An exercise feedback system monitors the performance of athletes wearing a garment with sensors while exercising. The sensors generate physiological data such as muscle activation data, heart rate data, or data describing the athlete's movement. The system extracts features from the physiological data and compares the features with reference exercise data to determine metrics of performance and biofeedback. Based on the physiological data, the system may also modify exercise training programs for the athlete. The exercise feedback system can display the biofeedback using visuals or audio, as well as modified exercise training programs, via the athlete's client device in real time while the athlete is exercising. By reviewing the biofeedback, the athlete may correct the athlete's exercise form to properly use the target muscles for the exercise, or change the certain workouts to personalize the training program.

A method is disclosed for using an artificial intelligence engine to interact with a user of an exercise device during an exercise session. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive data as input, and based on the data, providing an output. While a user performs an exercise using the exercise device, the method includes receiving the data from an input peripheral of a computing device associated with the user. Based on the data being received from the input peripheral, the method includes determining, via the machine learning model, the output to control an aspect of the exercise device.

A method is disclosed for using an artificial intelligence engine to perform a control action. The control action is based on one or more measurements from a wearable device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive the one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the control action to be performed. The method includes receiving the one or more measurements from the wearable device being worn by a user, determining whether the one or more measurements indicate, during an interval training session, that one or more characteristics of the user are within a desired target zone, and responsive to determining that the one or more measurements indicate the one or more characteristics of the user are not within the desired target zone during the interval training session, performing the control action.

A method is disclosed for using an artificial intelligence engine to modify resistance of one or more pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the exercise device to modify the resistance of the one or more pedals. The method includes receiving the one or more measurements from a sensor associated with the one or more pedals of the exercise device, determining whether the one or more measurements satisfy a trigger condition, and responsive to determining that the one or more measurements satisfy the trigger condition, transmitting the control instruction to the exercise device.

An exercise apparatus includes a base, an operating unit that is movable relative to the base, and a motor that is coupled to the operating unit. The exercise apparatus also includes a sensor that is operable to detect engagement of a user with the operating unit, and a controller that is in communication with the operating unit and the sensor. The controller con generate validated exercise use data in response to the sensor detecting engagement of the user with the operating unit and movement of the operating unit relative to the base.

Example embodiments may relate to a system, method, apparatus, and computer readable media configured for monitoring a user performing an exercise and generating a avatar of the user and a virtual shadow, wherein the virtual shadow illustrates proper form of the exercise. The example embodiments may further be configured for determining an amount of overlap between the virtual avatar and the virtual shadow, and generating a feedback score based on the amount of overlap.

A system, method, and wireless earpieces for implementing a virtual assistant. A request is received from a user to be implemented by wireless earpieces. A virtual assistant is executed on the wireless earpieces. An action is implemented to fulfill the request utilizing the virtual assistant. The wireless earpieces may be a set of wireless earpieces and the virtual assistant may be implemented independently by the wireless earpieces.

A lighting system for a treadmill comprising front power disks positioned at each end of a front axle, rear power disks positioned at each end of a rear axle, a spring-loaded carbon brush associated with a respective power disk, each spring-loaded carbon brush attached to a fixed part of the treadmill and in physical contact with the respective power disk, each spring-loaded carbon brush electrified by a primary power source and in turn electrifying the respective power disk, and a first conductor connected to at least one slat of the multiple slats and a second conductor connected to the at least one slat, the first conductor and second conductor in electrical communication with a light on the at least one slat and in contact with one of the front power disks or the rear power disks.

Systems and methods for prescribing athletic activity to be performed by a user, and for adapting the prescribed athletic activity based on completed (e.g. ongoing) athletic performances by the user. A coaching plan may be automatically created that prescribes personalized athletic activities as a user trains towards a goal date. The athletic information may be received from one or more sensor devices associated with a user, and the coaching plan may be continuously or intermittently updated based on the received sensor data.

Exemplary embodiments of the present disclosure are directed to systems, methods, and computer-readable media configured to autonomously generate personalized recommendations for a user before, during, or after a round of golf. The systems and methods can utilize course data, environmental data, user data, and/or equipment data in conjunctions with one or more machine learning algorithms to autonomously generate the personalized recommendations.