Literature DB >> 2016926

Control performance characteristics of myoelectric signal with additive interference.

Y T Zhang1, P A Parker, R N Scott.   

Abstract

Myoelectric signal is an important source of control information for powered prostheses. A commonly used performance measure for the signal processors of such control systems is the ratio of processor output mean to variance. This ratio (SNR) is a function of a number of factors including physiological parameters and additive interference. The paper investigates the effects of motor unit physiological parameters and interference on control performance, with particular reference to SNR. Performance equations are derived and verified with in vivo experiments. The results show a complex interaction among the physiological parameters and interference. A particular point of interest is the misleading SNR values that can occur under certain recruitment and interference conditions.

Mesh:

Year:  1991        PMID: 2016926     DOI: 10.1007/bf02446301

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  9 in total

1.  A METHOD OF INSERTING WIRE ELECTRODES FOR ELECTROMYOGRAPHY.

Authors:  R N SCOTT
Journal:  IEEE Trans Biomed Eng       Date:  1965-01       Impact factor: 4.538

2.  Study of the effects of motor unit recruitment and firing statistics on the signal-to-noise ratio of a myoelectric control channel.

Authors:  Y T Zhang; P A Parker; R N Scott
Journal:  Med Biol Eng Comput       Date:  1990-05       Impact factor: 2.602

3.  A nonstationary model for the electromyogram.

Authors:  E Shwedyk; R Balasubramanian; R N Scott
Journal:  IEEE Trans Biomed Eng       Date:  1977-09       Impact factor: 4.538

4.  The experimental demonstration of a multichannel time-series myoprocessor: system testing and evaluation.

Authors:  R J Triolo; G D Moskowitz
Journal:  IEEE Trans Biomed Eng       Date:  1989-10       Impact factor: 4.538

5.  Physiology and mathematics of myoelectric signals.

Authors:  C J De Luca
Journal:  IEEE Trans Biomed Eng       Date:  1979-06       Impact factor: 4.538

6.  A signal-to-noise investigation of nonlinear electromyographic processors.

Authors:  J G Kreifeldt; S Yao
Journal:  IEEE Trans Biomed Eng       Date:  1974-07       Impact factor: 4.538

7.  A model for myoelectric signal generation.

Authors:  G Brody; R N Scott; R Balasubramanian
Journal:  Med Biol Eng       Date:  1974-01

8.  Optimizing the acquisition and processing of surface electromyographic signals.

Authors:  M I Harba; P A Lynn
Journal:  J Biomed Eng       Date:  1981-04

9.  Myoelectric signal processing: optimal estimation applied to electromyography--Part II: experimental demonstration of optimal myoprocessor performance.

Authors:  N Hogan; R W Mann
Journal:  IEEE Trans Biomed Eng       Date:  1980-07       Impact factor: 4.538

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.