# US 11,992,727 B2 · Wearable device systems and methods for guiding physical movements > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,992,727 B2 - Document ID: US11992727B2 - Status: Granted - Kind: B2 - Application number: 17/321,620 - Filed: 2021-05-17 - Published: 2022-01-13 - Granted: 2024-05-28 - Earliest claimed: 2019-05-03 - Assignees: Xperience Robotics, Inc. - Applicants: Xperience Robotics, Inc. - Inventors: Ankit Shah - Web page: https://patentgrep.com/patent/US11992727B2 ## CPC - A63B 24/0006: {Computerised comparison for qualitative assessment of motion sequences or the course of a movement} - G09B 19/0038: {Sports} - A63B 24/0062: {Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance} - G06F 1/163: {Wearable computers, e.g. on a belt} - G06F 3/016: {Input arrangements with force or tactile feedback as computer generated output to the user} - G06F 3/017: {Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883)} - G09B 19/003: {Repetitive work cycles; Sequence of movements} - A63B 71/0619: {Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills} - A63B 2024/0009: {Computerised real time comparison with previous movements or motion sequences of the user} - A63B 2024/0015: {Comparing movements or motion sequences with computerised simulations of movements or motion sequences, e.g. for generating an ideal template as reference to be achieved by the user} - A63B 2220/40: Acceleration - A63B 2220/803: Motion sensors - A63B 2220/836: Sensors arranged on the body of the user - A63B 2220/89: Field sensors, e.g. radar systems - A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H) - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject. ## Claims ### Claim 1 A method of guiding physical movements, the method comprising: calibrating motion sensors of a wearable device according to movement models for designated movements of a movement plan, wherein the motion sensors include at least two of: a gyroscope, an accelerometer, or a magnetometer, and wherein the movement models are derived from an assessment of a subject using the wearable device and reflect an ability of the subject to perform each of the designated movements; converting, with a computing device associated with the wearable device applying one or more data filters, raw sensor data to formatted data, the raw sensor data being generated by the motion sensors according to the calibration of the motion sensors, for a performance of a physical movement by the subject; identifying, with the computing device, a designated movement from the designated movements that corresponds to the physical movement; selecting, with the computing device, one or more probability-based comparative modeling techniques based on a movement model for the designated movement; determining, with the computing device and based on the calibration of the motion sensors, a level of compliance of the physical movement with the movement model by applying the one or more probability-based comparative modeling techniques to the formatted data; and delivering, with the wearable device, real-time feedback to the subject according to a first feedback scheme during the performance of the physical movement based on the level of compliance. ### Claim 2 (depends on 1) The method of claim 1, further comprising, prior to delivering the real-time feedback, determining the first feedback scheme based on a proximity of the wearable device to a location on a body of the subject where a deviation from the movement model is identified real-time. ### Claim 3 (depends on 1) The method of claim 1, further comprising, prior to delivering the real-time feedback, determining the first feedback scheme based on a magnitude of a deviation of the physical movement from the movement model. ### Claim 4 (depends on 1) The method of claim 1, further comprising determining a second feedback scheme based on a degree of correction exhibited by the subject during the performance of the physical movement in response to the first feedback scheme. ### Claim 5 (depends on 1) The method of claim 1, further comprising selecting one or more matrix operations based on the raw sensor data and condition of a subject, and wherein converting the raw sensory data to the formatted data includes performing the one or more matrix operations on one of the raw sensor data and data derived from the raw sensor data. ### Claim 6 (depends on 1) The method of claim 1, further comprising generating, with the computing device, the first feedback scheme based on one of the assessment, a condition of the subject, and the level of compliance. ### Claim 7 A non-transitory, computer-readable medium comprising instructions that, when executed by a processor, perform operations for guiding physical movements, the operations comprising: calibrating motion sensors of a wearable device according to movement models for designated movements of a movement plan, wherein the motion sensors include at least two of: a gyroscope, an accelerometer, or a magnetometer, and wherein the movement models are derived from an assessment of a subject using the wearable device and reflect an ability of the subject to perform each of the designated movements; converting, with a computing device associated with the wearable device applying one or more data filters, raw sensor data to formatted data, the raw sensor data being generated by the motion sensors according to the calibration of the motion sensors, for a performance of a physical movement by the subject; identifying, with the computing device, a designated movement from the designated movements that corresponds to the physical movement; selecting, with the computing device, one or more probability-based comparative modeling techniques based on a movement model for the designated movement; determining, with the computing device and based on the calibration of the motion sensors, a