Disclosed is a technical training garment configured for use with modular, interchangeable biomechanics units and or resistance modules. The garment may provide resistance to movement throughout an angular range of motion and or tracks a variety of biomechanical parameters such as stride length, stride rate, angular velocity and power expended by the wearer. The garment may be low profile, and worn by a wearer as a primary garment or beneath or over conventional clothing or athletic uniform. The device may be worn as a supplemental training and or diagnostic tool during conventional training protocols, or as a biomechanics or biometric data capture device during competition.

The present disclosure provides an exercise monitoring device. The exercise monitoring device comprises a sensor module, a processing module, a storage module, and a user interface. The present disclosure also provides a method for estimating maximal oxygen consumption and/or future total exercise time by obtaining a person's physiological data and exercise data. The present disclosure further provides a method for calibrating the future total exercise time by environmental conditions to form an environmental specific total exercise time.

Disclosed is a technical training garment configured for use with modular, interchangeable biomechanics units and or resistance modules. The garment may provide resistance to movement throughout an angular range of motion and or tracks a variety of biomechanical parameters such as stride length, stride rate, angular velocity and power expended by the wearer. The garment may be low profile, and worn by a wearer as a primary garment or beneath or over conventional clothing or athletic uniform. The device may be worn as a supplemental training and or diagnostic tool during conventional training protocols, or as a biomechanics or biometric data capture device during competition.

A method includes receiving a set of treatment plans. Each treatment plan comprising the set of treatment plans may be associated with a user capable of using a treatment device to perform the associated treatment plan. The method also includes receiving healthcare professional profile information. The method also includes identifying treatment device information for each treatment device capable of being used by a cohort of users associated with respective treatment plans. The method also includes using an artificial intelligence engine, wherein the artificial intelligence engine uses at least one machine learning model configured to generate resource deployment predictions, to generate at least one resource deployment prediction. The at least one machine learning model may generate the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information.

Disclosed is a technical training garment configured for use with modular, interchangeable electronics and resistance modules. The garment provides resistance to movement throughout an angular range of motion and tracks biomechanical parameters such as stride length, stride rate, angular velocity and incremental power expended by the wearer. The garment may be low profile, and worn by a wearer as a primary garment or beneath or over conventional clothing. Alternatively, the device may be worn as a supplemental training tool during conventional training protocols.

Exemplary embodiments of the present disclosure are directed to systems, methods, and computer-readable media configured to autonomously track a round of golf and/or 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 computer-implemented method for (1) receiving one or more characteristics of a user, wherein the one or more characteristics comprise personal information, performance information, measurement information, or some combination thereof, (2) generating, using one or more trained machine learning models, a treatment plan for the user, wherein the treatment plan is generated based on the one or more characteristics of the user, and the treatment plan comprises: (i) a dietary plan that is tailored to manage one or more medical conditions associated with the user, and (ii) an exercise plan comprises one or more exercises associated with the one or more medical conditions, and (3) presenting, via a display device, at least a portion of the treatment plan comprising the dietary plan.

A computer-implemented system may include a stair-climbing machine configured to be manipulated by a user while the user performs a treatment plan, an interface comprising a display configured to present information associated with the treatment plan, and processing structure configured to receive, from one or more data sources, information associated with the user, wherein the information comprises one or more risk factors associated with a cardiac-related event; generate, using one or more trained machine learning models, the treatment plan for the user, wherein the treatment plan is generated based on the information associated with the user, and the treatment plan comprises one or more exercises associated with managing the one or more risk factors in order to reduce a probability that a cardiac intervention will occur; and transmit the treatment plan to cause the stair-climbing machine to implement the one or more exercises.

A computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue, enable the user to perform an exercise at different exertion levels. Next, while the user uses the electromechanical machine to perform the first treatment plan, receiving, at the computing device, data from sensors configured to measure the data associated with the invasive surgical-related health issue and transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, and the invasive surgical-related health issue. The method additionally includes receiving the second treatment plan.

Systems including an elliptical machine and a processing device. The processing device may be configured to receive, before or while a user operates the elliptical machine, one or more messages pertaining to the user or a use of the elliptical machine by the user. The processing device may be also configured to determine whether the one or more messages were received by the processing device. In response to determining that the one or more messages were not received by the processing device, the processing device may be configured to determine, via one or more machine learning models, one or more actions to perform. The one or more actions may include at least one of initiating a telecommunications transmission, stopping operation of the elliptical machine, and modifying one or more parameters associated with the operation of the elliptical machine.