NDLI: Promise of embedded system with GPU in artificial leg control: Enabling time-frequency feature extraction from electromyography

Content Provider	IEEE Xplore Digital Library
Author	Weijun Xiao He Huang Yan Sun Qing Yang
Copyright Year	2009
Description	Author affiliation: Department of Electrical, Computer, and Biomedical Engineering, Kingston, RI, 02881 USA (Weijun Xiao; He Huang; Yan Sun; Qing Yang)
Abstract	Applying electromyographic (EMG) signal pattern recognition to artificial leg control is challenging because leg EMGs are non-stationary. Time-frequency features are suitable for representing non-stationary signals; however, the computational complexity to extract time-frequency features is too high and current embedded systems used for artificial limb control are inadequate for real-time computing. The aim of this study was to quantify the computational speed of a novel embedded system, the Graphic Processor Unit (GPU), on EMG time-frequency feature extraction. The computational time derived from a GPU was compared to that derived from a general purpose CPU. The results indicated that the GPU significantly increased the computational speed. When the size of EMG analysis window was set to 100 ms, the GPU extracted EMG time-frequency features over 50 times faster than the CPU setting. Therefore, high performance GPU shows a great promise for EMG-controlled artificial legs and other medical applications that need high-speed and real-time computation.
Starting Page	6926
Ending Page	6929
File Size	1225207
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333633
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Embedded system Leg Control systems Time frequency analysis Feature extraction Electromyography Embedded computing Pattern recognition Computational complexity Artificial limbs high performance computing control of artificial limbs embedded system
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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