Estimation of handgrip force using frequency-band technique during fatiguing muscle contraction
Received 5 March 2009; received in revised form 22 August 2009; accepted 28 August 2009. published online 19 October 2009. Corrected Proof
Abstract
In this paper, we propose a force estimation model to compute the handgrip force from SEMG signal during fatiguing muscle contraction tasks. The appropriate frequency range was analyzed using various combinations of a wavelet scale, and the highest accuracy was achieved at a range from 242 to 365Hz. After that, eight healthy individuals performed a series of static (70%, 50%, 30%, and 20% MVC) and dynamic (0–50% MVC) muscle contraction tasks to evaluate the performance of this technique in comparison with that of former method using the Root Mean Square of the SEMG signal. Both methods had comparable results at the beginning of the experiments, before the onset of muscle fatigue. However, differences were clearly observed as the degree of muscle fatigue began to increase toward the endurance time. Under this condition, the estimated handgrip force using the proposed method improved from 17% to 134% for static contraction tasks and 40% for dynamic contraction tasks. This study overcomes the limitation of the former method during fatiguing muscle contraction tasks and, therefore, unlocks the potential of utilizing the SEMG signal as an indirect force estimation method for various applications.
aResearch into Artifacts, Center for Engineering (RACE), The University of Tokyo, Kashiwanoha 5-1-5, Kashiwa, Chiba 277-8568, Japan
bDepartment of Mechanical Engineering and Intelligent Systems, The University of Electro-Telecommunications, 1-5-1 Chofugaoka, Chofushi, Tokyo 182-8585, Japan
cDepartment of Precision Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
ABSTRACT
Hz. After that, eight healthy individuals performed a series of static (70%, 50%, 30%, and 20% MVC) and dynamic (0–50% MVC) muscle contraction tasks to evaluate the performance of this technique in comparison with that of former method using the Root Mean Square of the SEMG signal. Both methods had comparable results at the beginning of the experiments, before the onset of muscle fatigue. However, differences were clearly observed as the degree of muscle fatigue began to increase toward the endurance time. Under this condition, the estimated handgrip force using the proposed method improved from 17% to 134% for static contraction tasks and 40% for dynamic contraction tasks. This study overcomes the limitation of the former method during fatiguing muscle contraction tasks and, therefore, unlocks the potential of utilizing the SEMG signal as an indirect force estimation method for various applications.