Electromyography-based gesture recognition for quadriplegic users using hidden Markov model with improved particle swarm optimization

Abstract

People with quadriplegia cannot move their body and limbs freely, making them unable to interact normally with their environment. This article aims to improve the life quality of quadriplegia patients through a development of a system to help them interact with their surroundings. A novel algorithm to classify human gestures is proposed in this article. The algorithm is developed as the core of an assistive technology system in the form of a human interface device, which utilizes electromyograph as its sensor. The system utilizes a wearable electromyograph with a custom software as the signal capturing and processing tool. The electrodes of the electromyograph are placed on certain positions on the face, corresponding to the locations of the major muscles that govern certain facial gestures. The signals are then processed using a novel algorithm that employs hidden Markov model and improved particle swarm optimization to classify the gesture. Based on the gestures, a custom command can be assigned for different conditions. The accuracy of the system is 96.25% for five gestures classification.