Abstract: The present disclosure relates to systems and methods for elastic delivery, processing, and storage for wearable devices based on system resources. For example, a wearable apparatus may have at least one battery; at least one sensor configured to measure at least one property associated with a user of the wearable apparatus; at least one memory storing measurements from the at least one sensor and instructions; at least one transmitter configured to send data to a device remote from the wearable apparatus; and at least one processor configured to execute the instructions to: receive, from the at least one battery, an indicator of a charge; and based on the received indicator, sending a command to the at least one sensor to operate in a low-power mode or a high-power mode, the low-power mode having. a lower sampling rate than the high-power mode.

Abstract: This disclosure relates to systems, media, and methods for quantifying and monitoring exercise parameters and/or motion parameters, including performing data acquisition, analysis, and providing scientifically valid, clinically relevant, and/or actionable diagnostic feedback. Embodiments may be related to systems, devices, methods, and computer-readable media for providing baseline-adjusted real-time feedback to a user. Embodiments may include determining a type of activity for the user. Embodiment may include receiving data from the one or more motion sensors indicating a time-dependent series of three axis acceleration data and three-axis orientation data. Embodiments additionally may include providing a graphical user interface with a real-time representation of the received data.

Abstract: The present disclosure relates to systems and methods for elastic delivery, processing, and storage for wearable devices based on system resources. For example, a wearable apparatus may have at least one battery; at least one sensor configured to measure at least one property associated with a user of the wearable apparatus; at least one memory storing measurements from the at least one sensor and instructions; at least one transmitter configured to send data to a device remote from the wearable apparatus; and at least one processor configured to execute the instructions to: receive, from the at least one battery, an indicator of a charge; and based on the received indicator, sending a command to the at least one sensor to operate in a low-power mode or a high-power mode, the low-power mode having. a lower sampling rate than the high-power mode.

Abstract: This disclosure relates to systems, media, and methods for mitigating measurement drift and improving IMU odometry measurement. In an embodiment, the system may perform operations including receiving first sensor data from at least one motion sensor; receiving 3-dimensional (3-D) motion data based on motion detected by at least one camera; inputting model input data into a machine learning model configured to generate at least one vector, the model input data being based on the received first sensor data and the received 3-D motion data; and apply the at least one vector as an offset.

Abstract: This disclosure relates to systems, media, and methods for updating motion models using sensor data. In an embodiment, the system may perform operations including receiving sensor data from at least one motion sensor; generating training data based on at least one annotation associated with the sensor data and at least one data manipulation; receiving at least one experiment parameter; performing a first experiment using the training data and the at least one experiment parameter to generate experiment results; and performing at least one of: update or validate a first motion model based on the experiment results.

Abstract: This disclosure relates to systems, media, and methods for quantifying and monitoring exercise parameters and/or motion parameters, including performing data acquisition, analysis, and providing scientifically valid, clinically relevant, and/or actionable diagnostic feedback. Embodiments may be related to systems, devices, methods, and computer-readable media for providing baseline-adjusted real-time feedback to a user. Embodiments may include determining a type of activity for the user. Embodiment may include receiving data from the one or more motion sensors indicating a time-dependent series of three axis acceleration data and three-axis orientation data. Embodiments additionally may include providing a graphical user interface with a real-time representation of the received data.

Abstract: The present disclosure relates to systems and methods for balance index determination. For example, a wearable apparatus may have at least one gyroscope configured to measure angular velocity about a first axis; at least one inertial measurement device (IMU) configured to measure deviation along a second axis and a third axis; at least one memory storing instructions; and at least one processor configured to execute the instructions to: receive angular velocity measurements over a period of time from the at least one gyroscope; receive deviations from the second axis and from the third axis over the period of time from the at least one IMU; weight the deviations based on directions associated with the deviations; and generate a composite balance index based on the angular velocity measurements, the weighted deviations from the second axis, and the weighted deviations from the third axis.

Abstract: This disclosure relates to systems, media, and methods for providing near-instantaneous feedback from real-time motion sensor data. In an embodiment, the system may perform operations including loading at least one target motion trigger. Disclosed embodiments may receive real-time sensor data from the first motion sensor detachably fixed to a user. Additionally, disclosed embodiments may include calculating a motion profile based on the real-time sensor data, the motion profile describing a multi-dimensional representation of acceleration of a motion performed by the user. Disclosed embodiments may also include comparing the at least one target motion trigger to the calculated motion profile to determine if the motion performed by the user corresponds to the target motion. Further, disclose embodiments may include transmitting, based on the comparison, an instruction to provide an alert to a user.

