Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-39470-6-6
Title: A wavelet feature based mechanomyography classification system for a wearable rehabilitation system for the elderly
Authors: Sasidhar, S.
Panda, S.K. 
Xu, J. 
Issue Date: 2013
Source: Sasidhar, S.,Panda, S.K.,Xu, J. (2013). A wavelet feature based mechanomyography classification system for a wearable rehabilitation system for the elderly. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7910 LNCS : 45-52. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-39470-6-6
Abstract: This paper proposes a pattern recognition based system for identification of the forearm movements using Mechanomyography(MMG) for the rehabilitation of the elderly. The system is used to assist in the relearning and rehabilitation of the movements of the wrist and the hand. Surface MMG signals acquired from the flexor carpi ulnaris, brachioradialis supinator and abductor pollicis longus. The MMG is processed and wavelet based features are extracted which are classified into eight different forearm movements using a multilayer perceptron (MLP) classifier. A classification efficiency of 90.2 % is achieved using the MLP classifier. The MMG system is designed to measure data using accelerometers built into the assistive device and, hence, doesn't require any active involvement of the elderly. © 2013 Springer-Verlag Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/69122
ISBN: 9783642394690
ISSN: 03029743
DOI: 10.1007/978-3-642-39470-6-6
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