straps and/or the strap system comprising a first strap configured to be wrapped around an arch of the foot, and a second strap configured to wrap around a heel of the foot.40. The system of any of the previous clauses, further comprising a Bluetooth low energy transmitter having an antennae.41. An instrumented artificial intelligence (AI) driven motion tracking and alignment system usable in various applications, the system comprising: a semi-rigid orthotic, the semi-rigid orthotic configured to be worn by a user, the semi-rigid orthotic having a foot-conforming shape and having at least a portion that is semi-flexible during movement of the user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement or the gait cycle, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes during movement throughout a movement cycle of the user; one or more sensors coupled to the semi-rigid orthotic near one or more locations on the foot of a user, the one or more sensors configured to generate output signals conveying information related to position, movement, and orientation of one or more different regions of semi-rigid orthotic, the one or more sensors configured to be removably inserted into the shoe of the user with the semi-rigid orthotic and the foot; and one or more processors operatively coupled to the one or more sensors and the semi-rigid orthotic, the one or more processors configured to determine, based on the information in the output signals, a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a timing, direction, and degree of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at multiple points during movement throughout the movement cycle of the user; wherein the pattern of motion: is time normalized by the one or more processors for a plurality of movement cycles; comprises a rich three dimensional (frontal, sagittal, and transverse) data set; and is usable by AI based systems to make predictions, and/or to generate a predictive model configured to make predictions, for users.42. The system of any of the previous clauses, wherein the pattern of motion is indicative of pronation and/or supination, if present, in the user.43. The system of any of the previous clauses, wherein the AI based systems comprise machine learning models including generative models, large language models, and/or neural networks.44. The system of any of the previous clauses, wherein the AI based systems are configured to predict user injury, athletic ability, an optimal piece of equipment, a setting for such equipment, stability in senior citizens, and/or a type of sport or work that is best for a user; and/or classify a user as higher risk for injury, lower risk for injury.45. The system of any of the previous clauses, wherein the movement cycle of the user comprises a cycled event, task, or test associated with human motion that results in a rating, score, or classification.46. The system of any of the previous clauses, wherein the pattern of motion is further useable by the AI systems to generate realistic animation, motion tracking, motion analysis, and/or movement related classification, that accurately depicts positions and/or movements of the user's foot, ankle, knee, leg, and/or hip.47. The system of any of the previous clauses, wherein animation comprises computer generated imagery (CGI).48. The system of any of the previous clauses, wherein the one or more sensors comprise one or more accelerometers, gyroscopes, magnetometers, strain gauges, force transducers, temperature sensors, weight sensors, timing sensors, pressure transducers, and/or location sensors.49. The system of any of the previous clauses, wherein the one or more processors are configured to use one or more AI pattern recognition algorithms, and time normalized data from the one or more sensors, to determine the pattern of motion.50. The system of any of the previous clauses, further comprising a Bluetooth low energy transmitter having an antennae configured to transmit data associated with the pattern of motion to another computing device.51. The system of any of the previous clauses, wherein the other computing device is configured to use one or more AI pattern recognition algorithms, and time normalized data from the one or more sensors, to detect variation in graphical depictions of movement cycle shapes.52. The system of any of the previous clauses, wherein the one or more processors are configured to generate or receive a timing pulse configured to facilitate coordination with other technologies.53. The system of any of the previous clauses, further comprising a self-charging power source configured to power the one or more sensors and/or the one or more processors.54. The system of any of the previous clauses, wherein the self-charging power source comprises a self-charging battery.55. The system of any of the previous clauses, wherein the self-charging battery includes and/or is operatively associated with one or more piezoelectric components configured to convert mechanical energy into electrical energy that can be used to charge the self-charging battery.

The reader should appreciate that the present application describes several inventions. Rather than separating those inventions into multiple isolated patent applications, applicants have grouped these inventions into a single document because their related subject matter lends itself to economies in the application process. But the distinct advantages and aspects of such inventions should not be conflated. In some cases, embodiments address all of the deficiencies noted herein, but it should be understood that the inventions are independently useful, and some embodiments address only a subset of such problems or offer other, unmentioned benefits that will be apparent to those of skill in the art reviewing the present disclosure. Due to costs constraints, some inventions disclosed herein may not be presently claimed and may be claimed in later filings, such as continuation applications or by amending the present claims. Similarly, due to space constraints, neither the Abstract nor the Summary sections of the present document should be taken as containing a comprehensive listing of all such inventions or all aspects of such inventions.