Abstract: The present disclosure relates to systems and methods for elastic delivery, processing, and storage for wearable devices based on system resources. For example, a wearable apparatus may have at least one battery; at least one sensor configured to measure at least one property associated with a user of the wearable apparatus; at least one memory storing measurements from the at least one sensor and instructions; at least one transmitter configured to send data to a device remote from the wearable apparatus; and at least one processor configured to execute the instructions to: receive, from the at least one battery, an indicator of a charge; and based on the received indicator, sending a command to the at least one sensor to operate in a low-power mode or a high-power mode, the low-power mode having. a lower sampling rate than the high-power mode.

Abstract: A wearable device for determining balance of a user is provided, comprising a gyroscope, an inertial measurement device, a memory storing instructions, and a processor configured to execute the instructions to perform operations, comprising receiving angular velocity measurements over a period of time from the gyroscope, receiving deviations from the second axis over the period of time from the inertial measurement device, weighting the deviations from the second axis based on directions associated with the deviations, receiving deviations from the third axis over the period of time from the inertial measurement device, weighting the deviations from the third axis based on directions associated with the deviations, and based on the angular velocity measurements, the weighted deviations from the second axis, and the weighted deviations from the third axis, determining an index associated with a left side of the user and an index associated with a right side of the user.

Abstract: This disclosure relates to systems, media, and methods for quantifying and monitoring visual acuity, motor skill, and cognitive ability of a user through measuring user activities while the user responds to prompts on an electronic device. Disclosed embodiments may receive image data from a user-facing camera, motion data from a sensor device attached to the user, location data from a touchscreen, and time data measuring a user's response times. Disclosed embodiments may provide graphical user interfaces to which the user responds and calculate health metrics based on characteristics of the user's response. Disclosed embodiments may calculate visual acuity, motor skill, and cognitive ability metrics based on data measured while the user responds to the graphical user interfaces. Disclosed embodiments may include comparing the metrics to respective baseline values and providing an alert when the metrics deviate from threshold values indicating a change in user health.

Abstract: The present disclosure relates to systems and methods for elastic delivery, processing, and storage for wearable devices based on system resources. For example, a wearable apparatus may have at least one battery; at least one sensor configured to measure at least one property associated with a user of the wearable apparatus; at least one memory storing measurements from the at least one sensor and instructions; at least one transmitter configured to send data to a device remote from the wearable apparatus; and at least one processor configured to execute the instructions to: receive, from the at least one battery, an indicator of a charge; and based on the received indicator, sending a command to the at least one sensor to operate in a low-power mode or a high-power mode, the low-power mode having. a lower sampling rate than the high-power mode.

Abstract: This disclosure relates to systems, media, and methods for quantifying and monitoring exercise parameters and/or motion parameters, including performing data acquisition, analysis, and providing scientifically valid, clinically relevant, and/or actionable diagnostic feedback. Disclosed embodiments may receive real-time sensor data from a motion sensor or sensors mounted on a user and/or equipment while a user performs a test motion. Disclosed embodiments may also calculate a test motion profile based on the real-time sensor data, the test motion profile describing a multi-dimensional representation of the test motion performed by the user or computed motion profiles. Disclosed embodiments may include comparing the test motion profile to a template motion profile to determine a deviation amount for the test motion profile indicating how the test motion deviated from the template motion profile. Still further embodiments may correlate test motion profiles over time with health indicators.

Abstract: This disclosure relates to systems, media, and methods for quantifying and monitoring exercise parameters and/or motion parameters, including performing data acquisition, analysis, and providing scientifically valid, clinically relevant, and/or actionable diagnostic feedback. Disclosed embodiments may receive real-time sensor data from a motion sensor or sensors mounted on a user and/or equipment while a user performs a test motion. Disclosed embodiments may also calculate a test motion profile based on the real-time sensor data, the test motion profile describing a multi-dimensional representation of the test motion performed by the user or computed motion profiles. Disclosed embodiments may include comparing the test motion profile to a template motion profile to determine a deviation amount for the test motion profile indicating how the test motion deviated from the template motion profile. Still further embodiments may correlate test motion profiles over time with health indicators.

Abstract: This disclosure relates to systems, media, and methods for quantifying and monitoring exercise parameters and/or motion parameters, including performing data acquisition, analysis, and providing scientifically valid, clinically relevant, and/or actionable diagnostic feedback. Disclosed embodiments may receive real-time sensor data from a motion sensor or sensors mounted on a user and/or equipment while a user performs a test motion. Disclosed embodiments may also calculate a test motion profile based on the real-time sensor data, the test motion profile describing a multi-dimensional representation of the test motion performed by the user or computed motion profiles. Disclosed embodiments may include comparing the test motion profile to a template motion profile to determine a deviation amount for the test motion profile indicating how the test motion deviated from the template motion profile. Still further embodiments may correlate test motion profiles over time with health indicators.