Muscle to Machine: Surface Electromyography for a Robot Control

Abstract

During muscle activation, the surface Electromyography, sEMG, electrical signal is produced from small electrical currents generated by the exchange of ions across the muscle membranes and detected by electrodes. During a muscular activity, the brain sends excitation signals through the nervous system to a group of motor units which are the junction points between the neuron and the muscle fibers. As a result, each motor unit produces a ‘Motor Unit Action Potential’ (MUAP). This process is, continuously, repeated as long as the muscle is required to generate a force, producing a train of action potentials. The trains from concurrently active motor units superimpose to produce the resultant EMG signal. A group of muscles are involved in a certain movement of the human body. For a specific activity, there is a direct proportionality between the number of muscles, force, excitation from the nervous system, number of motor units, and firing rate. The bioelectric EMG signal has a wide range of applications such as a diagnostic and evaluation tool for neurological disorders, low back pain, physiotherapy, rehabilitation, sports, biofeedback and ergonomics research. Recently, EMG has found its use in the robotics field. A robotic mechanism can be effectively controlled by an EMG signal. The advances in electronics and microcontroller technology such as filtration, rectification, and amplification, improved the control options for robotic mechanisms. In this sense, we propose a design and implementation of an EMG data acquisition system with the Myoware device and a microcontroller. This thesis discusses, in detail, the effective use of sEMG as a tool for controlling a robotic hand. A detailed elaboration of the electrode types, signal acquisition technique, electronics circuit design considerations and the control procedure to drive electric motors in a robotic hand is provided. The MATLAB is used to analyze the acquired non-invasive signal.