Combination of AR and neural network technique for EMG pattern identification

Ainishet Asres, Huifang Dou, Zhaoying Zhou, Yuli Zhang, Sencun Zhu

Research output: Contribution to journalConference article

18 Scopus citations


The EMG data acquired during voluntary movement of the active muscles of the disabled may provide useful control commands and information in functional electrical stimulation or in artificial prosthesis provided that the raw EMG data are properly processed and identified. This technique may be used by the patients to transfer commands to their paralyzed extremities or artificial limbs. Combination of autoregressive and neural network technique to identify various functional hand movements is proposed. Functional hand movements such as palmar flexion and dorsiflexion, wrist pronation and supination, wrist flexion and extension, are identified. A fourth-order parametric model is employed to evaluate the set of coefficients. The coefficients are then used as input for the neural network to identify the functional movement. Experiment was done on three healthy individuals and the rate of identification is shown to be adequate to be used in the development of either neural prostheses or artificial limbs.

Original languageEnglish (US)
Pages (from-to)1464-1465
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publication statusPublished - Dec 1 1996
EventProceedings of the 1996 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 5) - Amsterdam, Neth
Duration: Oct 31 1996Nov 3 1996


All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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