TY - GEN
T1 - Variance distribution analysis of surface EMG signals based on marginal maximum likelihood estimation
AU - Furui, Akira
AU - Hayashi, Hideaki
AU - Kurita, Yuichi
AU - Tsuji, Toshio
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - This paper describes the estimation and analysis of variance distribution of surface electromyogram (EMG) signals based on a stochastic EMG model. With the assumption that EMG signals at a certain time follow Gaussian distribution, their variance is handled as a random variable that follows inverse gamma distribution, and noise superimposed onto this variance can be expressed accordingly. The paper proposes variance distribution estimation based on marginal likelihood maximization of EMG signals. A simulation experiment using artificially generated signals to verify its accuracy indicated that the method can estimate variance distribution with high accuracy for a wide range of variance distribution shaping. Analysis of variance distribution using measured EMG signals revealed the relationship between muscle force and variance distribution involving signal-dependent noise.
AB - This paper describes the estimation and analysis of variance distribution of surface electromyogram (EMG) signals based on a stochastic EMG model. With the assumption that EMG signals at a certain time follow Gaussian distribution, their variance is handled as a random variable that follows inverse gamma distribution, and noise superimposed onto this variance can be expressed accordingly. The paper proposes variance distribution estimation based on marginal likelihood maximization of EMG signals. A simulation experiment using artificially generated signals to verify its accuracy indicated that the method can estimate variance distribution with high accuracy for a wide range of variance distribution shaping. Analysis of variance distribution using measured EMG signals revealed the relationship between muscle force and variance distribution involving signal-dependent noise.
UR - http://www.scopus.com/inward/record.url?scp=85032215116&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2017.8037368
DO - 10.1109/EMBC.2017.8037368
M3 - Conference contribution
C2 - 29060410
AN - SCOPUS:85032215116
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2514
EP - 2517
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -