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.

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.

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.

A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan, a patient interface. and a computing device configured to: receive treatment data pertaining to the user who uses the treatment device to perform the treatment plan; identify at least one enhanced component using the treatment data; generate an enhanced environment using the at least one enhanced component and the treatment plan; output at least one aspect of the enhanced environment to at least one of the patient interface and another interface; receive subsequent treatment data pertaining to the user; and selectively modify at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan using the subsequent treatment data.

Systems and methods directed to handheld respiratory diagnostics, trainings, and therapy are disclosed. More specifically, a device that collects data associated with a user's breathing using sensors, analyzes that data to determine time series readings of one or more health-related vitals, and generates trainings and therapies to assist the user in real time is described.

An adjustably tensioned, transverse-suspended elastic element exercise device is disclosed. The transverse element is suspended between rigidly mounted frame or arm elements and sized so as to enable full range of motion exercises. The tension and height of the transverse elastic member can be adjusted to accommodate varying exercise levels and positions. The assembly may include handgrips and attachments to augment workouts. Electronic adjustability, metrics, programmability and Internet connectivity enhance the user experience. A method of using the device is also disclosed.

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.

A method is disclosed for using an artificial intelligence engine to modify resistance of one or more pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the exercise device to modify the resistance of the one or more pedals. The method includes receiving the one or more measurements from a sensor associated with the one or more pedals of the exercise device, determining whether the one or more measurements satisfy a trigger condition, and responsive to determining that the one or more measurements satisfy the trigger condition, transmitting the control instruction to the exercise device.

A system, method, and wireless earpieces for implementing a virtual assistant. A request is received from a user to be implemented by wireless earpieces. A virtual assistant is executed on the wireless earpieces. An action is implemented to fulfill the request utilizing the virtual assistant. The wireless earpieces may be a set of wireless earpieces and the virtual assistant may be implemented independently by the wireless earpieces.