A configuration that enables a rehabilitation execution user to appropriately execute rehabilitation while confirming whether or not exercise being executed by the user is an appropriate exercise according to an exercise prescription is achieved. The configuration includes an exercise type estimation unit configured to estimate an exercise type executed by a user on the basis of detection information of a sensor worn on a body of the user, a database configured to store user-specific exercise intensity corresponding to the exercise type, an exercise type/personalized exercise intensity acquisition unit configured to acquire, from the database, an exercise type/personalized exercise intensity that is user-specific exercise intensity corresponding to the exercise type estimated by the exercise type estimation unit, and an exercise implementation status analysis unit configured to analyze exercise implementation status data generated on the basis of the exercise type/personalized exercise intensity acquired from the database and generate an exercise implementation status analysis result indicating whether or not exercise being executed by the user is an exercise according to a pre-generated exercise prescription, in which the generated exercise implementation status analysis result is output to a display unit.

When controlling load torque of a drive unit on the basis of a biological signal detected from a subject's lower limb parts, adjustments are made to a hybrid ratio of voluntary control to generate running torque and a rotational speed of a crank according to the subject's intention and autonomous control to generate assisting power for the subject's pedaling motion on the basis of a rotation angle of the crank.

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.

A computer-implemented system may include a rowing 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 a processing device 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 rowing machine to implement the one or more exercises.

A computer-implemented system includes a processing device configured to receive a plurality of user and blood vessel characteristics associated with a user, generate a selected set of user and blood vessel characteristics, determine, based on the selected set of the user and blood vessel characteristics, a probability that angiogenesis will occur, and generate, based on the probability and the selected set of the user and blood vessel characteristics, a treatment plan that includes one or more exercises directed to modifying the probability that angiogenesis will occur, and a treatment apparatus configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

A computer-implemented system includes one or more processing devices configured to receive user information associated with a user, generate a selected set of the user information, determine, based on the selected set of the user information, at least one of a first probability of surviving one or more procedures and a second probability indicating an improvement, resulting from the one or more procedures, in quality-of-life metrics for the user, generate, based on the at least one of the first probability and the second probability and on the selected set of the user information, one or more recommendations of whether the user should undergo the one or more procedures, and generate, based on the one or more recommendations, a treatment plan that includes one or more exercises directed to modifying the at least one of the first probability and the second probability.

A respiratory training device providing both inspiratory and expiratory functional evaluation and training, as well as independent regulation of both the inspiratory and expiratory airway resistance levels used during training. The respiratory training device also includes data acquisition, recording, storage, retrieval and display functions for airway pressure monitoring data to provide functional evaluation, physiological monitoring, and diagnostic features. The respiratory training device allows the user to easily develop and follow precise and advanced training protocols, and utilize the respiratory device in both the clinical and home setting.

A program is presented to a user including a stream of content including a first auditory content and a first visual content related to the workout. A context state of the user participating in the workout is determined based on information from a sensor. A responsive content is selectively added based on the context state of the user to the stream of content in a manner that avoids conflict with the stream of content.

A system for providing a personalised exercise guide according to various embodiments of the present invention may comprise: a wearable device for measuring a first bio-signal of a user using one or more sensors, and for transmitting to an electronic device and measured first bio-signal of the user; and the electronic device for receiving from the wearable device the measured first bio-signal of the user, for obtaining body data of the user based on the received first bio-signal of the user, for calculating a required quantity of exercise based on the obtained body data of the user and a preset goal, and for comparing and analyzing body data variation of the user that is estimated based on the calculated required quantity of exercise with the actual body data variation of the user due to exercise.