Patent Application
20210308528
Generally, the present disclosure relates to the field of apparatus for applying pushing or pulling force. More specifically, the present disclosure relates to a weight-machine apparatus for facilitating voice-controllable resistances for strength training.
Gym equipment is categorically classified into Cardiovascular Machines, Resistance Machines, and Free Weight Machines. Most of these machines, particularly in the Resistance and Free Weight Machines rely on weight stack (or stackable weights) to achieve strength training. Further, the gym equipment has gained popularity for easy setup at home/office or virtually anywhere and promises a great workout experience. But instead of stack-up weights, this equipment typically relies upon durable resistance bands to simulate the force of weight stack.
Existing weight-machine apparatuses are deficient with regard to several aspects. For instance, current weight-machine apparatuses do not provide programmable resistance for strength training. Furthermore, current weight-machine apparatuses do not provide programmable pull resistance and counter-pull resistance the strength training. Moreover, current weight-machine apparatuses do not provide voice-controlled resistance for the strength training.
Therefore, there is a need for an improved weight-machine apparatus for facilitating voice-controllable resistances for strength training that may overcome one or more of the above-mentioned problems and/or limitations.
This summary is provided to introduce a selection of concepts in a simplified form, that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this summary intended to be used to limit the claimed subject matter's scope.
Disclosed herein is a weight-machine apparatus for facilitating voice-controllable resistances for strength training, in accordance with some embodiments. Accordingly, the weight-machine apparatus may include an electromechanical assembly, at least one voice sensor, a processing device, a storage device, and a power source. Further, the electromechanical assembly may include an electrically powered force-generating component and a force-receiving component. Further, the electrically powered force-generating component may be configured for generating at least one of a linear force and a rotatory force based on a command. Further, the force-receiving component may be coupled with the electrically powered force-generating component using a force transmission component. Further, the force-receiving component may be configured for receiving at least one of the linear force and the rotatory force. Further, the force-receiving component may be associated with an inertial value. Further, the inertial value may be based on at least one of the linear force and the rotatory force. Further, the force-receiving component may be configured for presenting the inertial value corresponding to at least one of the linear force and the rotatory force. Further, the at least one voice sensor may be configured for detecting at least one voice sample. Further, the at least one voice sensor may be configured for generating voice sensor data based on the detecting. Further, the processing device may be communicatively coupled with the at least one voice sensor and the electrically powered force-generating component. Further, the processing device may be configured for analyzing the voice sensor data. Further, the processing device may be configured for generating the command based on the analyzing. Further, the storage device may be communicatively coupled with the processing device. Further, the storage device may be configured for storing the voice sensor data and the command associated with the voice sensor data. Further, the power source may be electrically coupled with the electrically powered force-generating component, the processing device, the at least one voice sensor, and the storage device. Further, the power source may be configured for electrically powering the electrically powered force-generating component, the processing device, the at least one voice sensor, and the storage device.
Further disclosed herein is a weight-machine apparatus for facilitating voice-controllable resistances for strength training, in accordance with some embodiments. Accordingly, the weight-machine apparatus may include an electromechanical assembly, at least one voice sensor, a processing device, a storage device, and a power source. Further, the electromechanical assembly may include a geared electric motor and a handle. Further, the geared electric motor may be configured for generating at least one of a linear force and a rotatory force based on a command. Further, the handle may be coupled with the geared electric motor using a cable. Further, the handle may be configured for receiving at least one of the linear force and the rotatory force. Further, the handle may be associated with an inertial value. Further, the inertial value may be based on at least one of the linear force and the rotatory force. Further, the handle may be configured for presenting the inertial value corresponding to at least one of the linear force and the rotatory force. Further, the at least one voice sensor may be configured for detecting at least one voice sample. Further, the at least one voice sensor may be configured for generating voice sensor data based on the detecting. Further, the processing device may be communicatively coupled with the at least one voice sensor and the geared electric motor. Further, the processing device may be configured for analyzing the voice sensor data. Further, the processing device may be configured for generating the command based on the analyzing. Further, the storage device may be communicatively coupled with the processing device. Further, the storage device may be configured for storing the voice sensor data and the command associated with the voice sensor data. Further, the power source may be electrically coupled with the geared electric motor, the processing device, the at least one voice sensor, and the storage device. Further, the power source may be configured for electrically powering the geared motor, the processing device, the at least one voice sensor, and the storage device.
Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the applicants. The applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure.
As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.
Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim limitation found herein and/or issuing here from that does not explicitly appear in the claim itself.
Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive.
Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present disclosure. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.
Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein—as understood by the ordinary artisan based on the contextual use of such term—differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.
Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”
The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the claims found herein and/or issuing here from. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.
The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of a weight-machine apparatus for facilitating voice-controllable resistances for strength training, embodiments of the present disclosure are not limited to use only in this context.
In general, the method disclosed herein may be performed by one or more computing devices. For example, in some embodiments, the method may be performed by a server computer in communication with one or more client devices over a communication network such as, for example, the Internet. In some other embodiments, the method may be performed by one or more of at least one server computer, at least one client device, at least one network device, at least one sensor, and at least one actuator. Examples of the one or more client devices and/or the server computer may include, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a portable electronic device, a wearable computer, a smartphone, an Internet of Things (IoT) device, a smart electrical appliance, a video game console, a rack server, a super-computer, a mainframe computer, mini-computer, micro-computer, a storage server, an application server (e.g. a mail server, a web server, a real-time communication server, an FTP server, a virtual server, a proxy server, a DNS server, etc.), a quantum computer, and so on. Further, one or more client devices and/or the server computer may be configured for executing a software application such as, for example, but not limited to, an operating system (e.g. Windows, Mac OS, Unix, Linux, Android, etc.) in order to provide a user interface (e.g. GUI, touch-screen based interface, voice-based interface, gesture-based interface, etc.) for use by the one or more users and/or a network interface for communicating with other devices over a communication network. Accordingly, the server computer may include a processing device configured for performing data processing tasks such as, for example, but not limited to, analyzing, identifying, determining, generating, transforming, calculating, computing, compressing, decompressing, encrypting, decrypting, scrambling, splitting, merging, interpolating, extrapolating, redacting, anonymizing, encoding and decoding. Further, the server computer may include a communication device configured for communicating with one or more external devices. The one or more external devices may include, for example, but are not limited to, a client device, a third-party database, public database, a private database and so on. Further, the communication device may be configured for communicating with the one or more external devices over one or more communication channels. Further, the one or more communication channels may include a wireless communication channel and/or a wired communication channel. Accordingly, the communication device may be configured for performing one or more of transmitting and receiving of information in electronic form. Further, the server computer may include a storage device configured for performing data storage and/or data retrieval operations. In general, the storage device may be configured for providing reliable storage of digital information. Accordingly, in some embodiments, the storage device may be based on technologies such as, but not limited to, data compression, data backup, data redundancy, deduplication, error correction, data finger-printing, role-based access control, and so on.
Further, one or more steps of the method disclosed herein may be initiated, maintained, controlled and/or terminated based on a control input received from one or more devices operated by one or more users such as, for example, but not limited to, an end-user, an admin, a service provider, a service consumer, an agent, a broker and a representative thereof. Further, the user as defined herein may refer to a human, an animal or an artificially intelligent being in any state of existence, unless stated otherwise, elsewhere in the present disclosure. Further, in some embodiments, the one or more users may be required to successfully perform authentication in order for the control input to be effective. In general, a user of the one or more users may perform authentication based on the possession of a secret human-readable secret data (e.g. username, password, passphrase, PIN, secret question, secret answer, etc.) and/or possession of a machine-readable secret data (e.g. encryption key, decryption key, bar codes, etc.) and/or possession of one or more embodied characteristics unique to the user (e.g. biometric variables such as, but not limited to, fingerprint, palm-print, voice characteristics, behavioral characteristics, facial features, iris pattern, heart rate variability, evoked potentials, brain waves, and so on) and/or possession of a unique device (e.g. a device with a unique physical and/or chemical and/or biological characteristic, a hardware device with a unique serial number, a network device with a unique IP/MAC address, a telephone with a unique phone number, a smartcard with an authentication token stored thereupon, etc.). Accordingly, the one or more steps of the method may include communicating (e.g. transmitting and/or receiving) with one or more sensor devices and/or one or more actuators in order to perform authentication. For example, the one or more steps may include receiving, using the communication device, the secret human-readable data from an input device such as, for example, a keyboard, a keypad, a touch-screen, a microphone, a camera and so on. Likewise, the one or more steps may include receiving, using the communication device, the one or more embodied characteristics from one or more biometric sensors.
