Spectral Collaborative Representation based Classification for hand gestures recognition on electromyography signals. (February 2016)

• Record Type: Journal Article
• Authors: Boyali, Ali; Hashimoto, Naohisa
• Is Part Of: Biomedical signal processing and control. Volume 24:(2015)
• Page Start: 11
• Page End: 18
• Publication Date: 2016-02
• Subjects: 00-01 -- 99-00; EMG gesture -- Continuous gesture recognition -- Spectral representation -- Gesture training matrix -- MYO armband; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.09.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: Spectral Collaborative Representation based Classification (S-CRC) method is proposed. S-CRC yields overwhelming pattern recognition accuracy over 97% for stochastic signals. S-CRC is tested for EMG signal pattern recognition for 10 hand gestures. The recognition is performed in a continuous manner eliminating to pattern spotting. The methods introduced allow on the spot training, thus it can be used for other applications. The SCRC is a fast classification method, it can be used for mobile computing environments. Abstract: The classification of the bio-signal has been used for various purposes in the literature as they are versatile in diagnosis of anomalies, improvement of overall health and sport performance and creating intuitive human computer interfaces. However, automatic identification of the signal patterns on a streaming real-time signal requires a series of complex procedures. A plethora of heuristic methods, such as neural networks and fuzzy systems, have been proposed as a solution. These methods stipulate certain conditions, such as preconditioning the signals, manual feature selection and large number of training samples. In this study, we introduce a novel variant and application of the Collaborative Representation based Classification (CRC) in spectral domain for recognition of hand gestures using raw surface electromyography (EMG) signals. The CRC based methods do not require large number of training samples for an efficient pattern classification. Additionally, we present a training procedure in which a high end subspace clustering method is employed for clustering the representative samples into their corresponding class labels. Thereby, the need for feature extraction and spotting patterns manually on the training samples is obviated. We presented the intuitive use of spectral features via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize gestures with higher levels of accuracy for a fairly rich gesture set compared to the available methods. The worst recognition result which is the best in the literature is obtained as 97.3% among the four sets of the experiments for each hand gestures. The recognition results are reported with a substantial number of experiments and labeling computation.