It should be understood that the description and the drawings are not intended to limit the invention to the particular form disclosed, but to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. Further modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art in view of this description. Accordingly, this description and the drawings are to be construed as illustrative only and are for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It is to be understood that the forms of the invention shown and described herein are to be taken as examples of embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed or omitted, and certain features of the invention may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description of the invention. Changes may be made in the elements described herein without departing from the spirit and scope of the invention as described in the following claims. Headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). The words “include”, “including”, and “includes” and the like mean including, but not limited to. As used throughout this application, the singular forms “a.” “an,” and “the” include plural referents unless the content explicitly indicates otherwise. Thus, for example, reference to “an element” or “a element” includes a combination of two or more elements, notwithstanding use of other terms and phrases for one or more elements, such as “one or more.” The term “or” is, unless indicated otherwise, non-exclusive, i.e., encompassing both “and” and “or.” Terms describing conditional relationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,” “when X, Y,” and the like, encompass causal relationships in which the antecedent is a necessary causal condition, the antecedent is a sufficient causal condition, or the antecedent is a contributory causal condition of the consequent, e.g., “state X occurs upon condition Y obtaining” is generic to “X occurs solely upon Y” and “X occurs upon Y and Z.” Such conditional relationships are not limited to consequences that instantly follow the antecedent obtaining, as some consequences may be delayed, and in conditional statements, antecedents are connected to their consequents, e.g., the antecedent is relevant to the likelihood of the consequent occurring. Statements in which a plurality of attributes or functions are mapped to a plurality of objects (e.g., one or more processors performing steps A, B, C, and D) encompasses both all such attributes or functions being mapped to all such objects and subsets of the attributes or functions being mapped to subsets of the attributes or functions (e.g., both all processors each performing steps A-D. and a case in which processor 1 performs step A, processor 2 performs step B and part of step C, and processor 3 performs part of step C and step D), unless otherwise indicated. Further, unless otherwise indicated, statements that one value or action is “based on” another condition or value encompass both instances in which the condition or value is the sole factor and instances in which the condition or value is one factor among a plurality of factors. Unless otherwise indicated, statements that “each” instance of some collection have some property should not be read to exclude cases where some otherwise identical or similar members of a larger collection do not have the property, i.e., each does not necessarily mean each and every. Limitations as to sequence of recited steps should not be read into the claims unless explicitly specified, e.g., with explicit language like “after performing X. performing Y,” in contrast to statements that might be improperly argued to imply sequence limitations, like “performing X on items, performing Y on the X′ed items,” used for purposes of making claims more readable rather than specifying sequence. Statements referring to “at least Z of A, B, and C,” and the like (e.g., “at least Z of A, B, or C”), refer to at least Z of the listed categories (A, B, and C) and do not require at least Z units in each category. Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic processing/computing device.

One or more embodiments and/or portions of an embodiment illustrated in the figures and/or described above may be combined with any other embodiment. For example, a piezoelectric transducer that is shown as part of an adjustable semi-rigid orthotic in FIG. 9 may also be included with the various electronic and/or sensors shown in FIG. 2, FIG. 4, FIG. 7, FIG. 12, etc. Other examples of combined embodiments are contemplated.

Claims

1. An instrumented semi-rigid orthotic adjustment system comprising: a) a semi-rigid orthotic of a foot-conforming shape having at least a portion that is semi-flexible during movement of a user, the orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes;b) an electronic system embedded in the semi-rigid orthotic comprising: one or more sensors in one or more locations on the semi-rigid orthotic configured to generate output signals conveying information related to a position, movement, and orientation of different regions in the semi-flexible portion; anda CPU and memory in electronic communication with the one or more sensors where the memory comprises a program that reads sensor data in the output signals, and the CPU is configured to (1) process the sensor data to determine a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a direction, magnitude, and timing, of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at different points during the movement of the user, and (2) compare the sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; andc) a piezoelectric transducer for altering an adjustable device of the semi-rigid orthotic to conform the semi-rigid orthotic more closely to the foot of the user based on the sensor data.

2. The instrumented semi-rigid orthotic adjustment system of claim 1, wherein altering the adjustable device of the semi-rigid orthotic changes an alignment of the semi-rigid orthotic to the foot of the user, and is automated based on a signal from the one or more sensors and/or the CPU such that user input is not required for an adjustment.