Further, one or more steps of the method may be automatically initiated, maintained and/or terminated based on one or more predefined conditions. In an instance, the one or more predefined conditions may be based on one or more contextual variables. In general, the one or more contextual variables may represent a condition relevant to the performance of the one or more steps of the method. The one or more contextual variables may include, for example, but are not limited to, location, time, identity of a user associated with a device (e.g. the server computer, a client device, etc.) corresponding to the performance of the one or more steps, environmental variables (e.g. temperature, humidity, pressure, wind speed, lighting, sound, etc.) associated with a device corresponding to the performance of the one or more steps, physical state and/or physiological state and/or psychological state of the user, physical state (e.g. motion, direction of motion, orientation, speed, velocity, acceleration, trajectory, etc.) of the device corresponding to the performance of the one or more steps and/or semantic content of data associated with the one or more users. Accordingly, the one or more steps may include communicating with one or more sensors and/or one or more actuators associated with the one or more contextual variables. For example, the one or more sensors may include, but are not limited to, a timing device (e.g. a real-time clock), a location sensor (e.g. a GPS receiver, a GLONASS receiver, an indoor location sensor, etc.), a biometric sensor (e.g. a fingerprint sensor), an environmental variable sensor (e.g. temperature sensor, humidity sensor, pressure sensor, etc.) and a device state sensor (e.g. a power sensor, a voltage/current sensor, a switch-state sensor, a usage sensor, etc. associated with the device corresponding to performance of the or more steps).
Further, the one or more steps of the method may be performed one or more number of times. Additionally, the one or more steps may be performed in any order other than as exemplarily disclosed herein, unless explicitly stated otherwise, elsewhere in the present disclosure. Further, two or more steps of the one or more steps may, in some embodiments, be simultaneously performed, at least in part. Further, in some embodiments, there may be one or more time gaps between performance of any two steps of the one or more steps.
Further, in some embodiments, the one or more predefined conditions may be specified by the one or more users. Accordingly, the one or more steps may include receiving, using the communication device, the one or more predefined conditions from one or more and devices operated by the one or more users. Further, the one or more predefined conditions may be stored in the storage device. Alternatively, and/or additionally, in some embodiments, the one or more predefined conditions may be automatically determined, using the processing device, based on historical data corresponding to performance of the one or more steps. For example, the historical data may be collected, using the storage device, from a plurality of instances of performance of the method. Such historical data may include performance actions (e.g. initiating, maintaining, interrupting, terminating, etc.) of the one or more steps and/or the one or more contextual variables associated therewith. Further, machine learning may be performed on the historical data in order to determine the one or more predefined conditions. For instance, machine learning on the historical data may determine a correlation between one or more contextual variables and performance of the one or more steps of the method. Accordingly, the one or more predefined conditions may be generated, using the processing device, based on the correlation.
Further, one or more steps of the method may be performed at one or more spatial locations. For instance, the method may be performed by a plurality of devices interconnected through a communication network. Accordingly, in an example, one or more steps of the method may be performed by a server computer. Similarly, one or more steps of the method may be performed by a client computer. Likewise, one or more steps of the method may be performed by an intermediate entity such as, for example, a proxy server. For instance, one or more steps of the method may be performed in a distributed fashion across the plurality of devices in order to meet one or more objectives. For example, one objective may be to provide load balancing between two or more devices. Another objective may be to restrict a location of one or more of an input data, an output data and any intermediate data therebetween corresponding to one or more steps of the method. For example, in a client-server environment, sensitive data corresponding to a user may not be allowed to be transmitted to the server computer. Accordingly, one or more steps of the method operating on the sensitive data and/or a derivative thereof may be performed at the client device.
