TY - GEN
T1 - Motion-Copying System Based on Modeling of Finger Force Characteristics Using Upper Limb-EMG
AU - Sodenaga, Daiki
AU - Egawa, Kosuke
AU - Katsura, Seiichiro
N1 - Funding Information:
ACKNOWLEDGMENT This work was partially supported by JSPS KAKENHI Grant Number 21H04566.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In recent years, motion-copying systems that store and reproduce human motions have attracted much attention. In the conventional method, the motion is stored using a motor, which affects the original task. In this study, we focused on the relationship between electromyography and force in order to realize unconstrained, non-contact force measurement. In this paper, we modeled the relationship between the force of pressing a force sensor with a fingertip and the myoelectric potential of performing an action by using the elemental description method, which is one of the system identification methods with easy physical interpretation. As a result, an accuracy of 0.260 N, the least squares error, was obtained. In addition, we conducted on copying and reproducing the motion of finger using this model. Although the accuracy of force estimation was low, we were able to estimate the force with the same accuracy. In the future, we aim to improve the accuracy of the estimation and to measure the force using only the myoelectric sensor without the force sensor.
AB - In recent years, motion-copying systems that store and reproduce human motions have attracted much attention. In the conventional method, the motion is stored using a motor, which affects the original task. In this study, we focused on the relationship between electromyography and force in order to realize unconstrained, non-contact force measurement. In this paper, we modeled the relationship between the force of pressing a force sensor with a fingertip and the myoelectric potential of performing an action by using the elemental description method, which is one of the system identification methods with easy physical interpretation. As a result, an accuracy of 0.260 N, the least squares error, was obtained. In addition, we conducted on copying and reproducing the motion of finger using this model. Although the accuracy of force estimation was low, we were able to estimate the force with the same accuracy. In the future, we aim to improve the accuracy of the estimation and to measure the force using only the myoelectric sensor without the force sensor.
KW - EMG
KW - Element Description Method
KW - Finger Force
KW - Motion-Copying System
KW - Myoelectric Potential
KW - Upper-Limb
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U2 - 10.1109/HSI55341.2022.9869479
DO - 10.1109/HSI55341.2022.9869479
M3 - Conference contribution
AN - SCOPUS:85137896854
T3 - International Conference on Human System Interaction, HSI
BT - 15th IEEE International Conference on Human System Interaction, HSI 2022
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Human System Interaction, HSI 2022
Y2 - 28 July 2022 through 31 July 2022
ER -