Estimation of hand force from surface Electromyography signals using Artificial Neural Network

Abstract

Haptic technology has many real world applications such as rehabilitation robotics, telepresence surgery, gaming, virtual reality and human-robot interaction. Force plays an important role in the above mentioned haptic applications. In this paper, we propose a method to estimate force from surface Electromyography (SEMG) signals using Artificial Neural Network (ANN). The haptic device is modeled to act as a virtual spring. The neural network is trained with EMG data from wrist flexion action as input and force values from the haptic device as target. The results shown in this paper illustrate the neural network performance in estimating the force values in real-time.