Evaluation by Expert Dancers of a Robot That Performs Partnered Stepping via Haptic Interaction

• Type of Material: Article
• Authors: Tiffany L. Chen, Georgia Institute of Technology; Tapomayukh Bhattacharjee, Georgia Institute of Technology; J. Lucas McKay, Emory University; Jacquelyn E. Borinski, Georgia Institute of Technology; Madeleine Hackney, Emory University; Lena Ting, Emory University; Charles C. Kemp, Georgia Institute of Technology
• Language: English
• Date: 2015-05-20
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2015, Public Library of Science. All rights reserved.
• License: Creative Commons Creative Commons CC0 1.0 Universal
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0125179
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 10
• Issue: 5
• Start Page: e0125179
• End Page: e0125179
• Grant/Funding Information: Funding was provided by the NSF Graduate Research Fellowship Program (GRFP), and National Science Foundation EFRI-M3C: Partnered Rehabilitative Movement: Cooperative Human-Robot Interactions for Motor Assistance, Learning, and Communication Award #: 1137229, MH LT CK
• Keywords: Science & Technology - Other Topics; Multidisciplinary Sciences; PARKINSONS-DISEASE; MOVEMENT; SUBACUTE STROKE; INTERFACE; Science & Technology; GAIT
• Research Categories: Engineering, Biomedical

Abstract

Our long-term goal is to enable a robot to engage in partner dance for use in rehabilitation therapy, assessment, diagnosis, and scientific investigations of two-person whole-body motor coordination. Partner dance has been shown to improve balance and gait in people with Parkinson's disease and in older adults, which motivates our work. During partner dance, dance couples rely heavily on haptic interaction to convey motor intent such as speed and direction. In this paper, we investigate the potential for a wheeled mobile robot with a human-like upper-body to perform partnered stepping with people based on the forces applied to its end effectors. Blindfolded expert dancers (N=10) performed a forward/backward walking step to a recorded drum beat while holding the robot's end effectors. We varied the admittance gain of the robot's mobile base controller and the stiffness of the robot's arms. The robot followed the participants with low lag (M=224, SD=194 ms) across all trials. High admittance gain and high arm stiffness conditions resulted in significantly improved performance with respect to subjective and objective measures. Biomechanical measures such as the human hand to human sternum distance, center-of-mass of leader to center-of-mass of follower (CoM-CoM) distance, and interaction forces correlated with the expert dancers' subjective ratings of their interactions with the robot, which were internally consistent (Cronbach's α=0.92). In response to a final questionnaire, 1/10 expert dancers strongly agreed, 5/10 agreed, and 1/10 disagreed with the statement "The robot was a good follower." 2/10 strongly agreed, 3/10 agreed, and 2/10 disagreed with the statement "The robot was fun to dance with." The remaining participants were neutral with respect to these two questions.