Abstract
Currently, the griping force estimation of using the linear prediction model built on static (isometric) contraction is low accurate in human works, especially at varied grasp motion. The objective of this study is to build an accurate Surface Electromyography (SEMG)-handgrip force model for estimating the handgrip force during dynamic (non-isometric) contraction tasks. Ten healthy individuals performed several MVC tasks and a series of dynamic sub-maximal force (SMF) tasks to obtain a suit of normalization ratio samples. Suitable nonlinear function is selected to fit the nonlinear SEMG-force relationship during the regression procedure. After that, ten subjects performed three dynamic evaluated contraction tasks (0–70% MVC) to analyze performance of the proposed model and compare it with performance of the former model. The experimental results show that the proposed force estimation model can evaluate handgrip force more accurate than former one in dynamic grasp condition.
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