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Haptic Human-Human Interaction During an Ankle Tracking Task: Effects of Virtual Connection Stiffness
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  • Matthew R. Short ,
  • Daniel Ludvig ,
  • Emek Barış Küçüktabak ,
  • Yue Wen ,
  • Lorenzo Vianello ,
  • Eric J. Perreault ,
  • Levi Hargrove ,
  • Kevin Lynch ,
  • José Pons
Matthew R. Short
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Daniel Ludvig
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Emek Barış Küçüktabak
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Lorenzo Vianello
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Eric J. Perreault
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Levi Hargrove
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Kevin Lynch
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José Pons
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While treating sensorimotor impairments, a therapist may provide physical assistance by guiding their patient’s limb to teach a desired movement. In this scenario, a key aspect is the compliance of the interaction, as the therapist can provide subtle cues or impose a movement as demonstration. One approach to studying these interactions involves haptically connecting two individuals through robotic interfaces. Upper-limb studies have shown that pairs of connected individuals estimate one another’s goals during tracking tasks by exchanging haptic information, resulting in improved performance dependent on the ability of one’s partner and the stiffness of the virtual connection. In this study, our goal was to investigate whether these findings generalize to the lower-limb during an ankle tracking task. Pairs of healthy participants (i.e., dyads) independently tracked target trajectories with and without connections rendered between two ankle robots. We tested the effects of connection stiffness as well as visual noise to manipulate the correlation of tracking errors between partners. In our analysis, we compared changes in task performance across conditions while tracking with and without the connection. We found that tracking improvements while connected increased with connection stiffness, favoring the worse partner in the dyad during hard connections. We modeled the interaction as three springs in series, considering the stiffness of the connection and each partners’ ankle, to show that improvements were likely due to a cancellation of random tracking errors between partners. These results suggest a simplified mechanism of improvements compared to what has been reported during upper-limb dyadic tracking.
2023Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering volume 31 on pages 3864-3873. 10.1109/TNSRE.2023.3319291