Application and Improvement of MFCC in Gesture Recognition with Surface Electromyography

As a physiological signal reflecting the state of muscle activation, surface electromyography (sEMG) plays a vital role in the assessment of neuromuscular health, human–computer interaction, and gait analysis. Inspired by the audio signal analysis outcome that features extracted with Mel Frequency Cepstral Coefficient (MFCC) empower better representation, this paper proposes a comparative study of a gesture recognition method by using and improving with the MFCC features of sEMG. Comparing and combining with the conventional time-domain and frequency-domain features, different learning-based techniques are deployed to evaluate the performance of the proposed approach on the NinaPro datasets. The proposed approach was evaluated on the NinaPro-DB1 and NinaPro-DB2 datasets, achieving the improvements of 3.42% and 3.67%, respectively, in terms of the highest accuracy using the standard MFCC method. Correspondingly, when combined with the improved MFCC, the accuracy was further increased, reaching the maximum values of 89.82% and 87.82%, respectively, on the two datasets. The impact on the performance reveals the effectiveness of MFCC, and the results show that the proposed method has the potential to realize high-precision gesture recognition.