The Walking Assistance System using the Lower Limb Exoskeleton Suit Commanded by Backpropagation Neural Network

Currently there are many elderly people who have walking problems. This paper aims to develop and solve these problems by introducing walking assistance system which can recognize 3 types of gestures, include walking, sitting and standing. Our system is divided into 3 main parts including Feature extraction which consists of Time domain and Frequency domain, Classification and Exoskeleton suit system. Conjugate Gradient Backpropagation Neural Network is used to classify sEMG signal of lower limb posture after extracted the features. Then the output of classification is used to command the Exoskeleton suit to perform the gesture according to the results of the recognition. In addition, our paper uses PID controller to control DC motor of Four Bar Linkages Mechanisms of Lower Limb Exoskeleton suit in order to reduce the number of motors and increase stability during the Stance Phase. The results from the experiment have concluded that all feature in time domain has the most recognition rate which up to 99.39%.