An exercising equipment having base formed of a U-shape pipe; first and second arms, each rotatably connected to the base and having a securing knob engageable to affix the arm at an acute angle to the base; first paddle rotatably connected at a first connecting point to the first arm; second paddle rotatably connected at a second connecting point to the second arm; foldable connecting bar assembly connected at one end to the first paddle and at the second end to the second paddle, to thereby form an S-shape together with the first and second paddles when assuming an extended position and form a W-shape together with the first and second paddles when assuming a folded position; and, at least one variable friction pivot assembly attached at the first or second connecting points thereby applying variable friction to rotation of at least one of the first or second paddles.

A stationary exercise device includes a support structure and one or more input members such as pedals, handles, or other feature movably connected to the support structure. A movable output member such as a wheel is movably mounted to the support structure, and operably connected to the one or more input members such that movement of the one or more input members causes the output member to move. A resistance member interacts with the output member when the output member is moving to generate a variable resistance force tending to prevent movement of the output member. The variable resistance force may at least partially simulate the effects of inertia and/or momentum.

A computer-implemented system may include an electromechanical 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 condition or a cardiac outcome, 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 of a cardiac intervention for the user, and transmit the treatment plan to cause the electromechanical machine to implement the one or more exercises.

A method for optimizing at least one exercise. An exercise apparatus is configured to enable a user to perform the at least one exercise. The method includes receiving user data. The method includes generating, based on the user data, initial target data. The method includes receiving measurement data associated with one or more sensors. The method includes determining, differential data. The determining is based on one or more differences between the initial target data and the measurement data. The method includes receiving cohort data. The method includes generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate message data based on a difference between the differential data and the cohort data. The method includes transmitting, to an interface associated with a user, a message to the user based on the message data.

A method includes receiving treatment data associated with a user capable of using a treatment device to perform a treatment plan. The method also includes receiving alignment data associated with the user while the user engages in at least one activity and receiving at least one alignment characteristic associated with the user and determining, using at least the at least one alignment characteristic, whether the at least one alignment characteristic correlates with at least one secondary condition of the user. The method also includes generating secondary condition information indicating at least the secondary condition and modifying at least one aspect of the treatment plan in response to receiving, from a healthcare professional, treatment plan input. The treatment plan input includes at least one modification to the at least one aspect of the treatment plan and wherein, further, the treatment plan input is generated based on the secondary condition information.

An improved method of physiotherapy includes loading muscles of a torso of a user with a torque acting substantially about a vertical axis; and the user using an exercise equipment while the torso is loaded with the torque.

A method that includes receiving treatment data associated with a user capable of using a treatment device to perform a treatment plan. The method also includes receiving user related data (URD) associated with the use and receiving alignment data associated with the user while the user engages in at least one activity. The method also includes identifying, based on at least the treatment data, the URD, and the alignment data, at least one alignment characteristic associated with the user and modifying at least one aspect of the treatment plan in response to receiving, from a healthcare professional, treatment plan input including at least one modification to the at least one aspect of the treatment plan.

A method for optimizing at least one exercise for a first user. The method includes receiving first user data including attribute data associated with the first user and outcome data associated with the exercise. The method includes generating, based on the first user data, initial target data. The initial target data is associated with at least one of the first user, the exercise apparatus, and the exercise. The method includes receiving measurement data associated with at least one of the first user, the exercise apparatus, and the exercise. The measurement data is associated with one or more sensors. The method includes determining differential data based on one or more differences between the initial target data and the measurement data. The method includes generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate optimized target data.

A method for updating a treatment plan. The treatment plan is associated with a user using a treatment apparatus to perform the treatment plan. The method includes receiving first data associated with a first diagnosis of the user. The method includes generating, based on the first data, an initial treatment plan to be performed on the treatment apparatus by the user. The method includes receiving second data associated with a first attribute of the user. The method includes generating, via an artificial intelligence engine, a machine learning model trained to generate an updated treatment plan based on the initial treatment plan and the second data.

An improved exercise equipment including a stationary exercise apparatus, a lever arm pivotably biased about a pivot axis, and a support for supporting the lever arm above the stationary exercise apparatus. The pivot axis extends substantially in a vertical direction. The stationary exercise apparatus is of a type to cause the legs of a user to scissor as the user operates the stationary exercise apparatus. In an exemplary embodiment, the stationary exercise apparatus is a treadmill.