Overview:
The present disclosure describes a weight-machine apparatus for facilitating voice-controllable resistances for strength training. Further, the present disclosure may create a Voice-Controlled Electro-Mechanical Weight Machine. Further, the Voice-Controlled Electro-Mechanical Weight Machine may be designed with a holistic approach. Further, the present disclosure may consider users, processes and a machine as one unit to achieve inclusivity, ease of use, safety, sustainability, and maintainability.
Force in a muscle is generated, and muscle strengthening occurs, as a muscle unit (sarcomere) contracts, actin is pulled along myosin filaments until they are maximally overlapped and at their strongest. Strengthening of that muscle depends upon the resistance to the muscle unit during active contraction and depends upon the overlap of filaments, the greater the overlap, and hence greater contact between these filaments, the more powerful the pull. There is therefore a mechanical advantage gained at a certain length of a muscle, as it lengthens or shortens about a joint. The body, however, expends energy and the muscle fatigues during this process and requires recovery.
Both weight stack and resistance bands have the following advantages:
But on the other hand, they have disadvantages and limitations as well:
1) The efficiency of muscle training is maximized when a joint is positioned to a degree that allows for the muscle length to achieve maximum myosin and actin overlap, as above, where the muscle is at its strongest; in weight stack or resistance band training the muscle must work, and expend energy, through less efficient lengths of muscle, when some myosin and actin filaments are not overlapped, and hence not strengthening those parts of the sarcomere. Energy is expended during periods when the sarcomere is not at its greatest mechanical advantage, less time can, therefore, be spent training the sarcomere at its most efficient length, the muscle fatigues and energy has been wasted, less than a maximum degree of muscle training has occurred.
2) Increasing/decreasing the force is typically by big increment (2.5 lbs and above)
3) The force is not totally consistent
(The longer the stretch, the higher the force—indicated in force elongation chart)
4) Dangerous to release weights/bands when not in a resting state
5) Dangerous to add/subtract weights/bands when not in a resting state
6) Dangerous to use heavy weights/bands without a spotter/assistant
7) Very hard to keep track of the current weight especially if there are multiple weights/bands
8) The force is always the same in both directions
9) May not safe to be used by patients in physical rehabilitation centers/facilities.
Further, the present disclosure may intend to solve all these disadvantages and limitations so that anyone, anywhere, even users with physical difficulties, could benefit from weights and resistance training within safe boundaries.
Further, the present disclosure may create the Voice-Controlled Electro-Mechanical Weight Machine that may be anchored on the following:
Functionality:
Further, the present disclosure redefines the capabilities of resistance and weight machines, by adding more features and functions using the latest technologies in the field of Internet of Things (IoT), Artificial Intelligence (AI) and Electro-Mechanical Engineering.
Inclusivity:
Further, the present disclosure may be used by people who are attempting strengthening training, those with physical impairments, those who are rehabilitating an injured or otherwise imbalanced area, those undergoing physical rehabilitation within facilities or at home, those who are visually impaired (but can talk and hear), those who have hearing and speech difficulty (but can see), and all others.
Usability:
Further, the present disclosure includes a voice command module to make it easier to operate. Everything is automated, including adding/reducing force (weight) dynamically.
Safety:
At the user's command, the present disclosure may start whenever the user is ready and stops immediately when being told to.
A unique feature of the present disclosure may be that the counter-weight (or counter-force) is programmable. Hence,
Further, the present disclosure's AI module is also smart enough to do actions based on spoken words automatically (ex.: ouch, wait, and no will stop the machine immediately if this option is activated).
All its components are guaranteed to be safe up to a force of 500 pounds, and force is configurable at a 1 pound increment as needed.
Extensibility:
Further, the present disclosure is designed to be extended to accommodate special functions and use cases:
Interoperability:
Although the present disclosure is designed to be a stand-alone gym machine, the core system can be integrated (inter-operable) with existing gym machines:
The concept of Interoperability in machines is not new. In fact, many of the existing gym resistance machines share the same cable-weight construct and differ only on the form-factor of the bench/fixture where the user will position himself.
