While human-robot interaction studies are becoming more common, quantification of the effects of repeated interaction with an exoskeleton remains unexplored. We draw upon existing literature in human skill assessment and present extrinsic and intrinsic performance metrics that quantify how the human-exoskeleton system's behavior changes over time. Specifically, in this paper, we present a new performance metric that provides insight into the system's kinematics associated with `successful' movements resulting in a richer characterization of changes in the system's behavior. A human subject study is carried out wherein participants learn to play a challenging and dynamic reaching game over multiple attempts, while donning an upper-body exoskeleton. The results demonstrate that repeated practice results in learning over time as identified through the improvement of extrinsic performance. Changes in the newly developed kinematics-based measure further illuminate how the participant's intrinsic behavior is altered over the training period. Thus, we are able to quantify the changes in the human-exoskeleton system's behavior observed in relation with learning.

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In Proceedings of the 35th International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, October 2022.

@inproceedings{IROS22-ghonasgi, title={Quantifying Changes in Kinematic Behavior of a Human-Exoskeleton Interactive System}, author={Keya Ghonasgi and Reuth Mirsky and Adrian M Haith and Peter Stone and Ashish D Deshpande}, booktitle={Proceedings of the 35th International Conference on Intelligent Robots and Systems (IROS)}, month={October}, address={Kyoto, Japan}, url="http://www.cs.utexas.edu/users/ai-lab?IROS22-ghonasgi", year={2022} }