A wearable computing device that is secured or attached to a resilient strap, the resilient strap sized to fit around at least a part of a circumference of a human head. The device has a waterproof storage volume housing a plurality of sensors. The wearable computing device may calculate a plurality of swimming metrics based at least partly on data received from the at least one sensor. The wearable computing device may include a wireless communication subsystem secured to the resilient strap within the waterproof storage volume and may transmit at least one of the calculated plurality of swimming metrics to at least one computing device via the wireless communication subsystem. The wearable computing device may include at least one user output component and may present an indication of at least one of the calculated plurality of swimming metrics at the at least one user output component for communication to the user. The indication may be audio played in an ear piece connected to the strap, or may be visually displayed on a display in an eye goggle portion of the strap.

A sensor event detection system including a motion capture element and another sensor. The sensor captures values associated with an orientation, position, velocity and acceleration and recognizes an event within the data to determine event data. Uses other values associated with a temperature, humidity, wind and elevation, i.e., environmental and physiological sensors and correlates the data or event data with the other values to determine a type of event or true event or a false positive event, or a type of equipment the motion capture element is coupled with, or a type of activity indicated by the data or event data and transmits the data or event data associated with the event.

Systems, methods, and computer-readable mediums for operating an electromechanical device are disclosed. The system includes, in one example, the electromechanical device, a patient portal, and a computing device. The computing device is configured to receive user data relating to a user, and receive treatment data relating to treatment plans and outcomes. The computing device is also configured to generate a prehabilitation plan by using a machine learning model to process the user data and the treatment data. The computing device is further configured to select, for the electromechanical device, an electromechanical device configuration that enables exercises of the prehabilitation plan to be performed by the user such that performance improves an area of the user's body. The computing device is also configured to enable the electromechanical device to implement the electromechanical device configuration.

A golf swing analyzer system for analyzing a golfer's golf swing. An inertial measurement unit (IMU) may be used to capture data regarding the swing. The IMU may be oriented along a hosel-axis and a face-axis to reduce the complexity of calculations to determine the path of the swing. Two IMUs may be used to permit correction of captured data, different orientations of the IMUs to increase accuracy of captured data, and a doubling of the captured data. The IMU may include a magnetometer that cooperates with a magnet that is adapted to indicate the time at or near impact of the head of the golf club with the golf ball. An LED system provides visual information to a user regarding the swing while the swing is being made.

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.

An operating method of an electronic device includes receiving a program including a machine learning model generated by performing machine learning using, as training data, information on a plurality of skeletons associated with a specific motion of an expert and/or a professional athlete associated with a specific sport, information on a plurality of angular velocities associated with the specific motion, and/or a plurality of pieces of evaluated information associated with the specific motion, which are accumulated in a server, from the server, and executing the program and receiving information on second angular velocity for the specific motion of a user of the electronic device from a swing practice device based on execution of the program.

A system and method for analyzing the swing motion of sporting equipment, for example, a golf club, including at least one or more inertial acceleration sensors, one or more gyrometric sensors, a data acquiring unit, a core micro-controller, and a Bluetooth radio. The motion detecting unit detects at least one motion of the swing. Particularly, the sensors and data acquiring unit calculates swing information using the acquired detection data to match a user’s swing motion to optimized equipment. Particularly, the data acquiring unit acquires detection data from the sensor(s) and forwards such data to a computing device or server having stored information that matches the swing motion to an optimized swing device.

Embodiments provided herein measure metrics of an athletic action and an object associated therewith, and more particularly, to measuring the metrics and characteristics of a baseball during the wind-up, release, flight, and catch of a pitch sequence. Methods may include: receiving, from at least one motion sensor associated with an object, acceleration data and angular velocity data of the object in response to an athletic action performed on the object; processing the acceleration data to establish vector rotation data between a frame of reference of the object and an Earth frame of reference; applying the vector rotation data to the acceleration data to obtain acceleration of the object in the Earth frame of reference; applying the vector rotation data to the angular velocity data to obtain angular velocity of the object in the Earth frame of reference.

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.

An exercise equipment system with a movable support that is movable in a lateral direction, a sensor adapted to generate a lateral tilt signal, and a processor for generating a left or right turn output signal to a ride simulation in response to tilting of the exercise equipment during use. The exercise equipment may be a cycle mounted to a base. The processor may further filter out small magnitude sensor signals generated through left or right tilting that occurs during cycling, which are not indicative of a turn. The system may also include a display for visualizing a ride simulation of one or more network connected cycle avatars that are individually controlled with a lean-to-steer system.