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：人体上肢的内在生物力学特性在物理人机交互(pHRI)过程中对交互能量的吸收起着关键作用。我们最近的研究表明，基于非线性控制理论中的"过量被动性(EoP)"概念，可以解码上肢和下肢的此类能量行为。所提取的知识可用于设计控制器，以优化人机交互和触觉系统中力场的透明度和保真度。本文首次研究了当通过视觉肌电反馈实时控制肌肉共激活时，上肢被动性图谱的频率行为。本研究纳入5名健康受试者（年龄：27±5岁）。在两个受控肌肉共激活水平下，于8个交互方向评估了两种刺激频率下的能量行为。使用Delsys Wireless Trigno系统采集肌电图(EMG)。结果显示，EMG与EoP之间存在相关性，且该相关性随频率增加而进一步改变。所提出的能量行为被命名为几何肌被动性(GMP)图谱。研究结果表明，GMP图谱具有实时量化可吸收能量的潜力，从而在pHRI中评估上肢交互的被动性稳定裕度。