level of compliance of the physical movement with the movement model by applying the one or more probability-based comparative modeling techniques to the formatted data; and delivering, with the wearable device, real-time feedback to the subject according to a first feedback scheme during the performance of the physical movement based on the level of compliance. ### Claim 8 (depends on 7) The non-transitory, computer-readable medium of claim 7, the operations further comprising, prior to delivering the real-time feedback, determining the first feedback scheme based on a proximity of the wearable device to a location on a body of the subject where a deviation from the movement model is identified real-time. ### Claim 9 (depends on 7) The non-transitory, computer-readable medium of claim 7, the operations further comprising, prior to delivering the real-time feedback, determining the first feedback scheme based on a magnitude of a deviation of the physical movement from the movement model. ### Claim 10 (depends on 7) The non-transitory, computer-readable medium of claim 7, the operatons further comprising, determining a second feedback scheme based on a degree of correction exhibited by the subject during the performance of the physical movement in response to the first feedback scheme. ### Claim 11 (depends on 7) The non-transitory, computer-readable medium of claim 7, the operations further comprising selecting one or more matrix operations based on the raw sensor data and a condition of the subject, and wherein converting the raw sensor data to the formatted data includes performing the one or more matrix operations on one of the raw sensor data and data derived from the raw sensor data. ### Claim 12 (depends on 7) The non-transitory, computer-readable medium of claim 7, the operations further comprising generating, with the computing device, the first feedback scheme based on one of the assessment, a condition of the subject, and the level of compliance. ### Claim 13 A system for guiding physical movements, comprising: a wearable device configured to be positioned on a subject; the wearable device including: a set of motion sensors including at least two of: a gyroscope, an accelerometer, or a magnetometer; a memory storage including a non-transitory, computer-readable medium comprising instructions; and a processor that executes the instructions; wherein the processor for the wearable device executes instructions to perform operations including: calibrating the motion sensors of the wearable device according to movement models for designated movements of a movement plan, and wherein the movement models are derived from an assessment of a subject using the wearable device and reflect an ability of the subject to perform each of the designated movements; converting, with a computing device associated with the wearable device applying one or more data filters, raw sensor data to formatted data, the raw sensor data being generated by the motion sensors according to the calibration of the motion sensors, for a performance of a physical movement by the subject; identifying, with the computing device, a designated movement from the designated movements that corresponds to the physical movement; selecting, with the computing device, one or more probability-based comparative modeling techniques based on a movement model for the designated movement; determining, with the computing device and based on the calibration of the motion sensors, a level of compliance of the physical movement with the movement model by applying the one or more probability-based comparative modeling techniques to the formatted data; and delivering, with the wearable device, real-time feedback to the subject according to a first feedback scheme during the performance of the physical movement based on the level of compliance. ### Claim 14 (depends on 13) The system of claim 13, the operations further comprising, prior to delivering the real-time feedback, determining the first feedback scheme based on a proximity of the wearable device to a location on a body of the subject where a deviation from the movement model is identified real-time. ### Claim 15 (depends on 13) The system of claim 13, the operations further comprising, prior to delivering the real-time feedback, determining the first feedback scheme based on a magnitude of a deviation of the physical movement from the movement model. ### Claim 16 (depends on 13) The system of claim 13, the operations further comprising, determining a second feedback scheme based on a degree of correction exhibited by the subject during the performance of the physical movement in response to the first feedback scheme. ### Claim 17 (depends on 13) The system of claim 13, the operations further comprising selecting one or more matrix operations based on the raw sensor sensor data and the condition of the subject, and wherein converting the raw sensor data to the formatted data includes performing the one or more matrix operations on one of the raw sensor data and data derived from the raw sensor data. ### Claim 18 (depends on 13) The system of claim 13, the operations further comprising generating, with the computing device, the first feedback scheme based on one of the assessment, the condition of the subject, and the level of compliance. ### Claim 19 (depends on 13) The system of claim 13, wherein calibrating the motion sensors includes setting one or more of a frame of reference, sensitivity, sample rate, storage time, or feedback triggers for the motion sensors. ### Claim 20 (depends on 13) The system of claim 13, wherein the probability-based comparative modeling is performed with unsupervised machine learning, supervised machine learning, semi-supervised machine learning, real-time waveform matching, Bayesian analysis, Kalman filtering, Extended Kalman filtering, or particle filtering.