Electromyography (EMG) is a method for monitoring the electrical activity of skeletal muscles. The EMG signal is used to diagnose neuromuscular diseases and muscular injuries. EMG can also be utilized as an indicator of user intent for a muscle contraction for a specific motion. This input signal could be used to control powered exoskeleton prostheses. Limbs with impaired motor function tend to have increased disuse that may result in further muscle weakness. Therapy and other physical activities that increase the use of an impaired limb may contribute to some recovery of motor function. A device that helps to perform activities of daily living may increase usage and enhance recovery. The objective of this project is to make developments toward an EMG controlled assistive rehabilitation system that monitors EMG signals of the bicep and triceps muscles, and drives a motor to assist with arm motion. A motor is used to develop torque that would assist rotations of the arm about the elbow. A pair of EMG sensors (one pair near the biceps and the other near the triceps muscle) transmits electrical activity of the arm to a microcontroller (Raspberry Pi, Raspberry Pi Foundation, United Kingdom). For the prototype, the EMG signal is sampled and rectified within a moving time window to determine the root mean squared (VRMS) value. This value is used by the microcontroller to generate a pulse-width modulated (PWM) signal that controls the motor. Sensors for the motor provide information to an algorithm on the microcontroller. The generated PWM signal is based on the Vrms values for the EMG signal. Testing and analysis has shown a correlation between the EMG Vrms amplitude and muscle generated torque. The EMG controlled assistive rehabilitation system shows promise for assisting motor function for rotations about the elbow. Further algorithmic development is needed to determine the appropriate amount of assistance from the motor for the motor function indicated by user intent.

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