Measuring Interaction Bandwidth During Physical Human-Robot Collaboration

Aleksandra Kalinowska, Millicent Schlafly, Kyra Rudy, Julius P.A. Dewald, Todd D. Murphey*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

An assistive robot can augment human performance by providing physical assistance or motion guidance. In human-robot collaboration, it is important to know the bandwidth of an individual's ability to generate motion in response to external stimuli, such as visual or haptic cuing. This becomes particularly relevant in timing-sensitive tasks, such as walking or catching a falling object. In this work, we propose a frequency-based assessment of motion that enables us to measure the bandwidth of physical human-robot interaction (pHRI) - quantifying how fast individuals can respond to stimuli on a continuous basis. We introduce a robot-assisted virtual dynamic task with a tunable resonant frequency. A human subject study with seven participants shows that our task can elicit a dynamic response in a participant at frequencies of 0.5 Hz, 1 Hz, 1.5 Hz and 2.5 Hz at the arm. Using the virtual task, we test whether haptic cues improve motion timing. At all tested frequencies, we find that haptic stimuli help guide timing of dynamic movement and improve performance compared to visual-only cuing. By quantifying the interaction bandwidth for other pHRI systems - particularly when the human collaborators have neuromotor impairments - our method can help assistive robots adapt to an individual. Moreover, our results highlight the importance of incorporating haptic feedback into pHRI for dynamic tasks - haptics can provide guidance around motion timing, such as in assistive robots used for assessment and physical rehabilitation.

Original languageEnglish (US)
Pages (from-to)12467-12474
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number4
DOIs
StatePublished - Oct 1 2022

Keywords

  • Haptics and haptic interfaces
  • human factors and human-in-the-loop
  • physical human-robot interaction
  • physically assistive devices
  • rehabilitation robotics

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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