Techniques for automatic tracking of user data for exercises are disclosed. In one aspect, a method of operating a wearable device may involve determining that a user of the wearable device has started an exercise, activating the GPS receiver in response to determining that the user has started the exercise, and detecting a time at which the GPS receiver achieves an initial GPS fix of a location of the wearable device. The method may further involve logging, based on output of the one or more biometric sensors, a first set of user data during a first time interval between the start of the exercise and the detected time of the initial GPS fix, and back-filling an exercise route of the user during the first time interval based on the first set of user data.

The method makes it possible to measure a speed of a competitor, such as a skier or snowboarder, on a ski or snowboard course, by means of a measurement system with a transponder module worn by the competitor. The method includes the step of activating the transponder module which includes a measurement unit with at least one measurement sensor, as soon as the race starts, a step of measuring the speed of the competitor via the measurement unit during the race along the course, and a step of transmitting the competitor's speed measurement to at least one base station of a decoder device of the measurement system for a timing device in order to display the speed of the competitor in real time or continuously on at least one screen.

A system for measuring weight-lifting performance is described. The system comprises a collar that may be attached to a weight bar, wherein the collar comprises at least one motion sensor, at least one touch sensor, and a signal processor. The motion sensors comprise a barometric altimeter, a gyroscope, and/or an accelerometer that measure the motion of the weight bar during a lifting activity. Signals from the motion sensors are interpreted by the signal processor as physical activity data, which are then wirelessly transmitted to a multimedia device having a data processor. The data processor is configured to calculate performance metrics from the physical activity data and to display them to a user via a display on the multimedia device. Athletes may use the performance metric outputs from the data processor to monitor the form of their lifting exercises for training purposes, safety considerations, and self-improvement.

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.

This disclosure relates to using a hand held target (e.g. tae kwon do paddle) as a tool for providing physical performance instructions according to a digital workout performance template loaded in advance.

A football device includes an outer skin and an inner bladder to be inflatable and substantially resemble and perform like an actual football. The football device includes a sensor unit that can sense various performance metrics and wirelessly communicate data to another device such as a mobile device. To maintain a feel and performance as close to an actual football as possible, a battery powering the sensor unit is wirelessly recharged.

An activity state analyzer 10 configured to analyze an activity state of a cyclist during cycling includes: a sensor unit (21 to 26, 18) configured to acquire sensor data from a plurality of types of sensors including an acceleration sensor 21 and a gyroscope sensor 22 that are attached to a lower back of the cyclist, and a CPU 11 configured to analyze an activity state of the cyclist during cycling based on a detection output from the plurality of types of sensors of the sensor unit (21 to 26, 18).

A system includes a minimally intrusive display system (MIDS) configured to be disposed on an eyewear. The MIDS includes a display system and a sensor system configured to provide for a sensor data. The MIDS further includes a processor configured to download a workout and to process the sensor data to monitor a user wearing the MIDS during the workout. The processor is further configured to display, via the display system, a workout progress based on the monitoring.

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.

An exercise machine includes a processor, a display, a deck, and a belt rotatable about the deck. The machine also includes a sensor operably connected to the processor and configured to detect a performance parameter associated with a belt of the exercise machine. In implementations described herein, the performance parameter may be disregarded when it is determined that a user is not on the belt.