Prothesis Movements Pattern Recognition Based on Auto-Regressive Model and Wavelet Neural Network

Abstract:

Wavelet neural networks (WNN) combine the functions of time–frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. Based on auto-regressive (AR) model and WNN, pattern recognition of prothesis movements was studied in this paper. Firstly, an AR model was used to analysis the surface myoelectric signals (SMES) which recorded on the ulnar flexor carpi and extensor carpi region of the right hand in resting position. Four types of prosthesis movements are recognized by extracting four-order AR coefficient and construct them as eigenvector into WNN, which was used to study the correlation between SMES and wristwork. This paper compares the classification accuracy of four movements such as hand open (HO), hand close (HC), forearm intorsion (FI) and forearm extorsion (FE).The experimental results show that the proposed method can classify correctly for at least 93.75% of the test data.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 2156-2161
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.2156
• Online since: October 2011
• Authors: Cheng Gao, Jiao Ying Huang, Wei Guo
• Keywords: Auto-Regressive Model, Pattern Recognition, Surface Electromyography Signal, Wavelet Neural Network (WNN)

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