Han Hyonyoung, Jo Sungho, Kim Jung
Department Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Med Biol Eng Comput. 2015 Jul;53(7):577-88. doi: 10.1007/s11517-015-1271-1. Epub 2015 Mar 10.
This paper proposes the feasibility of a stiffness measurement for muscle contraction force estimation under muscle fatigue conditions. Bioelectric signals have been widely studied for the estimation of the contraction force for physical human-robot interactions, but the correlation between the biosignal and actual motion is decreased under fatigue conditions. Muscle stiffness could be a useful contraction force estimator under fatigue conditions because it measures the same physical quantity as the muscle contraction that generates the force. Electromyography (EMG), mechanomyography (MMG), and a piezoelectric resonance-based active muscle stiffness sensor were used to analyze the biceps brachii under isometric muscle fatigue conditions with reference force sensors at the end of the joint. Compared to EMG and MMG, the change in the stiffness signal was smaller (p < 0.05) in the invariable contraction force generation test until failure. In addition, in the various contraction level force generation tests, the stiffness signal under the fatigue condition changed <10% (p < 0.05) compared with the signal under non-fatigue conditions. This result indicates that the muscle stiffness signal is less sensitive to muscle fatigue than other biosignals. This investigation provides insights into methods of monitoring and compensating for muscle fatigue.
本文提出了在肌肉疲劳条件下测量肌肉刚度以估计收缩力的可行性。生物电信号已被广泛研究用于估计人机物理交互中的收缩力，但在疲劳条件下，生物信号与实际运动之间的相关性会降低。肌肉刚度可能是疲劳条件下一种有用的收缩力估计指标，因为它测量的是与产生力的肌肉收缩相同的物理量。在等长肌肉疲劳条件下，使用肌电图（EMG）、机械肌电图（MMG）和基于压电共振的主动肌肉刚度传感器，在关节末端使用参考力传感器对肱二头肌进行分析。在直至失效的恒定收缩力产生测试中，与EMG和MMG相比，刚度信号的变化较小（p<0.05）。此外，在各种收缩水平力产生测试中，疲劳条件下的刚度信号与非疲劳条件下的信号相比变化<10%（p<0.05）。该结果表明，肌肉刚度信号对肌肉疲劳的敏感性低于其他生物信号。本研究为监测和补偿肌肉疲劳的方法提供了见解。
