TY - GEN
T1 - Wrist joint angle estimation by means of muscle bulge based on deformation of the forearm skin surface
AU - Kato, Akira
AU - Matsumoto, Yuya
AU - Kobayashi, Yo
AU - Sugano, Shigeki
AU - Fujie, Masakatsu G.
N1 - Funding Information:
The present work was supported in part by the Program for Leading Graduate Schools, the Graduate Program for Embodiement Informatics of the Ministry of Education, Culture, Sports, Science and Technology of Japan, by Grants for Excellent Graduate Schools (MEXT, Japan), by Grant Scientific Research (A)(26242061, Japan) and by the Research Fellowship for Young Scientists (JSPS, Japan)
Publisher Copyright:
© 2016 TSI Enterprise Inc (TSI Press).
PY - 2016/10/4
Y1 - 2016/10/4
N2 - The objective of this paper is to develop a method to estimate the wrist joint angle based on the deformation of the forearm skin surface during muscle contraction. We have focused on the longitudinal movement of the muscle bulge along the forearm. We previously confirmed a one-to-one relationship between the movement of the muscle bulge and the wrist joint angle, and validated the feasibility of the estimation of the joint angle using this relationship. However, the relationship between the movement of the muscle bulge and the wrist joint was previously difficult to perform because of the misalignment of the sensor on the muscle. Here we use a tactile sensor that can measure three-dimensional data on the forearm skin surface to map the muscle bulge location. We measured a large 32 × 96 mm area on the forearm skin with 48 skin distance sensors. We calculated x and y components of the barycentric coordinates from the measured data. We observed a one-to-one relationship between the y-component of the barycentric coordinate from the distribution of the movement of the muscle bulge on the forearm skin surface. We then calculated the RMSE between the measured and estimated wrist joint angle using our joint angle estimation algorithm. We found that the RMSE from our technique was greater than from the conventional method. While we validated the feasibility of our estimation method further research is required to reduce our estimation error by improving the extraction and interpretation of our sensor data.
AB - The objective of this paper is to develop a method to estimate the wrist joint angle based on the deformation of the forearm skin surface during muscle contraction. We have focused on the longitudinal movement of the muscle bulge along the forearm. We previously confirmed a one-to-one relationship between the movement of the muscle bulge and the wrist joint angle, and validated the feasibility of the estimation of the joint angle using this relationship. However, the relationship between the movement of the muscle bulge and the wrist joint was previously difficult to perform because of the misalignment of the sensor on the muscle. Here we use a tactile sensor that can measure three-dimensional data on the forearm skin surface to map the muscle bulge location. We measured a large 32 × 96 mm area on the forearm skin with 48 skin distance sensors. We calculated x and y components of the barycentric coordinates from the measured data. We observed a one-to-one relationship between the y-component of the barycentric coordinate from the distribution of the movement of the muscle bulge on the forearm skin surface. We then calculated the RMSE between the measured and estimated wrist joint angle using our joint angle estimation algorithm. We found that the RMSE from our technique was greater than from the conventional method. While we validated the feasibility of our estimation method further research is required to reduce our estimation error by improving the extraction and interpretation of our sensor data.
KW - Angle estimation
KW - Bio-signal processing
KW - Muscle bulge
KW - Powered prosthesis
KW - Skin surface
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U2 - 10.1109/WAC.2016.7582961
DO - 10.1109/WAC.2016.7582961
M3 - Conference contribution
AN - SCOPUS:84994000486
T3 - World Automation Congress Proceedings
BT - 2016 World Automation Congress, WAC 2016
PB - IEEE Computer Society
T2 - 2016 World Automation Congress, WAC 2016
Y2 - 31 July 2016 through 4 August 2016
ER -