TY - GEN
T1 - The MyoPassivity Puzzle
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
AU - Oliver, Suzanne
AU - Paik, Peter
AU - Zhou, Xingyuan
AU - Atashzar, S. Farokh
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.1109/IROS55552.2023.10341902
DO - 10.1109/IROS55552.2023.10341902
M3 - Conference contribution
AN - SCOPUS:85182525796
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4135
EP - 4140
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 October 2023 through 5 October 2023
ER -