Repurposability:
Although the present disclosure is designed to be a gym machine, a re-purposed software/firmware can have the machine function as:
The concept of the Repurposability of machines is not new. In fact, there are multipurpose gym benches that can transform from being a bench press to a lat pulldown, leg workout, etc.
Data Visibility (the Most Crucial):
Currently, there are no clear measures why two persons with similar physique and health conditions using the same gym machine have progressed differently during the same weight/resistance program even when there are trainers and coaches are around.
Parameters and variables (such as the length of time and how the distance/displacement of the weight is handled by the user) during the whole session is crucial to understand the good/bad part of the program and adjust accordingly if needed.
With the right digital sensors and proper recording of data (from setting up the machine, up to the end of the training session), many possibilities are achievable:
Traceability:
The data log written by the system gives 360-view of all the variables and parameters, thus supporting the accuracy and reliability of the training/workout.
Repeatability:
Because of data logs, a user can exactly repeat his previous workout. New users can also pick successful workout programs (workout templates) and repeat the session accordingly:
For muscle/bodybuilding programs, the templates can have incremental weight adjustments in it, with the specified timing, reps, and rest data.
For a physical rehabilitation facility, having a set of workout templates for each kind of rehab workout will hasten the recovery process of patients.
For physically challenged users, having workout templates for each kind of disability will make it easier and safe for an effective and repeatable process.
Sustainability:
With all the automated features that make this machine self-aware and very user-friendly brings the level of sustainable workout higher lessening the demanding need for human intervention (help/assist).
Users being able to do proper, effective and reliable workout/training merely on their own is a huge step towards a sustainable/unmanaged system.
Maintainability:
Since the force/weight exerted by the machine is monitored in real-time, any excessive amount beyond the capacity of the hardware, sensor, and mechanical design are predicted before they happen. Corresponding audio/video warnings and alerts will prompt both users and machine personnel so that further damage to the machine is prevented.
Further, the present disclosure may involve incrementing and decrementing the force/weight. Further, the increment/decrement in the weight may be as low as 1 pound per step. Further, the weight may be adjusted dynamically (no need to pause). Further, weights may be adjusted in a plurality of modes. Further, the plurality of modes may include a slow mode and an instant mode. Further, the slow mode may involve adjusting +/−1 pound per second that may be configurable. Further, the instant mode may involve adjusting +/−new weight within 1 second.
Further, the present disclosure may involve setting a pull-force and a counterforce. Further, the machine may restrict the cable until a pull-force value is met/exceeded, in which the machine may start to release the cable to maintain the pull-force within the value. Further, the machine may retract the cable back when a user ease-up his force below a counter-force value. Further, the user may set the pull-force value different to the counter-force value. Further, the pull-force value may be 500 pounds. Further, counter-force value may be 500 pounds (for normal use). Further, the counter-force value may be 250 pounds (with difficulty in extension). Further, the counter-force value may be 0 pounds (for flexion training only).
Further, the present disclosure may involve setting a counter speed. Further, the user may set the speed of which the machine may retract the cable between 0 and 10 R/S (rotation per second) Further, the counter-speed may be 0 (no retraction). Further, the counter-speed may be 1 (slowest retraction). Further, the counter-speed may be 10 (fastest retraction).
Further, the present disclosure may involve operating the machine automatically based on the configuration set prior to start up. Further, settings and current readings may be displayed in an LCD. Further, alerts and notifications may be triggered automatically. Further, data logging may be automatic and in sync with start/stop commands.
Further, the present disclosure may involve the voice-controlled operation of the machine. Further, the machine may be equipped with a voice/speech recognition module to process voice commands for operations and control. Further, the machine may be equipped with a Bluetooth headset (speaker+mic) for remote voice control. Further, keyword-based commands may be used to differentiate operation/control commands from ambient noise. Further, the operations and control commands may be configurable.
Further, the present disclosure may involve the recording of data logs. Further, the present disclosure may log all activities with a timestamp (date, time), pull-force and counterforce values in real-time, all commands executed during the workout session, all user inputs pertinent to the session, and all notifications and alerts.
