IEICE TRANSACTIONS on Fundamentals
Motion Artifact Elimination Using Fuzzy Rule Based Adaptive Nonlinear Filter
Summary :
Myoelectric (ME) signals during dynamic movement suffer from motion arifact noise caused by mechanical friction between electrodes and the skin. It is difficult to reject artifact noises using linear filters, because the frequency components of the artifact noise include those of ME signals. This paper describes a nonlinear method of eliminating artifacts. It consists of an inverse autoregressive (AR) filter, a nonlinear filter, and an AR filter. To deal with ME signals during dynamic movement, we introduce an adaptive procedure and fuzzy rules that improve the performance of the nonlinear filter for local features. The result is the best ever reported elimination performance. This fuzzy rule based adaptive nonlinear artifact elimination filter will be useful in measurement of ME signals during dynamic movement.
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- IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.5 pp.833-838
- Publication Date
- 1994/05/25
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- Special Section PAPER (Special Section on Signal Processing and System Theory)
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Tohru KIRYU, Hidekazu KANEKO, Yoshiaki SAITOH, "Motion Artifact Elimination Using Fuzzy Rule Based Adaptive Nonlinear Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E77-A, no. 5, pp. 833-838, May 1994, doi: .
Abstract: Myoelectric (ME) signals during dynamic movement suffer from motion arifact noise caused by mechanical friction between electrodes and the skin. It is difficult to reject artifact noises using linear filters, because the frequency components of the artifact noise include those of ME signals. This paper describes a nonlinear method of eliminating artifacts. It consists of an inverse autoregressive (AR) filter, a nonlinear filter, and an AR filter. To deal with ME signals during dynamic movement, we introduce an adaptive procedure and fuzzy rules that improve the performance of the nonlinear filter for local features. The result is the best ever reported elimination performance. This fuzzy rule based adaptive nonlinear artifact elimination filter will be useful in measurement of ME signals during dynamic movement.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e77-a_5_833/_p
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@ARTICLE{e77-a_5_833,
author={Tohru KIRYU, Hidekazu KANEKO, Yoshiaki SAITOH, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Motion Artifact Elimination Using Fuzzy Rule Based Adaptive Nonlinear Filter},
year={1994},
volume={E77-A},
number={5},
pages={833-838},
abstract={Myoelectric (ME) signals during dynamic movement suffer from motion arifact noise caused by mechanical friction between electrodes and the skin. It is difficult to reject artifact noises using linear filters, because the frequency components of the artifact noise include those of ME signals. This paper describes a nonlinear method of eliminating artifacts. It consists of an inverse autoregressive (AR) filter, a nonlinear filter, and an AR filter. To deal with ME signals during dynamic movement, we introduce an adaptive procedure and fuzzy rules that improve the performance of the nonlinear filter for local features. The result is the best ever reported elimination performance. This fuzzy rule based adaptive nonlinear artifact elimination filter will be useful in measurement of ME signals during dynamic movement.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - Motion Artifact Elimination Using Fuzzy Rule Based Adaptive Nonlinear Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 833
EP - 838
AU - Tohru KIRYU
AU - Hidekazu KANEKO
AU - Yoshiaki SAITOH
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 1994
AB - Myoelectric (ME) signals during dynamic movement suffer from motion arifact noise caused by mechanical friction between electrodes and the skin. It is difficult to reject artifact noises using linear filters, because the frequency components of the artifact noise include those of ME signals. This paper describes a nonlinear method of eliminating artifacts. It consists of an inverse autoregressive (AR) filter, a nonlinear filter, and an AR filter. To deal with ME signals during dynamic movement, we introduce an adaptive procedure and fuzzy rules that improve the performance of the nonlinear filter for local features. The result is the best ever reported elimination performance. This fuzzy rule based adaptive nonlinear artifact elimination filter will be useful in measurement of ME signals during dynamic movement.
ER -
English
