A method for generating an exercise session for a user using an exercise machine is disclosed herein. The method includes receiving a plurality of inputs, wherein the plurality of inputs comprise an indication of a level of pain of the user and a range of motion of a body part of the user. The method also includes determining, based on the plurality of inputs, an exercise level of the user, generating, using a machine learning model, the exercise session for the user by selecting, based on the exercise level of the user, one or more exercises to be performed by the user using an exercise machine, and causing initiation of the exercise session on the exercise machine and a virtual coach executed by a computing device associated with the exercise machine to provide instructions pertaining to the exercise session.

A method is disclosed for generating an improved exercise plan for a user to perform using at least an exercise machine. The method includes receiving data pertaining to the user. The data includes a physical activity goal the user desires to achieve and the physical activity goal includes levels of attainment to achieve. The method includes generating, by an artificial intelligence engine, the improved exercise plan. The improved exercise plan includes a set of exercises to be performed by the user to achieve the levels of attainment associated with the physical activity goal. The artificial intelligence engine uses at least one data source configured to include information pertaining to one or more exercises and at least one of the levels of attainment associated with the physical activity goal. The method includes transmitting the improved exercise plan to a computing device.

A method is disclosed for using an artificial intelligence engine to modify resistance of pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive measurements as input, and outputting, based on the measurements, a control instruction that causes the exercise device to modify, independently from each other, the resistance of the pedals. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the pedals. The method includes determining, based on the measurements, a quantifiable or qualitative modification to the resistance provided by a pedal of the pedals. The resistance provided by another pedal of the pedals is not modified. The method includes transmitting the control instruction to the exercise device to cause the resistance provided by the pedal to be modified.

A processor-implemented method includes receiving a request that specifies a workout performed at a first time, a list of users, a second time after the first time, an overlay to be displayed during a rebroadcast associated with the request, and a skill level of the workout. The request is compared to calendar data, and a session acknowledgment message is sent to the compute device of the first user based on the comparison. An invitation message is sent to compute devices of a second user and a third user, identifying the second time, and invitation responses are received from the second user and the third user. In response to the invitation responses, a video of the workout is rebroadcast at the second time, to a smart mirror of the first user, a smart mirror of the second user and a smart mirror of the third user.

A system configured to be coupled with a participant of an activity. The system comprises: a participant activity monitoring unit configured for monitoring a performance of the activity by the participant; an activity information module configured for storing performance information corresponding to the activity; and a participant performance correlator configured for delivering comparative performance data based on the monitored performance of the activity by the participant and the stored performance information.

An exercise machine includes a cyclical actuator and a mechanical energy storage device. Connective structure operatively connects the cyclical actuator to the mechanical energy storage device. Motion of the cyclical actuator urges physical motion of and energy storage in the mechanical energy storage device. The exercise machine further includes an electric machine. A communication pathway enables exchange of data between multiple associated exercise machines such that multiple associated operators on multiple associated exercise machines have a common experience.

Training at the proper level of effort is important for athletes whose objective is to achieve the best results in the least time. In running, for example, pace is often monitored. However, pace alone does not reveal specific issues with regard to running form, efficiency, or technique, much less inform how training should be modified to improve performance or fitness. A sensing system and wearable sensor platform described herein provide real-time feedback to a user/wearer of his power expenditure during an activity. In one example, the system includes an inertial measurement unit (IMU) for acquiring multi-axis motion data at a first sampling rate, and an orientation sensor to acquire orientation data at a second sampling rate that is varied based on the multi-axis motion data.

A weight training method, a weight training apparatus and a weight training system are provided. In the method, a weight of a load of a user operating the weight training apparatus is detected by a load sensor, a motion of the load is detected by an activity sensor, and thereby an operation power of the weight training apparatus is calculated. A force applied by the muscle portion when the user operates the weight training apparatus is detected by at least one biophysical quantity sensor disposed on at least one muscle portion of the user and used to calculate an energy power consumed by the user. An exercise efficiency value of the user is calculated by using the energy power and the operation power, and the user is prompted to adjust an operation performed on the weight training apparatus when the exercise efficiency value drops beyond a preset ratio.

A robotic training apparatus for martial arts and combat sports that is of a dimension like a punching bag and can be hung or mounted on a floor. The apparatus includes a frame, an upper revolving member and a lower revolving member mounted to the frame, wherein the two members can revolve independently of each other along a vertical axis. A pair of robotic arms coupe to the upper revolving member can be actuated to resemble a punching action. A pair of robotic legs coupled to the lower revolving member can be actuated to resemble a kicking action. Both the pair of robotic arms and the pair of robotic legs horizontally extends from the upper revolving member and the lower revolving member respectively.

In one embodiment, a method includes accessing, by one or more computing devices, user sensor data from one or more wearable sensors on one or more players and optical sensor data from one or more cameras, where the user sensor data includes location data of the player and acceleration data, and where the optical sensor data includes several frames portraying the players and several scenes from an athletic event. The one or more computing devices analyzes, using a machine-learning model, the optical sensor data to identify the players and one or more actions during the athletic event and calculates one or more player metrics for the players based on the user sensor data and the identified actions. The one or more computing devices normalizes the player metrics for the players based on one or more weighted parameters and provides a report to one or more users.