Automated Recognition of Hand Grasps Using Electromyography Signal Based on LWT and DTCWT of Wavelet Energy

• Published in: International journal of computational intelligence systems Vol. 13; no. 1; pp. 1027 - 1035
• Main Authors: Haiter Lenin, A., Vasanthi, S. Mary, Jayasree, T.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2020; Springer Nature B.V; Springer
• Subjects: Adaptive algorithms; Back propagation networks; Discrete wavelet transform; Electromyogram; Electromyography; Feature extraction; Machine learning; Myoelectricity; Neural networks; Noise reduction; Pattern recognition; Performance enhancement; Principal components analysis; Research Article; Signal processing; Support vector machine; Support vector machines; Wavelet analysis; Wavelet transforms; Deep Learning Neural Network; Discrete wavelet transform; Signal processing; Electromyogram; Feature extraction; Pattern recognition; Support vector machine
• ISSN: 1875-6891; 1875-6883; 1875-6883
• DOI: 10.2991/ijcis.d.200724.001

This paper presents a novel framework that automatically classifies hand grasps using Electromyogram (EMG) signals based on advanced Wavelet Transform (WT). This method is motivated by the observation that there lies a unique correlation between different samples of the signal at various frequency levels obtained by Discrete WT. In the proposed approach, EMG signals captured from the subjects are subjected to denoising using symlet wavelets, followed by Principal Component Analysis (PCA) for dimensionality reduction. Further, the important attributes of the signal are extracted using Lifting Wavelet Transform (LWT) and Dual Tree Complex WT (DTCWT). Multiple classifiers such as Feed Forward Neural Networks (FFNN), Cascaded Feed Forward Neural Networks (CFNN), Support Vector Machine (SVM) and Deep Learning Neural Network (DLNN) are used for classification. The simulation results are compared with various training algorithms and it is observed that DTCWT features combined with CFNN and trained with Gradient Descent with Adaptive Back Propagation (GDABP) algorithm achieved the best performance. The advantages of the proposed method were proved by comparing with the earlier conventional methods, in terms of recognition performance. These experimental results prove that the proposed method gives a potential performance in the recognition of hand grasps using EMG signals. In addition, the proposed method supports clinicians to improve the performance of myoelectric pattern recognition.