3. The instrumented semi-rigid orthotic adjustment system of claim 1, wherein the adjustable device can be used with modular components to adjust the semi-rigid orthotic.

4. An instrumented semi-rigid orthotic adjustment system comprising: a) a semi-rigid orthotic of a foot-conforming shape having at least a portion that is semi-flexible during movement of a user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes;b) an electronic system embedded in the semi-rigid orthotic comprising: one or more sensors in one or more locations on the semi-rigid orthotic configured to generate output signals conveying information related to a position, movement, and orientation of different regions in the semi-flexible portion; anda CPU and memory in electronic communication with the one or more sensors where the memory comprises a program that reads sensor data in the output signals, and the CPU is configured to (1) pre-process the sensor data to determine a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a direction, magnitude, and timing, of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at different points during the movement or the gait cycle of the user, and (2) compare the sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; andc) modular components in the semi-rigid orthotic configured to tilt or elevate the semi-rigid orthotic in different increments based on the sensor data.

5. The instrumented semi-rigid orthotic adjustment system of claim 4, wherein the modular components elevate a heel of the foot in different increments.

6. The instrumented semi-rigid orthotic adjustment system of claim 4, wherein the modular components comprise geometries with different lengths, widths, and thicknesses.

7. The instrumented semi-rigid orthotic adjustment system of claim 4, wherein the modular components change timing of motion of a heel and a midfoot of the user.

8. The instrumented semi-rigid orthotic adjustment system of claim 4, wherein the modular components can be used with an adjustable device to adjust the semi-rigid orthotic.

9. The instrumented semi-rigid orthotic adjustment system of claim 4, wherein the modular components tilt a heel of the semi-rigid orthotic in a valgas manner.

10. The instrumented semi-rigid orthotic adjustment system of claim 4, wherein the modular components tilt a heel of the semi-rigid orthotic in a varus manner.

11. An instrumented semi-rigid orthotic system comprising: a) a semi-rigid orthotic of a foot-conforming shape having at least a portion that is semi-flexible during movement of a user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement or the gait cycle, the orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes; andb) an electronic system coupled to the semi-rigid orthotic comprising: one or more sensors in one or more locations on the semi-rigid orthotic configured to generate output signals conveying information related to a position, movement, and orientation of different regions in the semi-flexible portion;detectors on the semi-rigid orthotic that measure temperature, body weight, heart rate, and timing of motion; anda CPU and memory in electronic communication with the one or more sensors and detectors, wherein the memory comprises a program that reads sensor data and detector data in the output signals, and the CPU is configured to (1) process the sensor data to determine a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a direction, magnitude, and timing, of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at different points during the movement of the user, and (2) compare the sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; and (3) compare the detector data to previously taken detector data.

12. The instrumented semi-rigid orthotic system of claim 11, wherein a temperature detector measures a real-time body temperature of the user.

13. The instrumented semi-rigid orthotic system of claim 11, wherein a body weight detector measures a body weight of the user, which is stored to compare to previous body weight data.

14. The instrumented semi-rigid orthotic system of claim 11, wherein a heart rate detector measures a real-time heart rate of the user.

15. The instrumented semi-rigid orthotic system of claim 11, wherein a timing of motion detector measures a timing of motion between a heel and a midfoot of the foot of the user.

16. The instrumented semi-rigid orthotic system of claim 11, wherein data collected by the detectors on the orthotic are used to optimize the instrumented semi-rigid orthotic system.

17. An instrumented semi-rigid orthotic evaluation and display system comprising one or more processors configured by machine-readable instructions to: a) receive output signals from an electronic system coupled to a semi-rigid orthotic worn by a user, the electronic system comprising one or more sensors in one or more locations on the semi-rigid orthotic configured to generate the output signals, the output signals conveying information related to position, movement, and orientation of a different regions of the orthotic, the semi-rigid orthotic having a foot-conforming shape and having at least a portion that is semi-flexible during movement or a gait cycle of the user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement or the gait cycle, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes;b) determine, based on the information in the output signals, a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a timing, direction, and degree of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at multiple points during movement of the user;c) compare sensor data from a given sensor to data from other sensors on the semi-rigid orthotic, to previously taken data, and/or to motion of the shoe; andd) stream and display, in real-time, a comparison of the sensor data from the given sensor to the data from other sensors on the semi-rigid orthotic, to the previously taken data, and/or to the motion of the shoe, wherein the one or more processors are part of one or more of the semi-rigid orthotic, a phone, a watch, a tablet, or a computer.