Further, the present disclosure may involve extensible (Features and Functions) such as speech recognition, gesture recognition, visual recognition and detection, human pose detection, and facial expression recognition.
Further, the present disclosure may involve external sensor connectivity. Further, external vital sign sensors may be connected to the machine (wired/wireless). Further, vital sign data may be recorded together with workout data. Further, vital sign data may trigger alerts, notifications and systems functions (lessen the weight/force, stop device, etc.).
A user 112, such as the one or more relevant parties, may access online platform 100 through a web-based software application or browser. The web-based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 1500.
Further, the electromechanical assembly 202 may include an electrically powered force-generating component and a force-receiving component. Further, the electrically powered force-generating component may be configured for generating at least one of a linear force and a rotatory force based on a command. Further, the force-receiving component may be coupled with the electrically powered force-generating component using a force transmission component. Further, the force-receiving component may be configured for receiving at least one of the linear force and the rotatory force. Further, the force-receiving component may be associated with an inertial value. Further, the inertial value may be based on at least one of the linear force and the rotatory force. Further, the force-receiving component may be configured for presenting the inertial value corresponding to at least one of the linear force and the rotatory force.
Further, the at least one voice sensor 204 may be configured for detecting at least one voice sample. Further, the at least one voice sensor 204 may be configured for generating voice sensor data based on the detecting.
Further, the processing device 206 may be communicatively coupled with the at least one voice sensor 204 and the electrically powered force-generating component. Further, the processing device 206 may be configured for analyzing the voice sensor data. Further, the processing device 206 may be configured for generating the command based on the analyzing.
Further, the storage device 208 may be communicatively coupled with the processing device 206. Further, the storage device 208 may be configured for storing the voice sensor data and the command associated with the voice sensor data.
Further, the power source 210 may be electrically coupled with the electrically powered force-generating component, the processing device 206, the at least one voice sensor 204, and the storage device 208. Further, the power source 210 may be configured for electrically powering the electrically powered force-generating component, the processing device 206, the at least one voice sensor 204, and the storage device 208.
Further, in some embodiments, the processing device 206 may be configured for generating a percentage command based on the analyzing. Further, the electrically powered force-generating component may be configured for varying at least one of the linear force and the rotatory force by a force percentage of at least one of the linear force and the rotatory force based on the percentage command.
Further, in some embodiments, the processing device 206 may be configured for generating a magnitude command based on the analyzing. Further, the electrically powered force-generating component configured for varying at least one of the linear force and the rotatory force to a fixed force magnitude of at least one of the linear force and the rotatory force based on the magnitude command.
Further, in some embodiments, the processing device 206 may be configured for generating a step command based on the analyzing. Further, the electrically powered force-generating component may be configured for varying at least one of the linear force and the rotatory force from a first force magnitude of at least one of the linear force and the rotatory force to a second force magnitude of at least one of the linear force and the rotatory force instantly based on the step command.
Further, in some embodiments, the processing device 206 may be configured for generating a progressing command based on the analyzing. Further, the electrically powered force-generating component may be configured for varying at least one of the linear force and the rotatory force from a first force magnitude of at least one of the linear force and the rotatory force to a second force magnitude of at least one of the linear force and the rotatory force progressively based on the progressive command.
Further, in some embodiments, the processing device 206 may be configured for generating a rate command based on the analyzing. Further, the electrically powered force-generating component may be configured for varying at least one of the linear force and the rotatory force from a first force magnitude of at least one of the linear force and the rotatory force to a second force magnitude of at least one of the linear force and the rotatory force by a varying rate based on the rate command.
Further, in some embodiments, the weight-machine apparatus 200 further may include at least one assembly sensor 302, as shown in
Further, in some embodiments, the electrically powered force-generating component may include a geared electric motor, the force-receiving component may include a handle, and the force transmission component may include a cable. Further, the geared electric motor may be configured for generating at least one of the linear force and the rotatory force based on the command. Further, the handle may be coupled with the geared electric motor using the cable. Further, the handle may be configured for receiving at least one of the linear force and the rotatory force using the cable. Further, the handle may be configured for presenting the inertial value.
