An orthostatic hypotension alleviation device comprising an outer casing comprising an aperture extending therethrough; a carriage arranged within the outer casing and configured to be reciprocally displaced towards and away from the aperture along a carriage displacement axis; a spindle arranged on the carriage and configured to rotate about a spindle axis; a tether at least partially wound around the spindle, the tether comprising a distal end extending through the aperture, the tether being arranged such that it can be extended from a retracted configuration to an extended configuration in which the tether is fully extended by pulling the distal end away from the aperture so rotating the spindle about the spindle axis; a spindle biasing mechanism connected to the spindle and configured to apply a biasing torque to the spindle about the spindle axis so as to bias the tether towards the retracted configuration; and, a carriage biasing mechanism arranged within the outer casing connected to the carriage and configured to bias the carriage away from the aperture.

Systems and methods for electronically creating and modifying a fitness plan are disclosed. The method may include receiving electronic user data, collecting electronic fitness data, and displaying a suggestion for a fitness activity based on the electronic user data and the electronic fitness data.

A method is disclosed for using an artificial intelligence engine to modify resistance of pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive measurements as input, and outputting, based on the measurements, a control instruction that causes the exercise device to modify, independently from each other, the resistance of the pedals. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the pedals. The method includes determining, based on the measurements, a quantifiable or qualitative modification to the resistance provided by a pedal of the pedals. The resistance provided by another pedal of the pedals is not modified. The method includes transmitting the control instruction to the exercise device to cause the resistance provided by the pedal to be modified.

A monitoring system includes a shoe and a sole integrated into the shoe. The monitoring system also includes a connection mechanism attached to an underside of the sole and is shaped to connect the sole to a pedal. A pressure sensor is incorporated into the shoe that senses a force exerted on the pedal when the shoe is connected to the pedal through the connection mechanism.

A patient can undergo physical therapy to rehabilitate a musculoskeletal condition. The success of the rehabilitation relies in part on whether the patient uses an exercise band with the correct resistance for their condition. In order to select the correct resistance, a controller can instruct the patient to perform a directed movement with an exercise band, receive exercise data from at least one sensor (e.g., a camera, like a front-facing camera, that records the patient, a plurality of sensors on or near the patient's skin, etc.) as the subject performs the directed movement, and calculate a pull force exerted by the patient on the exercise band based on at least a portion of the exercise data. The adequacy of an exercise with the exercise band for the patient is determined based on the pull force can be determined based on the calculated pull force.

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 treadmill includes a frame, an exercise deck attached to the frame, and a first handle movably attached to the frame. The first handle has a first orientation where the first handle is positioned within a region above the exercise deck and stabilized to support a user's weight during a body weight exercise, and a second orientation where the first handle is positioned away from the region above the exercise deck.

Systems and methods for hypoxia delivery are provided. An apparatus for providing intermittent normoxia and hypoxia intervals includes a breathing component, a normoxia fluid source, a hypoxia fluid source, a valve, and a control system. The valve is configured to disrupt flow from at least one of the normoxia fluid source and the hypoxia fluid source and the control system is configured to cause the at least one valve to switch between delivery of fluid from the normoxia fluid source and the hypoxia fluid source while maintaining positive pressure at the breathing component.

Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, an exercise program comprising a first user energy score, wherein the method comprises generating, by the artificial intelligence engine, the exercise program including an exercise plan including a plurality of exercises. Each respective exercise is associated with user energy consumption metrics based at least on a metabolic equivalent of task (MET) value, and based on the user energy consumption metrics, the first user energy score is associated with the exercise program. The method includes receiving data pertaining to a plurality of users. The data includes physical activity goals the plurality of users desires to achieve. The method includes determining second user energy scores for the physical activity goals, and based on the first and second user energy scores, assigning, by the artificial intelligence engine, at least a subset of the plurality of users to the exercise program.

An intelligent system that automatically adjusting optimal rehabilitation intensity or exercise volume with personalized exercise prescription, comprising: upload the physiological information data measured by registered members to a cloud data integration server through the Internet; an expert diagnostic unit, which can download and use the member's physiological information data from the cloud data integration server. After the diagnosis, a personalized exercise prescription is issued and uploaded to the cloud data integration server; a rehabilitation fitness equipment unit can download the exercise prescription from the cloud data integration server to control and automatically adjust the optimal rehabilitation intensity or exercise volume; after being diagnosed by experts such as doctors, rehabilitators or fitness coaches, the optimal parameter values of a continuously updated artificial intelligence exercise prescription can be used to improve the efficacy of personal rehabilitation or exercise fitness.