A scientifically controlled exercise based on a measurement of maximum voluntary contraction (MVC). A mechanical apparatus includes a sensor, an actuator, and a processor. The apparatus receives a mechanical exertion from a user while the processor receives signals from the sensor and sends signals to the actuator to control the mechanical apparatus. The processor measures a MVC exerted by a user and determines a protocol for the exercise based on the measured MVC. The protocol includes a specified exertion to be performed by a user, the specified force and velocity profile governing the exertion, and a specified sequence of repetitions of the exertion, spaced by rest periods. The protocol includes real-time feedback to the user related to compliance with the protocol. The methodology and equipment described herein provides users a safe and effective means of improving muscular strength or endurance and ameliorating various neurological or physiological conditions.

A method is provided for monitoring and managing muscle activity and soft tissue loading. The method includes providing to a subject a plurality of sensors for measuring muscle activity and soft tissue loading levels; directing the subject to undertake a program of exercise; measuring muscle activity and soft tissue loading during the program of exercise; comparing the measured muscle activity and soft tissue loading levels against calibrated muscle activity and soft tissue loading levels for the subject; and alerting the subject if the comparison of measured muscle activity and soft tissue loading levels against calibrated muscle activity and soft tissue loading levels indicates that a desirable level of muscle activity and/or soft tissue loading is being exceeded.

An Internet of Thing (IoT) device includes a body with a processor, a camera and a wireless transceiver coupled to the processor.

A resistive therapeutic device is disclosed such that upon a user applying torque a controller emits a signal to an electromagnetic resistance generating member. The controller detects a maximum torque applied by a user detects if the maximum torque applied by a user is less than or equal to a resistance setting. The torque resistance is variable from a neutral position to a maximum position, and the controller increases the resistance setting as a user moves a torque application member in a direction away from the neutral position that represents an increase in torque applied by the user. A control and user interface for a resistive therapeutic device includes an evaluation display of torque strength applied and displays an angle of rotation clockwise or counterclockwise directions ranging from 0 degrees to 360 degrees and a count of repetitive times a user has rotated at least 360 degrees in either direction.

A testing and training apparatus, comprising: an upright frame; an instrumentation support that is supported by the upright frame so as to be adjustable in height; and instrumentation mountable on the instrumentation support. The instrumentation comprises a plurality of force sensors, and is rotatable relative to the upright frame, and the apparatus is controllable to output data signals indicative of force detected by the force sensors.

Systems, methods, and computer-readable mediums for operating an electromechanical device are disclosed. The system includes, in one example, the electromechanical device, a patient portal, and a computing device. The computing device is configured to receive user data relating to a user, and receive treatment data relating to treatment plans and outcomes. The computing device is also configured to generate a prehabilitation plan by using a machine learning model to process the user data and the treatment data. The computing device is further configured to select, for the electromechanical device, an electromechanical device configuration that enables exercises of the prehabilitation plan to be performed by the user such that performance improves an area of the user's body. The computing device is also configured to enable the electromechanical device to implement the electromechanical device configuration.

A weight machine sensor includes a force sensor, a position sensor, and a processor. The force sensor is programmed to output a force signal representing a force applied to a pulley-disposed on a cable incorporated into exercise equipment having a stack of weights. The position sensor is programmed to detect motion of the stack of weights and output a position signal representing the motion detected. The processor is programmed to receive the force signal and the rotation signal and determine, from the force signal and the position signal, exercise data including an amount of exercise resistance and a number of repetitions performed.

In aspects of a wireless hand sensory apparatus for weight monitoring, a wearable article is worn by a user who moves items. A tracking system is implemented in the wearable article, and the tracking system includes a force sensor, or force sensors, in the wearable article to register a force on an item. The tracking system includes tracking logic that determines a weight of the item based on the force on the item. The tracking system may also include a motion sensor to sense motion of the wearable article, and the tracking logic determines how the item is moved based on the motion of the wearable article. The tracking logic can also determine the weight of the item based on the force on the item in combination with a speed of the motion of the wearable article.

Provided is a method for evaluating muscle strength characteristics of a limb based on a muscle group model including a first pair of antagonistic one-joint muscles, a second pair of antagonistic one-joint muscles, and a pair of antagonistic two-joint muscles, where the limb has a first rod having a proximal end supported by a first joint and a second rod supported on a free end of the first rod through a second joint. The method includes: measuring a maximum output of a free end of the second rod in at least one predetermined direction; measuring orbiting outputs of the free end of the second rod in all directions in the plane; and creating a hexagonal maximum output distribution corresponding to a contribution amount of each muscle of the muscle group model based on the maximum output in the predetermined direction and the orbiting outputs.

A training apparatus includes an operating rod, a strength detector, a motion position detector, a strength speed calculator, a boundary line arrival speed calculator, and a motion speed calculator. The operating rod moves a held limb. The strength detector outputs a strength component signal based on a magnitude of a strength component. The motion position detector detects a motion position of the operating rod. The strength speed calculator calculates a strength speed. The boundary line arrival speed calculator calculates a boundary line arrival speed whose absolute value is smaller as a boundary line distance is shorter. The motion speed calculator calculates a lower one of the strength speed and the boundary line arrival speed as the motion speed at which the operating rod should move.