Abstract
Bio-signal processing is a major topic in the field of robotics. Especially, prostheses have been studied for a long time to detect amputee's intentions from bio-signals because they do not have their limbs. However, controlling prostheses using bio-signals such as from the brain and surface electromyograms (sEMG), is complex and nonlinear because these signals are noisy and varied. It is not easy to determine the extent of motion such as a joint angle, and previous research used complex models or machine learning based on bio-signals. To estimate a joint angle easily and accurately, we propose a new bio-signal derived from the muscle bulge movements measured on the skin. We hypothesized a simple relationship between the muscle bulge movement longitudinally along a muscle and the corresponding joint angle, because the muscle contraction causes the change in the joint angle. We estimated a wrist joint angle as a function of the muscle bulge movement longitudinally along the extensor carpi radialis longus that is the agonist muscle of wrist extension. From the experimental results, the following three achievements were obtained. First, we extracted a linear relationship from the raw data with a high determination coefficient. Second, we showed that the estimation error using the proposed method was not much different from that of using sEMG in related work. Third, we established that the proposed method could estimate the joint angle robustly for the external loads on a limb.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 614-619 |
| Number of pages | 6 |
| ISBN (Print) | 9781467396745 |
| DOIs | |
| Publication status | Published - 2016 Feb 24 |
| Event | IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015 - Zhuhai, China Duration: 2015 Dec 6 → 2015 Dec 9 |
Other
| Other | IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015 |
|---|---|
| Country | China |
| City | Zhuhai |
| Period | 15/12/6 → 15/12/9 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Hardware and Architecture
- Control and Systems Engineering
Cite this
Estimating a joint angle by means of muscle bulge movement along longitudinal direction of the forearm. / Kato, Akira; Matsumoto, Yuya; Kobayashi, Yo; Sugano, Shigeki; Fujie, Masakatsu G.
2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 614-619 7418836.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Estimating a joint angle by means of muscle bulge movement along longitudinal direction of the forearm
AU - Kato, Akira
AU - Matsumoto, Yuya
AU - Kobayashi, Yo
AU - Sugano, Shigeki
AU - Fujie, Masakatsu G.
PY - 2016/2/24
Y1 - 2016/2/24
N2 - Bio-signal processing is a major topic in the field of robotics. Especially, prostheses have been studied for a long time to detect amputee's intentions from bio-signals because they do not have their limbs. However, controlling prostheses using bio-signals such as from the brain and surface electromyograms (sEMG), is complex and nonlinear because these signals are noisy and varied. It is not easy to determine the extent of motion such as a joint angle, and previous research used complex models or machine learning based on bio-signals. To estimate a joint angle easily and accurately, we propose a new bio-signal derived from the muscle bulge movements measured on the skin. We hypothesized a simple relationship between the muscle bulge movement longitudinally along a muscle and the corresponding joint angle, because the muscle contraction causes the change in the joint angle. We estimated a wrist joint angle as a function of the muscle bulge movement longitudinally along the extensor carpi radialis longus that is the agonist muscle of wrist extension. From the experimental results, the following three achievements were obtained. First, we extracted a linear relationship from the raw data with a high determination coefficient. Second, we showed that the estimation error using the proposed method was not much different from that of using sEMG in related work. Third, we established that the proposed method could estimate the joint angle robustly for the external loads on a limb.
AB - Bio-signal processing is a major topic in the field of robotics. Especially, prostheses have been studied for a long time to detect amputee's intentions from bio-signals because they do not have their limbs. However, controlling prostheses using bio-signals such as from the brain and surface electromyograms (sEMG), is complex and nonlinear because these signals are noisy and varied. It is not easy to determine the extent of motion such as a joint angle, and previous research used complex models or machine learning based on bio-signals. To estimate a joint angle easily and accurately, we propose a new bio-signal derived from the muscle bulge movements measured on the skin. We hypothesized a simple relationship between the muscle bulge movement longitudinally along a muscle and the corresponding joint angle, because the muscle contraction causes the change in the joint angle. We estimated a wrist joint angle as a function of the muscle bulge movement longitudinally along the extensor carpi radialis longus that is the agonist muscle of wrist extension. From the experimental results, the following three achievements were obtained. First, we extracted a linear relationship from the raw data with a high determination coefficient. Second, we showed that the estimation error using the proposed method was not much different from that of using sEMG in related work. Third, we established that the proposed method could estimate the joint angle robustly for the external loads on a limb.
UR - http://www.scopus.com/inward/record.url?scp=84964413669&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964413669&partnerID=8YFLogxK
U2 - 10.1109/ROBIO.2015.7418836
DO - 10.1109/ROBIO.2015.7418836
M3 - Conference contribution
AN - SCOPUS:84964413669
SN - 9781467396745
SP - 614
EP - 619
BT - 2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -