The intrinsic biomechanical characteristic of the human upper limb plays a central role in absorbing the interactive energy during physical human-robot interaction (pHRI). We have recently shown that based on the concept of ``Excess of Passivity (EoP)," from nonlinear control theory, it is possible to decode such energetic behavior for both upper and lower limbs. The extracted knowledge can be used in the design of controllers for optimizing the transparency and fidelity of force fields in human-robot interaction and in haptic systems. In this paper, for the first time, we investigate the frequency behavior of the passivity map for the upper limb when the muscle co-activation was controlled in real-time through visual electromyographic feedback. Five healthy subjects (age: 27 +/- 5) were included in this study. The energetic behavior was evaluated at two stimulation frequencies at eight interaction directions over two controlled muscle co-activation levels. Electromyography (EMG) was captured using the Delsys Wireless Trigno system. Results showed a correlation between EMG and EoP, which was further altered by increasing the frequency. The proposed energetic behavior is named the Geometric MyoPassivity (GMP) map. The findings indicate that the GMP map has the potential to be used in real-time to quantify the absorbable energy, thus passivity margin of stability for upper limb interaction during pHRI.
翻译:人体上肢固有的生物机能特征在吸收人体-机器人物理互动期间的互动能量(pHRI)方面发挥了核心作用。我们最近从非线性控制理论中显示,根据“被动性过量(EoP)”的概念,从非线性控制理论中可以解码上肢和下肢的这种能动行为。提取的知识可用于设计控制器,优化人体-机器人相互作用中和机能系统中的力量场的透明度和忠诚性。在本文中,我们首次调查了在通过视觉电感学反馈实时控制肌肉共活性(EoP)时,上肢被动性图的频率行为。本研究包括了五个健康科目(年龄:27+/-5),在两个受控肌肉共活性水平上的两个振动频率上评价了强性。电感学(EMG)利用Delsysyles Wiressles Trigno系统采集了。结果显示EMG和EoP的被动性图的频率变化,在GMRVS中,用真实的频率显示的是磁感应变。