Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 24, Issue -, Pages 11-18Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2015.09.001
Keywords
EMG gesture; Continuous gesture recognition; Spectral representation; Gesture training matrix; MYO armband
Categories
Funding
- Japan Society for the Promotion of Science (JSPS)
- KAKENHI [15F13739]
- Grants-in-Aid for Scientific Research [15F13739] Funding Source: KAKEN
Ask authors/readers for more resources
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. (C) 2015 The Authors. Published by Elsevier Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available