A folding exercise bike has handlebar telescoping members configured to support handlebars, seat telescoping members configured to support a seat, a lower frame member from which the seat telescoping members and the handlebar telescoping members extend, pedals having an attachment point above the lower frame member, a first pair of legs attached to one end of the lower frame member and a second pair of legs attached to another end of the lower frame member. Each leg of the first pair of legs extending at an oblique angle from the lower frame member in a deployed position configured to support the folding exercise bike on a surface and each leg of the second pair of legs extending from lower frame member at an oblique angle in the deployed position.

A muscle activity output system includes: a muscle activity database configured to hold movable part attitude information and muscle activity information in such a manner that the movable part attitude information and the muscle activity information are linked to each other; and a processor. The movable part attitude information indicates an attitude of a movable part of training equipment. The muscle activity information indicates a muscle activity of each of muscle sites of the exerciser. The processor is configured to acquire the movable part attitude information while the exerciser is moving the body along the trajectory, acquire current muscle activity information for each of the muscle sites based on current movable part attitude information of the training equipment and the muscle activity database, and output the current muscle activity information for each of the muscle sites.

A processing system includes a processor. The processor is configured to: acquire user data including information on an activity of daily living a user is able to perform; calculate a recommended setting of training equipment that places a load on a muscle of the user, based on the user data; and output the recommended setting.

An exercise machine accessory is disclosed. In one embodiment, a resistance identifier is configured to identify resistance for an exercise machine associated with the exercise machine accessory, wherein the resistance identifier is coupled to the exercise machine. In one embodiment, a motion identifier is configured to identify exercise motion for a user of the exercise machine. In one embodiment, a communications module is configured to communicate with the user of the exercise machine, wherein the communications module is coupled to the resistance identifier and the motion identifier.

An electromechanical device for rehabilitation includes pedals coupled to radially-adjustable couplings, an electric motor coupled to the pedals via the radially-adjustable couplings, and a control system including a processing device operatively coupled to the electric motor. The processing device configured to, responsive to a first trigger condition occurring, control the electric motor to operate in a passive mode by independently driving the radially-adjustable couplings rotationally coupled to the pedals. The processing device also configured to, responsive to a second trigger condition occurring, control the electric motor to operate in an active-assisted mode by measuring revolutions per minute of the radially-adjustable couplings, and cause the electric motor to drive the radially-adjustable couplings when the measured revolutions per minute satisfy a threshold condition, and responsive to a third trigger condition occurring, control the electric motor to operate in a resistive mode by providing resistance to rotation of the radially-adjustable couplings.

A system for determining and storing identification data of a user and training data relating of the user in a training space, includes a central data processing unit; a central memory unit; an image acquisition device; an exercise machine; and an exercise tool free from an actuator operable during physical exercise. The image acquisition device acquires identification images of the user, images representative of the user while performing a bodyweight physical exercise, images representative of the user and of the exercise machine while performing a physical exercise using the exercise machine, images representative of the user and of the exercise tool while performing a physical exercise using the exercise tool. Identification data and training data of the user is determined based on the images acquired by the image acquisition device. The device stores the determined identification data and training data inside the training space in the central memory unit.

Tri-Power Exercising device allows a rider to simultaneously, or on demand, exercise virtually all muscle groups in his lower and upper body. The device includes a bicycle frame, pedals, forearm bars, sliding seat, computer and electronic display recommending energy modulation amounts from various muscle groups to optimize physical performance on any given trek. Because riders can exercise virtually all muscle groups at once, they reduce their exercising time, continuously builds muscle tissue throughout their whole body, and exercises their cardiovascular and respiratory systems completely. Riders operate the device by rotating legs on the pedals, rotationally oscillating the forearm bars up and down with their arms and shoulders, and then use core muscles to pull and push the seat back and forth on the slider. Inverted racks, pinion gears, and one-way bearings turn this linear power from the oscillating forearm bars and sliding seat into torque that rotates the crank axle.

Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises, while the user uses a treatment apparatus to perform a first treatment plan for the user, receiving measurement data associated with the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.

An exercise machine includes a shaft configured to rotate about an axis; a spool configured to receive a strap and drive rotation of the shaft about the axis in response to movement of the strap; a flywheel configured to rotate about the axis; one or more conductive coils arranged to generate magnetic fields that induce eddy currents in the flywheel; a clutch configured to engage and disengage transmission of torque between the spool and the flywheel; and a spring arranged around the axis, the spring configured to exert a return torque on the spool about the axis in response to rotation of the shaft.

In general, a computing device disposed in a cardio exercise machine receives data related to a fitness level of a user. The computing device selects a workout program based at least in part on the data. The operations of the cardio exercise machine are controlled based at least in part on the workout program. The computing device generates visual feedback based on at least one of the user's operation of the cardio exercise machine and the selected workout program measured against performance criteria.