Estimation of Arm Joint Angles from Surface Electromyography signals using Artificial Neural Networks

Vicon system is implemented in almost every motion analysis systems. It has many applications like robotics, gaming, virtual reality and animated movies. The motion and orientation plays an important role in the above mentioned applications. In this paper we propose a method to estimate arm joint angles from surface Electromyography (s-EMG) signals using Artificial Neural Network (ANN). The neural network is trained with EMG data from wrist flexion and extension action as input and joint angle values from the vicon system as target. The results shown in this paper illustrate the neural network performance in estimating the joint angle values during offline testing.