Further, in some embodiments, the force-receiving component may be configured for transitioning between a first component position and a second component position based on the receiving. Further, the presenting of the inertial value may be based on the transitioning.
Further, in some embodiments, the force-receiving component may be configured for transitioning between a first component position and a second component position based on the receiving. Further, the transitioning between the first component position and the second component position may be associated with at least one transition speed. Further, the presenting of the inertial value may be based on the transitioning. Further, in some embodiments, the processing device 206 may be configured for generating a speed command based on the analyzing. Further, the electrically powered force-generating component may be configured for varying at least one linear force and the rotary force corresponding to the at least one transition speed based on the speed command.
Further, in some embodiments, the weight-machine apparatus 200 further may include at least one recognition sensor 402, as shown in
Further, in some embodiments, the weight-machine apparatus 200 further may include at least one physiological sensor 502, as shown in
Further, in some embodiments, the weight-machine apparatus 200 further may include a presentation device 602, as shown in
Further, the electromechanical assembly 702 may include a geared electric motor and a handle. Further, the geared electric motor may be configured for generating at least one of a linear force and a rotatory force based on a command. Further, the handle may be coupled with the geared electric motor using a cable. Further, the handle may be configured for receiving at least one of the linear force and the rotatory force. Further, the handle may be associated with an inertial value. Further, the inertial value may be based on at least one of the linear force and the rotatory force. Further, the handle may be configured for presenting the inertial value corresponding to at least one of the linear force and the rotatory force.
Further, the at least one voice sensor 704 may be configured for detecting at least one voice sample. Further, the at least one voice sensor 704 may be configured for generating voice sensor data based on the detecting.
Further, the processing device 706 may be communicatively coupled with the at least one voice sensor 704 and the geared electric motor. Further, the processing device 706 may be configured for analyzing the voice sensor data. Further, the processing device 706 may be configured for generating the command based on the analyzing.
Further, the storage device 708 may be communicatively coupled with the processing device 706. Further, the storage device 708 may be configured for storing the voice sensor data and the command associated with the voice sensor data.
Further, the power source 710 may be electrically coupled with the geared electric motor, the processing device 706, the at least one voice sensor 704, and the storage device 708. Further, the power source 710 may be configured for electrically powering the geared motor, the processing device 706, the at least one voice sensor 704, and the storage device 708.
Further, in some embodiments, the processing device 706 may be configured for generating a magnitude command based on the analyzing. Further, the geared motor configured for varying at least one of the linear force and the rotatory force to a fixed force magnitude of at least one of the linear force and the rotatory force based on the magnitude command.
Further, in some embodiments, the processing device 706 may be configured for generating a step command based on the analyzing. Further, the geared motor may be configured for varying at least one of the linear force and the rotatory force from a first force magnitude of at least one of the linear force and the rotatory force to a second force magnitude of at least one of the linear force and the rotatory force instantly based on the step command.
Further, in some embodiments, the processing device 706 may be configured for generating a progressing command based on the analyzing. Further, the geared motor may be configured for varying at least one of the linear force and the rotatory force from a first force magnitude of at least one of the linear force and the rotatory force to a second force magnitude of at least one of the linear force and the rotatory force progressively based on the progressive command.
Further, in some embodiments, the handle may be configured for transitioning between a first component position and a second component position based on the receiving. Further, the transitioning between the first component position and the second component position may be associated with at least one transition speed. Further, the presenting of the inertial value may be based on the transitioning. Further, in some embodiments, the processing device 706 may be configured for generating a speed command based on the analyzing. Further, the geared motor may be configured for varying at least one linear force and the rotary force corresponding to the at least one transition speed based on the speed command.
Further, the gear assembly 1002 may include a geared motor, a cable reel/spool, and other components. Further, the geared motor may generate torque from 0 to 500 pounds (may be upgraded to 0 to 1000 pounds or higher). Further, the cable reel/spool may draw 10 feet of cable (may be upgraded). Further, the other components may include gears and pulleys to harness cables in the ideal positions.