18. The instrumented semi-rigid orthotic evaluation and display system of claim 17, wherein the data is displayed on a smartphone.

19. The instrumented semi-rigid orthotic evaluation and display system of claim 17, wherein the display includes avatar training of the user.

20. The instrumented semi-rigid orthotic evaluation and display system of claim 17, wherein the display includes feedback and coaching comprising motion adjustments, post op, and/or post injury rehab.

21. The instrumented semi-rigid orthotic evaluation and display system of claim 17, further configured by the machine-readable instructions to determine, based on the comparison, that the shoe of the user should be replaced.

22. The instrumented semi-rigid orthotic evaluation and display system of claim 17, further configured by the machine-readable instructions to determine, based on the comparison, that the motion of the user indicates a neurological disorder or depression in the user.

23. The instrumented semi-rigid orthotic evaluation and display system of claim 17, further configured by the machine-readable instructions to use one or more pattern recognition algorithms, and time normalized data from the one or more sensors, to detect variation in graphical depictions of movement cycle shapes.

24. The instrumented semi-rigid orthotic evaluation and display system of claim 17, wherein the electronic system is used with and/or built into a shoe, a boot, a sandal, a high heel shoe, or other footwear, scales, floors, equipment associated with user standing and/or movement, medical equipment configured for testing industrial function, work out equipment, and/or sports equipment.

25. The instrumented semi-rigid orthotic evaluation and display system of claim 17, wherein the electronic system is configured to generate or receive a timing pulse configured to facilitate coordination with other technologies.

26. The instrumented semi-rigid orthotic evaluation and display system of claim 17, wherein data from the electronic system is configured to be used to help animators generate realistic CGI movement patterns for characters generated with GGI.

27. A data generation system, comprising: one or more sensors configured to be located near one or more locations on a foot of a user, the one or more sensors configured to generate output signals conveying information related to position, movement, and orientation of one or more different regions of the foot, the one or more sensors configured to be removably inserted into a shoe of the user with the foot, the foot configured to temporarily flex, bend, and/or rotate in sagittal, frontal, and transverse planes during movement throughout a movement cycle; andone or more processors configured to determine, based on the information in the output signals, a pattern of motion of the foot, the pattern of motion comprising a timing, direction, and degree of flexing, bending, and/or rotating of the different regions of the foot at multiple points during movement throughout the gait cycle of the user,wherein the pattern of motion of the foot is configured to be used to generate animation, motion tracking, motion analysis, and/or movement related classification, that accurately depicts positions and/or movements of the user's foot, ankle, knee, leg, and/or hip.

28. The system of claim 27, wherein animation comprises computer generated imagery (CGI).

29. The system of claim 28, wherein data from the one or more sensors and/or the one or more processors is configured to be used to help animators generate realistic CGI movement patterns for characters generated with GGI.

30. The system of claim 27, wherein the one or more sensors comprise one or more accelerometers, gyroscopes, magnetometers, strain gauges, force transducers, temperature sensors, weight sensors, timing sensors, pressure transducers, and/or location sensors.

31. The system of claim 27, wherein the one or more processors are configured to use one or more pattern recognition algorithms, and time normalized data from the one or more sensors, to determine the pattern of motion.

32. The system of claim 27, wherein the one or more sensors are configured to be directly coupled to the foot of the user at or near the one or more locations.

33. The system of claim 27, further comprising a foot covering configured to be worn on the foot of the user, the foot covering configured to carry the one or more sensors and locate the one or more sensors at or near the one or more locations on the foot of a user.

34. The system of claim 32, wherein the foot covering comprises a sock, a strap, multiple straps, and/or a strap system.

35. The system of claim 34, wherein the foot covering comprises a sock with the strap, multiple straps, and/or strap system integrally incorporated into the sock.

36. The system of claim 34, wherein the foot covering comprises the strap, the multiple straps, and/or the strap system, and wherein the strap, the multiple straps, and/or strap system is configured for mechanical control of the foot.

37. The system of claim 34, wherein the foot covering comprises the strap, the multiple straps, and/or the strap system, and wherein the strap, the multiple straps, and/or strap system is not configured for mechanical control of the foot.

