Author:
Ando Takeshi, ,Okamoto Jun,Takahashi Mitsuru,Fujie Masakatsu G., , ,
Abstract
The myoelectric controlled rollover support orthosis we have been developing for use in bone cancer metastasis requires high accuracy and quick response in signal processing to recognize movement. We quantitatively evaluated the response performance of recognizing rollover using our original Micro Macro Neural Network (MMNN) algorithm. Required response time was calculated as 60 ms by measuring contraction time for the muscle used in the orthosis to support rollover. TheMMNN recognized rollover 65 ms before it started. Rollover was recognized 5 ms after a myoelectric signal was generated, so the MMNN response was sufficient for the muscle to finish contraction before rollover started.
Publisher
Fuji Technology Press Ltd.
Subject
Electrical and Electronic Engineering,General Computer Science
Reference24 articles.
1. T. Ando, J. Okamoto, and M. G. Fujie, “EMG Controlled Rollover Support System for Bone Cancer Metastasis Patient (1st report): EMG Signal Analysis in Rollover Movement as an Input Signal,” JSMBE, Vol.46, No.3, pp. 383-389, 2008.
2. T. Ando, J. Okamoto, and M. G. Fuji, “Micro Macro Neural Network to Recognize Rollover Movement,” Advanced Robotics, Vol.25, No.1-2, pp. 253-271, 2011.
3. T. Ando, J. Okamoto, M. Takahashi, and M. G. Fujie, “Intelligent Trunk Corset to Support Rollover of Cancer Bone Metastasis Patients,” IEEE/ASME Trans. on Mechatronics, Vol.15, No.2, pp. 181-190, 2010.
4. B. Hudgins, P. Parker, and R. Scott, “A New Strategy for Multifunction Myoelectric Control,” IEEE Trans. Biomed. Eng. Vol.40, No.1, pp. 82-94, 1993.
5. K. A. Farry, I. D.Walker, and R. G. Baraniuk, “Myoelectric Teleoperation of a Complex Robotic Hand,” IEEE Trans. on Robotics and Automation, Vol.12, No.5, pp. 775-787, 1996.
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