Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| Subtitle of host publication | Smarter Technology for a Healthier World, EMBC 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2514-2517 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781509028092 |
| DOIs | |
| Publication status | Published - Sep 13 2017 |
| Externally published | Yes |
| Event | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: Jul 11 2017 → Jul 15 2017 |
Publication series
| Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
|---|---|
| ISSN (Print) | 1557-170X |
Other
| Other | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 7/11/17 → 7/15/17 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
Variance distribution analysis of surface EMG signals based on marginal maximum likelihood estimation. / Furui, Akira; Hayashi, Hideaki; Kurita, Yuichi; Tsuji, Toshio.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2514-2517 8037368 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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
UR - http://www.scopus.com/inward/citedby.url?scp=85032215116&partnerID=8YFLogxK
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.
ER -