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 [1], [2]. The extracted knowledge can be used in the design of controllers (such as [2]-[5]) 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 amplified by decreasing 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.

The human limb possesses a remarkable capacity to absorb energy during physical human-robot interaction (pHRI), which can be quantified as the biomechanical “Excess of Passivity” (EoP) using non-linear control theory. This biome-chanical passivity index can be used to reduce conservatism and increase the transparency of pHRI stabilizers. Previous work on EoP has used system identification techniques to compute EoP offline. However, for use in real-time controllers, an instantaneous method for EoP estimation would be desired. This paper hypothesizes that muscle fatigue can potentially be a complicating factor which can cumulatively affect the ability of human biomechanics to absorb mechanical energy over time during physical interaction with robots. In this work, we focused on the energetic behavior of the human wrist during pHRI, and, for the first time, we investigated the effect of fatigue on EoP. The EoP for five participants was computed throughout one hundred-second trials of high-frequency wrist perturbations in four directions. Subjects maintained a stiff and consistent grip throughout each trial, causing an accumulation of fatigue in the forearm muscles. Muscle activity was recorded using an array of sixteen sEMG sensors. It was found that the EoP degraded (in a statistically significant manner) with increased muscle fatigue in all directions, even when the level of muscle co-contraction was controlled consistently through a visual myofeedback mechanism. 100% of the subjects exhibited this decline in energy absorption capacity in all directions studied. The median drop in EoP after one-hundred seconds of perturbation was 11% for trials in the abduction and adduction directions and 22% in the pronation and supination directions. These results indicate a need for more robust estimation methods or new modalities to account for muscle fatigue in the control architectures of physical human-robot interaction.

In the context of physical human-(tele)robot interaction, passivity-based stabilizers have been used to guarantee the physical or (tele) physical stability. In most of these examples, human biomechanics is considered an inherently passive system that dissipates energy. This assumption may not hold true when the interaction is implemented in the force-position domain, even though such a setting would be needed to boost positional accuracy and avoid the common kinematic drifts in the force-velocity domains. The aforementioned topic is examined in this paper using the concept of shortage versus excess of passivity index for human biomechanics in the force-position domain. We also investigate the compounding effect of the frequency of interaction. The outcomes of this paper will be imperative for the design of force-position domain pURI stabilizers when the classical assumption of passivity of human biomechanics can lead to serious safety issues. In this work, for the first time, we quantitatively present the passivity margin and, thus, the energetic behavior of the human arm's biomechanics under various interaction scenarios in the Force-Position domain. The outcome of this work includes a three-dimensional passivity index map (3DPiM) that is validated on five healthy participants. The goal is to illustrate the passivity margin of the human upper limb biomechanics for two distinct levels of muscle co-contractions, as indicated by the Electromyography (EMG) signal, across four interaction frequencies and eight geometric directions. This outcome enables the future development of biomechanics-aware stabilizers in the force-position domain, quantifying the passivity margin in real-time and thus significantly reducing the stabilizer's conservatism while ensuring the safety of human-robot interactions.

Evidence suggests that the metabolic cost associated with the locomotive activity of walking is dependent upon ankle stiffness. This stiffness can be a control parameter in an ankle-foot prosthesis. Considering unique physical interaction between each individual with below-knee amputation and robotic ankle-foot prosthesis, individually tuned stiffness in a robotic ankle-foot prosthesis may improve assistance benefits. This personalization can be accomplished through human-in-the-loop (HIL) Bayesian optimization (BO). Here, we conducted a pilot study to identify personalized ankle- foot prosthesis stiffness using the HIL BO to minimize the cost of walking, shown by metabolic cost. We used an improved versatile ankle-foot prosthesis emulator, which enabled to test controllers with a wide range of stiffness conditions. Two participants with simulated amputation reduced their cost of walking under the condition of personalized (optimized) stiffness by 6% and 5%, respectively. This result suggests that personalized stiffness may improve assistance benefit.