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

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.

[1]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[2]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[3]  E. Kaplan.  Muscles Alive. Their Functions Revealed by Electromyography. J. V. Basmajian. Baltimore, The Williams and Wilkins Co., 1962. $8.50 , 1962 .

[4]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[5]  James Demmel,et al.  Benchmarking GPUs to tune dense linear algebra , 2008, HiPC 2008.

[6]  S H Park,et al.  EMG pattern recognition based on artificial intelligence techniques. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  Pasquale Daponte,et al.  A TFR based method for the quality assessment of UMTS signals: an application on the first Italian experimental network , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[8]  He Huang,et al.  A Strategy for Identifying Locomotion Modes Using Surface Electromyography , 2009, IEEE Transactions on Biomedical Engineering.

[9]  David G. Stork,et al.  Pattern Classification , 1973 .

[10]  Steven G. Johnson,et al.  The Design and Implementation of FFTW3 , 2005, Proceedings of the IEEE.

[11]  He Huang,et al.  An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[13]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[14]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[15]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[16]  J. Kulpa,et al.  Time-frequency analysis using NVIDIA compute unified device architecture (CUDA) , 2009, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).

[17]  D Graupe,et al.  A multifunctional prosthesis control system based on time series identification of EMG signals using microprocessors. , 1977, Bulletin of prosthetics research.

[18]  H. Devries.  MUSCLES ALIVE-THEIR FUNCTIONS REVEALED BY ELECTROMYOGRAPHY , 1976 .

[19]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[20]  S. Kak.  The discrete Hilbert transform , 1970 .

[21]  N. Fujimoto,et al.  Faster matrix-vector multiplication on GeForce 8800GTX , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.