Wearable EMG Sensors for Smart Applications

Dear Colleagues,

Electromyography (EMG) is used to show biological signals consisting of electrical activity produced by skeletal muscles. This has several applications in motor control, motor learning, biofeedback, biomechanics, neuromuscular physiology, movement disorders, and physical therapy. The clinical applications of EMG date back to as early as the1950s. Since then, several advancements have been made in the design and implementation of EMG sensors and, also, in post-processing and real-time processing of EMG signals. With these advancements, EMG has found applications in recent applications such as human–machine interfaces, prosthesis and exoskeleton control, powered wheelchair control, stress and fatigue measurements, and many other clinical applications. Fast forward to today’s applications, wearable EMG sensors are found everywhere in smart applications to improve health and wellbeing.   

The design and development of EMG sensors has resulted in their evolution over the years from being bulky wired cumbersome invasive electrodes to wireless wearable high-density noninvasive and completely safe sleeves with arrays of multiple electrodes. Meanwhile, the post-processing and real-time processing algorithms have seen developments with improved pattern recognition, thanks to recent advances in artificial intelligence, deep learning, machine learning, and signal processing. Consequently, with the coming of new age technologies and internet of things, several smart applications of EMG sensors have arisen in the areas of myoelectric control of robots and exoskeletons, clinical applications in telemedicine and telehealth, and wearable technologies to monitor everyday physical activity, stress, fatigue, and overall health and wellbeing.

The aim of this Special Issue is to compile the contributions of current leading researchers in the following areas: (1) the design and development of wearable EMG sensors; (2) the post-processing and real-time processing of EMG signals using artificial intelligence, deep learning, and machine learning; and (3) the smart applications of these wearable EMG sensors.

Dr. Ramana Kumar Vinjamuri
Guest Editor

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