Laboratoire Handibio, EA 4322, Université du Sud-Toulon-Var, Avenue de l'université, BP 20132, 83957, La Garde Cedex, France.
Med Biol Eng Comput. 2009 Nov;47(11):1173-9. doi: 10.1007/s11517-009-0530-4. Epub 2009 Sep 26.
Muscle force knowledge during reaching is an important research field and tools development for measuring those forces is a challenging task, especially for clinical routines. The purpose of this study was, during a simple reach-to-grasp movement, to compare forces estimation from a Hill-type model and from the EMG-to-Force Processing (EFP) method. Ten healthy male volunteers were tested. Surface EMG signals were recorded from deltoid scapular, deltoid clavicular, triceps brachii, and biceps brachii. Ten repeated measures of right upper limb kinematics had been recorded. Three reaching distances were tested: 20, 30, and 40 cm. Muscle activations were calculated and forces were estimated by the two methods. Correlations and low RMS error found between the two methods indicate that EFP is a good way to estimate muscle forces for this kind of movement. This knowledge is essential in order to integrate these forces in reaching models developed nowadays in robotic, rehabilitation, and ergonomics field of research.
在伸手抓握的简单运动过程中，本研究旨在比较基于 Hill 模型和基于肌电图-力处理（EFP）方法的力估计。10 名健康男性志愿者参与了测试。三角肌肩胛、三角肌锁骨、肱三头肌和肱二头肌的表面肌电信号被记录下来。记录了右上肢运动学的 10 次重复测量。测试了三个伸展距离：20、30 和 40 厘米。通过两种方法计算肌肉激活并估计力。两种方法之间的相关性和低 RMS 误差表明，EFP 是估计这种运动中肌肉力的一种很好的方法。为了将这些力集成到当今机器人、康复和人机工程学研究领域中开发的伸手模型中，了解这些知识至关重要。
