The invention discloses an elasticity adjusting mechanism of an exercise equipment which includes a base, a staggered frame, an adjusting socket, an elastic body and force exerting member. The base and the staggered frame pivot with each other and constitute the main structure of the elasticity adjusting mechanism of the exercise equipment. The staggered frame extends outwardly from the base and includes an extended socket and a plurality of fixed portions, the fixed portions are disposed on the staggered frame and with different distances to the extended socket. When the adjusting socket is selectively assembled with one of the fixed portions at different positions, the elastic body passing through the extended socket and the adjusting socket changes its elasticity in accordance with extension amount.

An elastic structure of a sport equipment comprises a base, a staggered frame, an elastic body and an applying member. The base is supported on a ground. The elastic body is extendable and retractable located on the base. The applying member is connected to the elastic body for generating a stretching movement back and forth. The base and the staggered frame can be foldable, thus the elastic structure of the sport equipment has a smaller volume for collecting and saving.

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 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.

The present invention relates to a self-ambulation helper and assistive rehabilitative device that is primarily designed to train the initial developmental phase of early life i.e., independent crawling. The device includes a tummy rest, a pair of forearm rest, a pair of leg rests, a left supporting rod, a right supporting rod, and a main frame. The tummy rest includes a tummy bracket, a top layer, and a pair of height adjusting mechanisms. The pair of forearm rest includes a left forearm rest and a right forearm rest. The pair of leg rest includes a left leg rest and a right leg rest. Each forearm rest and each leg rest include an adjustable strap and a base support. The device ensures reinforcement of biomechanically correct training patterns through repetitive practice at home for such patients.