A Framework for Dyadic Physical Interaction Studies during Ankle Motor Tasks

Sangjoon J. Kim, Yue Wen, Emek Bars Kucuktabak, Shaobo Zhan, Kevin Lynch, Levi Hargrove, Eric J. Perreault, Jose L. Pons*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Over the past few decades, there have been many studies of human-human physical interaction to better understand why humans physically interact so effectively and how dyads outperform individuals in certain motor tasks. Because of the different methodologies and experimental setups in these studies, however, it is difficult to draw general conclusions as to the reasons for this improved performance. In this study, we propose an open-source experimental system for the systematic study of the effect of human-human interaction, as mediated by robots, at the ankle joint. We also propose a new framework to study various interactive behaviors (i.e., collaborative, cooperative, and competitive tasks) that can be emulated using a virtual spring connecting human pairs. To validate the proposed experimental framework, we perform a transparency analysis, which is closely related to haptic rendering performance. We compare muscle EMG and ankle motion data while subjects are barefoot, attached to the unpowered robot, and attached to the powered robot implementing transparency control. We also validate the performance in rendering virtual springs covering a range of stiffness values (5-50 Nm/rad) while the subjects track several desired trajectories (sine waves at frequencies between 0.1 and 1.1 Hz). Finally, we demonstrate the feasibility of the system in studying human-human interaction under different interactive behaviors.

Original languageEnglish (US)
Article number9464715
Pages (from-to)6876-6883
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 2021


  • Physical human-robot interaction
  • haptics and haptic interfaces
  • human factors and human-in-The-loop
  • human-robot teaming

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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