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.

An electromyography processing apparatus 1 includes a storage device 10 that stores electromyography data 11 of a predetermined muscle, an onset detection unit 23 that detects an onset section of the electromyography data 11 in which an electromyography increases by a repetitive exercise performed by an athlete, and an ON/OFF indicator processing unit 25 that calculates a variance a probability distribution acquired by normalizing a root-mean-square value of the electromyography for each onset section, and outputs the calculated variance as an ON/OFF indicator for each onset section.

An electromyography processing apparatus 1 includes a storage device 10 that stores electromyography data 11 of a predetermined muscle, and a balance indicator processing unit 28 that calculates a root-mean-square value of the electromyography for each predetermined time, repeats a process for each of pieces of electromyography data of each of a plurality of muscles, the process being of outputting, as a balance evaluation value of the predetermined muscle, a value acquired by subjecting, to time differentiation, the time course of an average value of the root-mean-square value in a sliding window for calculating balance evaluation value, and outputs a balance indicator based on a balance evaluation value of each of the plurality of muscles.

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.

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.