A force sensor is provided for mounting on an outside lateral surface of a golf club grip to alert a user when an excessive manual grasping force is applied to the grip during a golf swing.

An interchangeable and weight adjustable golf grip including at least an end cap body having integral receiving ports adapted to receive variable weights, electronic chips, sensors, gauges, lasers, cameras, or biometrics within and wherein the end cap is capable of housing the various weights, chips, sensors, etc. within the end cap so that a player can interchange the weights or sensors without removing the golf grip from the shaft in order to make adjustments and so that the player can play golf without removing any other portions of the grip. Further, the invention includes contemplation of a tip collette with integral receiving ports adapted to also harbor similar weights, or sensors, etc. Data from the sensors may be transmitted by Bluetooth transmission to a smartphone or a ball launch monitor system for analysis.

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. The real-time representation may include a scaled representation of at least one dimension of the time-dependent series of three axis acceleration data and three-axis orientation data for at least one of the one or more motion sensors based on the updated baseline adjustment.

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.

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.

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. The real-time representation may include a scaled representation of at least one dimension of the time-dependent series of three axis acceleration data and three-axis orientation data for at least one of the one or more motion sensors based on the updated baseline adjustment.

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.

A system and method configured to receive or obtain action quality data related to a user's action with a tool, such as a swing with a golf club, and corresponding pressure data related to the user's grip on the tool during the action. The system and method are configured to determine a correlation between the pressure data and action quality data, which may be normalized to remove the effects of gravity if the data is collected with a sensor that is sensitive to gravity, and output at least one indicator related to the correlation.

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. The real-time representation may include a scaled representation of at least one dimension of the time-dependent series of three axis acceleration data and three-axis orientation data for at least one of the one or more motion sensors based on the updated baseline adjustment.

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.