Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals

Abstract

To perform basic hand movements, a hand amputee person needs an exoskeleton prosthetic hand (EPH). The EPH can be controlled through electroencephalogram (EEG) or electromyogram (EMG) signals. The EMG signals are preferred as they are acquired from surface of forearm and termed as surface EMG (sEMG). It is very challenging to design the control section for EPH. It should be able to classify different hand movements accurately based on the acquired sEMG signals. Also the sEMG signals must be acquired from minimum number of electrodes to make EPH cost-effective. In this paper, we have proposed a novel technique to classify the basic hand movements. The method proposed in this paper applies tunable-Q wavelet transform (TQWT) based filter-bank (TQWT-FB) for decomposition of cross-covariance of sEMG (csEMG) signals. Then, Kraskov entropy (KRE) features are extracted and ranked. The proposed method is tested on the data obtained from five subjects and achieved the average classification accuracy (CA) of 98.55% using k-nearest neighbour (k-NN) classifier. Therefore, our developed prototype is available for further validation using larger diverse data.