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.

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 swimming training aid is provided and comprises an elongate buoyant body. The body has a generally circular section along substantially the entirety of its length, and the diameter of the body is non-constant along at least the majority of its length.

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.

A method of operating an exoskeleton device includes: receiving sensor information; connecting a clutch system to a pulley system in; determining whether to engage a drive train gear to the clutch system based on the sensor information; engaging the drive train gear through the clutch system when determined to engage the drive train gear; and powering a first motor to drive the drive train gear for controlling a joint or segment of exoskeleton device.

A computer-implemented system comprising a treatment apparatus, a patient interface, and a processing device is disclosed. The processing device is configured to receive treatment data pertaining to the user during the telemedicine session, wherein the treatment data comprises one or more characteristics of the user; determine, via one or more trained machine learning models, at least one respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data; determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and transmit the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user.

In one embodiment, a computer-implemented system includes a treatment apparatus configured to be manipulated by a user while performing an exercise session, patient interfaces associated with users, and a server computing device configured to receive treatment data pertaining to the user, determine whether at least one characteristic of the user matches at least one second characteristic of a second user, where the second user belonging to a cohort. Responsive to determining the at least one characteristic of the user matches the at least one second characteristic of the second user, the server computing device is configured to assign the user to the cohort and select, via a trained machine learning model, a treatment plan for the user. Responsive to transmitting a signal to the patient interfaces of users in the cohort, the server computing device enables the patient interfaces to establish the virtual shared session between the patient interfaces.

An athletic training system has a data recording system and a data engine. The data recording system is configured to record an athletic competition event. The event may have a first team of players competing against a second team of players. The data engine is configured to receive data associated with the recorded athletic competition event. The data engine processes the data and displays the data as a replay of the event in animated form.

An athletic training system (200) has a data recording system (202) and a data engine (204). The data recording system (202) is configured to record an athletic competition event. The event may have a first team of players competing against a second team of players. The data engine (204) is configured to receive data associated with the recorded athletic competition event. The data engine (204) processes the data and displays the data as a replay of the event in animated form.

An electromechanical device for rehabilitation includes a motor and a control system including one or more processing devices operatively coupled to the motor. The one or more processing devices are configured to, responsive to a first trigger condition occurring, control the motor to operate in a passive mode, responsive to a second trigger condition occurring, control the motor to operate in an active-assisted mode, and responsive to a third trigger condition occurring, control the motor, by providing resistance, to operate in a resistive mode.