NDLI: A novel method of non-stationary sEMG signal analysis and decomposition using a latent process model

Content Provider	IEEE Xplore Digital Library
Author	Yan Song Ping Xie Xiaoguang Wu Yihao Du Xiaoli Li
Copyright Year	2013
Description	Author affiliation: Inst. of Electr. Eng., Yanshan Univ., Qinhuangdao, China (Yan Song; Ping Xie)
Abstract	To solve the problems of conventional signal analysis methods about non-stationary and frequency characteristics of surface electromyogrphy (sEMG) is of great significance to rehabilitation robot control with EMG-based human-computer interfaces (HCI). In this paper, the latent process models of sEMG signals were developed based on the combination of time-varying auto-regression (TVAR) model and dynamic linear model (DLM), which decomposed the signals into several components, and each component represents different time-frequency behavior of sEMG signals. On the basis of the latent process model, time-varying parameters, modulus and wavelength features were extracted. The fusing features of sEMG signals in two elbow movement conditions (elbow flexion and elbow extension) were adopted for clustering analysis and classification of data was visualized by using self-organizing map (SOM). An experiment with 9 healthy participants was carried out to verify the validity of this algorithm. The result implied that latent process model is a meaningful and valuable non-stationary sEMG signal analysis method which may be promising in rehabilitation robot control.
Sponsorship	IEEE Robot. Autom. Soc.
Starting Page	2363
Ending Page	2368
File Size	1166768
Page Count	6
File Format	PDF
ISBN	9781479927449
DOI	10.1109/ROBIO.2013.6739823
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Analytical models Elbow Brain modeling Time-frequency analysis Muscles Vectors
Content Type	Text
Resource Type	Article

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