Pattern recognition of number gestures based on a wireless surface EMG system

• Published in: Biomedical signal processing and control Vol. 8; no. 2; pp. 184 - 192
• Main Authors: Chen, Xun, Wang, Z. Jane
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2013
• Subjects: Classification; Feature extraction; Multiple kernel learning; Number gestures; Multiple kernel learning; Feature extraction; Number gestures; Classification
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2012.08.005

► We investigated 3 most popular feature sets and 4 classifiers for sEMG analysis. ► We proposed employing the MKL-SVM method and obtained super results. ► We proposed a novel application, number gesture recognition. ► We implemented a real-time system for the proposed application. Using surface electromyography (sEMG) signal for efficient recognition of hand gestures has attracted increasing attention during the last decade, with most previous work being focused on recognition of upper arm and gross hand movements and some work on the classification of individual finger movements such as finger typing tasks. However, relatively few investigations can be found in the literature for automatic classification of multiple finger movements such as finger number gestures. This paper focuses on the recognition of number gestures based on a 4-channel wireless sEMG system. We investigate the effects of three popular feature types (i.e. Hudgins’ time–domain features (TD), autocorrelation and cross-correlation coefficients (ACCC) and spectral power magnitudes (SPM)) and four popular classification algorithms (i.e. k-nearest neighbor (k-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM)) in offline recognition. Motivated by the good performance of SVM, we further propose combining the three features and employing a new classification method, multiple kernel learning SVM (MKL-SVM). Real sEMG results from six subjects show that all combinations, except k-NN or LDA using ACCC features, can achieve above 91% average recognition accuracy, and the highest accuracy is 97.93% achieved by the proposed MKL-SVM method using the three feature combination (3F). Referring to the offline recognition results, we also implement a real-time recognition system. Our results show that all six subjects can achieve a real-time recognition accuracy higher than 90%. The number gestures are therefore promising for practical applications such as human–computer interaction (HCI).