Clench Strength Prediction for Prosthesis Hand Using Surface Electromyogram

Abstract

Hand is the main environmental manipulator for humans. Therefore, any accidental lost of hand does a great harm in amputee life. Only choice left that he or she is using prosthesis, which is dated back to ancient time. Due to their usefulness prosthesis is also used in modern days. Now-a-day robotic rehabilitation opens a new era in the field of prosthesis. They are more capable of doing things compared to their ancestors. However, to duplicate the complex work of hand, the robotic hand is needed to complete two basic operations first one is predict the angular displacement and another one is the estimation of force. Estimation of force is essential for instance when drinking from a glass, one needs to apply sufficient grip force to prevent the object from slipping Out of the hand. In addition, one needs to control the total torque exerted on the glass such that the glass remains vertical. Usually, the requirements for grip force stabilization allow for some laxity, while the requirements for total torque production are highly specified. The grip force needs only to be larger than the slip threshold and smaller than the force that would break the object. Different researchers used different approach to solve this problem like body-powered prostheses, force-sensitive resistors. Nevertheless, when it comes to rehabilitation of amputee they are not up to the mark in practical field.Using of Electromyography (EMG) in the robotic rehabilitation shows some hope. Muscle tissue conducts electrical potentials similar to the way nerves do and the name given to these electrical signals is the muscle action potential. A muscle is composed of bundles of specialized cells capable of contraction and relaxation. The primary function of these specialized cells is to generate forces, movements and the ability to communicate such as speech or writing or other modes of expression. The skeletal muscle tissue is attached to the bone and its contraction is responsible for supporting and moving the skeleton. The contraction of skeletal muscle is initiated by impulses in the neurons to the muscle and is usually under voluntary control. Skeletal muscle fibers are well-supplied with neurons for its contraction. This depolarization, accompanied by a movement of ions, generates an electric field near each muscle fiber. An EMG signal is the train of Motor Unit Action Potential (MUAP) showing the muscle response to neural stimulation. There is a clear relationship between force and EMG. The higher the muscle force, the higher EMG level is developed. Clench force estimation is highly desirable in the field of prosthesis hand. It is one of the most used postures among different types of postures. In this thesis, the author proposes to estimate the clench force using two types of Surface Electromyography (SEMG): rectified SEMG and integrated SEMG. An Artificial Neural Network (ANN) is used to estimate the force from SEMG. For weight adjustment of the estimator Levenberg-Marquardt (L-M) back propagation algorithm is used. The proposed network is trained and tested using SEMG recorded from five subjects. The estimation result clearly shows that integrated SEMG performed 3.53 times better than rectified SEMG in the case of cross correlation coefficient. So integrated SEMG is recommended for clench force estimation. A neural number based experiment is also done to find the optimal number of neurons in hidden layer, which are five neurons as previously described neural network. In addition to that, these result also compared with Support Vector Machine (S\TM). Same result is found for integrated SEMG. In the case of rectified SEMG ANN is more suitable than SVM.