38. The system of claim 34, wherein the foot covering comprises the strap, the strap configured to be coupled to the foot at a first end on a side of an arch of the foot, wrap under the arch and over a top of the foot so that a second end of the strap is configured to be coupled to the foot and/or the first end on the side of the arch.

39. The system of claim 34, wherein the foot covering comprises the strap system, or the multiple straps, the strap system or the multiple straps comprising a first strap configured to be wrapped around an arch of the foot, and a second strap configured to wrap around a heel of the foot.

40. The system of claim 27, further comprising a Bluetooth low energy transmitter having an antennae.

41. An instrumented artificial intelligence (AI) driven motion tracking and alignment system usable in various applications, the system comprising: a semi-rigid orthotic, the semi-rigid orthotic configured to be worn by a user, the semi-rigid orthotic having a foot-conforming shape and having at least a portion that is semi-flexible during movement of the user, the semi-rigid orthotic configured to be removably inserted into a shoe of the user such that there is a freedom of motion between the semi-rigid orthotic and the shoe, the semi-rigid orthotic configured to temporarily bend under load to control, restrict, or reduce motion of a foot of the user during the movement or the gait cycle, the semi-rigid orthotic configured for complex flexing, bending, and/or rotating in sagittal, frontal, and transverse planes during movement throughout a movement cycle of the user;one or more sensors coupled to the semi-rigid orthotic near one or more locations on the foot of a user, the one or more sensors configured to generate output signals conveying information related to position, movement, and orientation of one or more different regions of semi-rigid orthotic, the one or more sensors configured to be removably inserted into the shoe of the user with the semi-rigid orthotic and the foot; andone or more processors operatively coupled to the one or more sensors and the semi-rigid orthotic, the one or more processors configured to determine, based on the information in the output signals, a pattern of motion of the semi-rigid orthotic, the pattern of motion comprising a timing, direction, and degree of flexing, bending, and/or rotating of the different regions of the semi-rigid orthotic at multiple points during movement throughout the movement cycle of the user;wherein the pattern of motion: is time normalized by the one or more processors for a plurality of movement cycles;comprises a rich three dimensional (frontal, sagittal, and transverse) data set; andis usable by AI based systems to make predictions, and/or to generate a predictive model configured to make predictions, for users.

42. The system of claim 41, wherein the pattern of motion is indicative of pronation and/or supination, if present, in the user.

43. The system of claim 41, wherein the AI based systems comprise machine learning models including generative models, large language models, and/or neural networks.

44. The system of claim 41, wherein the AI based systems are configured to predict user injury, athletic ability, an optimal piece of equipment, a setting for such equipment, stability in senior citizens, and/or a type of sport or work that is best for a user; and/or classify a user as higher risk for injury, lower risk for injury.

45. The system of claim 41, wherein the movement cycle of the user comprises a cycled event, task, or test associated with human motion that results in a rating, score, or classification.

46. The system of claim 41, wherein the pattern of motion is further useable by the AI systems to generate realistic animation, motion tracking, motion analysis, and/or movement related classification, that accurately depicts positions and/or movements of the user's foot, ankle, knee, leg, and/or hip.

47. The system of claim 46, wherein animation comprises computer generated imagery (CGI).

48. The system of claim 41, wherein the one or more sensors comprise one or more accelerometers, gyroscopes, magnetometers, strain gauges, force transducers, temperature sensors, weight sensors, timing sensors, pressure transducers, and/or location sensors.

49. The system of claim 41, wherein the one or more processors are configured to use one or more AI pattern recognition algorithms, and time normalized data from the one or more sensors, to determine the pattern of motion.

50. The system of claim 41, further comprising a Bluetooth low energy transmitter having an antennae configured to transmit data associated with the pattern of motion to another computing device.

51. The system of claim 50, wherein the other computing device is configured to use one or more AI pattern recognition algorithms, and time normalized data from the one or more sensors, to detect variation in graphical depictions of movement cycle shapes.

52. The system of claim 41, wherein the one or more processors are configured to generate or receive a timing pulse configured to facilitate coordination with other technologies.

53. The system of claim 41, further comprising a self-charging power source configured to power the one or more sensors and/or the one or more processors.

54. The system of claim 53, wherein the self-charging power source comprises a self-charging battery.

55. The system of claim 54, wherein the self-charging battery includes and/or is operatively associated with one or more piezoelectric components configured to convert mechanical energy into electrical energy that can be used to charge the self-charging battery.