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Human arm endpoint-impedance in rhythmic human-robot interaction exhibits cyclic variations

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  • Vincent Fortineau
  • Isabelle A Siegler
  • Maria Makarov
  • Pedro Rodriguez-Ayerbe

Abstract

Estimating the human endpoint-impedance interacting with a physical environment provides insights into goal-directed human movements during physical interactions. This work examined the endpoint-impedance of the upper limb during a hybrid ball-bouncing task with simulated haptic feedback while participants manipulated an admittance-controlled robot. Two experiments implemented a force-perturbation method to estimate the endpoint parameters of 31 participants. Experimental conditions of the ball-bouncing task were simulated in a digital environment. One experiment studied the influence of the target height, while the other explored the impedance at three cyclic phases of the rhythmic movement induced by the task. The participants’ performances were analyzed and clustered to establish a potential influence of endpoint impedance on performance in the ball-bouncing task. Results showed that endpoint-impedance parameters ranged from 45 to 445 N/m, 2.2 to 17.5 Ns/m, and 227 to 893 g for the stiffness, damping, and mass, respectively. Results did not support such a critical role of endpoint impedance in performance. Nevertheless, the three endpoint-impedance parameters described significant variations throughout the arm cycle. The stiffness is linked to a quasi-linear increase, with a maximum value reached before the ball impacts. The observed damping and mass cyclic variations seemed to be caused by geometric and kinematic variations. Although this study reveals rapid and within-cycles variations of endpoint-impedance parameters, no direct relationship between endpoint-impedance values and performance levels in ball-bouncing could be found.

Suggested Citation

  • Vincent Fortineau & Isabelle A Siegler & Maria Makarov & Pedro Rodriguez-Ayerbe, 2023. "Human arm endpoint-impedance in rhythmic human-robot interaction exhibits cyclic variations," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0295640
    DOI: 10.1371/journal.pone.0295640
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    References listed on IDEAS

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    1. Etienne Burdet & Rieko Osu & David W. Franklin & Theodore E. Milner & Mitsuo Kawato, 2001. "The central nervous system stabilizes unstable dynamics by learning optimal impedance," Nature, Nature, vol. 414(6862), pages 446-449, November.
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