Abstract
This paper analyses the different walking speed of a person for torque detection by using the Electromyography (EMG) signals. EMG measures electrical activity and the muscle response to a nerve stimulation. The reduced ability to move due to variation in tendon structured muscle in lower limb joints of legs affects the overall activity of the human lifecycle. This defect makes it difficult to move causing severe pain resulting in disability. In order to overcome this problem, exoskeleton technology is implemented where Electromyography (EMG) signals are collected from electric impulse generated during the muscle movements. From these electric impulses, the torque present in EMG signals are detected and classified accordingly. The first step consists of pre processing EMG signals using Infinite Impulse Response (IIR) filter. Secondly features are extracted from pre processed signals by using Continuous Wavelet Transform (CWT) methods and classified using Convolutional Neural Networks (CNN) classifier. From this classification, the rotational movement of muscles named torque in processed EMG signal is detected. Form detected torque, the normal muscle system and problematic muscle system are differentiated
Published Version
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