Further, the plurality of force sensors 1004-1006 may include tension and compression sensors (from 0 to 1000 pounds).
Further, the microprocessor 1008 may include AI-capable IoT boards with wireless connectivity.
Further, peripherals may include an LCD touchscreen 1010, a microphone 1012, a speaker 1014, a power supply 1016, and a wireless communication 1018. Further, the LCD touchscreen 1010 may be utilized for visual user interface and to interact with a device. Further, the microphone may be utilized for voice command input to the system 1000. Further, the speaker 1014 may be utilized for sound alerts and systems response. Further, the power supply 1016 may be utilized to power electronics components of the core system.
Further, the system 1100 may utilize a chain cable instead of the strongest fiber core polyurethane-coated gym wire. The decision to choose from various models may depend on the expected frequency of use of a machine, a precision of control, and a maximum force that the machine may be used for.
Further, the sensors 1202 may include an HD camera 1208, a mmWave 1210, a gesture sensor 1212, a distance sensor 1214, and a 10-DOF 1216. Further, the HD camera 1208 may include a high definition camera for visual input to the system 1200. Further, the mmWave 1210 may measure the vital sign of the user 1222 as well as for determining spatial data. Further, gesture sensor 1212 may read the user's gestures. Further, the distance sensor 1214 may read the distance between a device and object/users in front. Further, the 10-DOF 1216 may sense the level, position and stability of a machine during use.
Further, the system's extensions 1204 may include an MCU 1218 (for sensors) and a memory 1220. Further, the MCU 1218 (for sensors) may handle sensor-specific processing. Further, the memory 1220 may store sensor data.
Further, the control devices 1302 may include a headset 1308, a mobile phone 1310, and tablets 1312. Further, the headset 1308 may send voice commands and hear responses. Further, the mobile phone 1310 may send voice command and control the core system 1306 (via touchscreen) as well as to receive audio/visual response. Further, the tablets 1312 may send voice command and control the core system 1306 (via touchscreen) as well as to receive audio/visual response.
Further, the other devices may include 1304 a laptop/desktop 1314, a mobile phone 1316, and tablets 1318. Further, the laptop/desktop 1314, the mobile phone 1316, and the tablets 1318 may connect to the core system 1306 to extract, store, and analyze data from the core system 1306. Further, the laptop/desktop 1314, the mobile phone 1316, and the tablets 1318 may connect to the core system 1306 to configure the core system remotely. Further, the laptop/desktop 1314, the mobile phone 1316, and the tablets 1318 may connect to the core system 1306 to create a workout/training template then load the workout/training template to the core system 1306. Further, the laptop/desktop 1314, the mobile phone 1316, and the tablets 1318 may connect to the core system 1306 to backup and restore core system data.
Further, the external sensors 1402 may be associated with a user 1412. Further, the external sensor may be configured to gather data associated with the user 1412 and transmit the data to a core system 1404 for data integration and real-time analysis (raise alerts/notification if necessary). Further, the pulse/heart-rate sensor 1406 may detect blood pressure and heartbeat data of the user 1412. Further, the 10-DOF 1408 may gather spatial data of the user 1412 to determine motion, inertia, and altitude of the user 1412. Further, the flex and pressure sensor 1410 may gather bending force and strain/stress force exerted by the user 1412.
With reference to
Computing device 1500 may have additional features or functionality. For example, computing device 1500 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in
Computing device 1500 may also contain a communication connection 1516 that may allow device 1500 to communicate with other computing devices 1518, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 1516 is one example of communication media. Communication media may typically be embodied by computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer-readable media as used herein may include both storage media and communication media.
As stated above, a number of program modules and data files may be stored in system memory 1504, including operating system 1505. While executing on processing unit 1502, programming modules 1506 (e.g., application 1520 such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit 1502 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.
Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, general-purpose graphics processor-based systems, multiprocessor systems, microprocessor-based or programmable consumer electronics, application-specific integrated circuit-based electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems.
Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer-readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The computer-usable or computer-readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
Embodiments of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, solid-state storage (e.g., USB drive), or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the disclosure.
Although the present disclosure has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the disclosure.
