Gestures recognition based on wavelet and LLE

• DOI: 10.1007/s13246-013-0191-3
• Publication Date: 2013-03-19T12:11:50Z
• Authors: Qingsong Ai; Quan Liu; Tingting Yuan; Ying Lu
• Keywords: Gestures; Electromyography; 0206 medical engineering; Wavelet Analysis; Reproducibility of Results; 02 engineering and technology; Sensitivity and Specificity; Pattern Recognition, Automated; Data Interpretation, Statistical; Arm; Humans; Muscle, Skeletal; Algorithms; Muscle Contraction

Wavelet analysis is a time-frequency, non-stationary method while the largest Lyapunov exponent (LLE) is used to judge the non-linear characteristic of systems. Because surface electromyography signal (SEMGS) is a complex signal that is characterized by non-stationary and non-linear properties. This paper combines wavelet coefficient and LLE together as the new feature of SEMGS. The proposed method not only reflects the non-stationary and non-linear characteristics of SEMGS, but also is suitable for its classification. Then, the BP (back propagation) neural network is employed to implement the identification of six gestures (fist clench, fist extension, wrist extension, wrist flexion, radial deviation, ulnar deviation). The experimental results indicate that based on the proposed method, the identification of these six gestures can reach an average rate of 97.71 %.