Abstract-Electromyography

Hand grip force and motion pattern classification using bio signal such as Electromyogram (EMG) has been very important in current studies. EMG based pattern classification has gain the utmost consideration especially in the commercial prostheses. Developing an intuitive hand control with fast response both in time and space are the major challenges. These challenges are due to the lack of information gathered from adjacent muscles. The study of adjacent muscles is crucially needed as it will allow to provide optimised hand grip and motion pattern classification without redundancy in the use of muscle information. The main aim of this paper is to investigate the effect of two adjacent flexor muscles; flexor digitorum superficial (FDS) and flexor carpi radialis (FCR), two adjacent extensor muscles: extensor carpi radialis longus (ECRL) and extensor digitorum communis (EDC) providing the perspective view of individual muscle performance compared to their adjacent muscle with respect to finger pinch and hand grip force. Practical classification results prove the significance of the study, both adjacent muscles perform almost similar with approximately 95% of similarities across different subjects. The results achieved lead to the conclusion, that the use of adjacent muscles can be reduced to only single muscle channel providing an optimised data for pattern recognition or classification.

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Features extraction is important for electromyography (EMG) signal analysis. The paper's objective is to evaluate the features extraction of the EMG signal. The experimental setup for EMG signal acquisition followed the procedures recommended by Europe's Surface Electromyography for Non-invasive Assessment of Muscle (SENIAM) project. The EMG signal's data were analysed in the time domain to get the features. Four features were considered based on the analysis, which are IEMG, MAV, VAR and RMS. The average muscle force condition can be estimated by correlation between the EMG voltage amplitude with linear estimation with the full-wave rectification method. The R-squared value determined the correlation between the EMG voltage amplitude with the loads. IEMG was chosen as the reference feature for estimation of the muscle's force due to its R-squared value equal to 0.997. By referring to the IEMG, the linear equation obtained from the correlation was used for estimation of the muscle's force. These findings can be integrated to design a muscle force model based on the biceps muscle.

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