Abstract—Biomedical Signal Processing is one of the key areas in Prosthetics. Electromyogram (EMG) signals are used in Prosthetic designing due to good corporation with biomechanics of human body. The aim of this research is to analyze Surface Electromyogram (SEMG) signal parameters related to upper limb speed and flexion angle for one test subject. SEMG signal acquisition was carried out noninvasively for upper limb elbow flexion with minimal ethical issues. Captured Surface EMG signals were amplified by INA128 amplifier IC and filtered by UAF42 filter IC into 0Hz-500Hz frequency range. Beaglebone Black digital signal processing unit was interfaced with MATLAB R2015a Simulink platform for processing of SEMG signals. Offline SEMG signal speed classification was done using Fast Fourier Transformation and Wavelet Transformation along with MATLAB R2015a software to classify elbow flexion with respect to speed. Graphical representation of Amplitude variations in each transformation results were able to distinguish the fast elbow flexion and slow elbow flexion. . Flexion angle was approximately calculated by goniometer and data were acquired using Arduino ATMEGA 2560 microcontroller. Applying Curve fitting algorithm to correlate SEMG signals with flexion angle will be the future studies. Ultimate goal will be a generalized algorithm for speed classification.

Index Terms—Elbow flexion, electromyography, flexion angle, speed analysis.

The authors are with Department of Electrical and Electronic Engineering, General Sir John Kotelawala Defence University, Ratmalana, Sri Lanka (email: kgswins@gmail.com).

Cite: Kasun Samarawickrama, Sadun Ranasinghe, Yasoja Wickramasinghe, Wageesha Mallehevidana, Vidarshi Marasinghe and Kanchana Wijesinghe, "Surface EMG Signal Acquisition Analysis and Classification for the Operation of a Prosthetic Limb," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 8, no. 1, pp. 32-41, 2018.