Electromyography (EMG) based signal analysis for physiological device application in lower limb rehabilitation

Electromyography (EMG) is an experiment-based method for evaluating and recording a series of electrical signals that emanate from body muscles. The electrical manifestation of neuromuscular activation generated in muscles during contraction and/or relaxation is known as EMG signals. In this paper, a preliminary study is conducted in order to improve the fitness of post-stroke survivors with a minimal supervision from therapists in physiological activity especially on the lower limb rehabilitation. Therefore, a pattern recognition technique is required to extract the important features of an EMG signal to control the physiological devices (PDs), for instance, cycling-like and stepping-like machines in a lower limb rehab application. A new approach for feature extraction vectors in a recognition system will be proposed using Discrete Wavelet Transform (DWT) and Fuzzy C-Means (FCM) algorithms. In addition to this, a Principle Component Analysis (PCA) method will be utilized to reduce the dimension of data in prior to computing the classification accuracy using the Adaptive Neuro-Fuzzy Inference System (ANFIS).