Case Study: Influence of Muscle Fatigue and Perspiration on the Recognition of the EMG Signal

Abstract

EMG data processing and muscle activity recognition has become the most popular method for upper limb prosthetics. The high sensitivity of EMG sensors with respect to external disturbances and other factors prevent from accurate muscle activity recognition. The aim of the paper is to investigate robustness of window recognition method with respect to muscle fatigue and perspiration of the forearm skin. The current experiment was carried out using Arduino nano microcontroller connected to EMG sensors. The subject under study is a healthy man of 26 years old with an average build. The subject was asked to do physical exercises, thereby loading the muscles of the fingers of the hand to achieve partial or complete fatigue and perspiration. During the whole process, EMG sensors have installed on the subject and transmitted the signal to the computer using Arduino. All signal processing is done directly on the computer with a pre-recorded signal. Experimental results have been shown that with the appearance of external factors during prosthesis operation recognition accuracy may degrade to unsatisfactory. False positives occur with perspiration of skin surface and complete muscle fatigue. An algorithm for automatic self-correction of the boundaries of motion detection zones has been introduced. Instead of identification of causes that leads to performance degradation, we use correction scheduling started by timer. Experimental results have shown that proposed automatic adaptive correction is effective. Despite higher recognition delay, proposed auto-tuning method provides satisfactory muscle activity identification and feature extraction in real-time.