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Quantifying Changes in Kinematic Behavior of a Human-Exoskeleton Interactive System.
Keya
Ghonasgi, Reuth Mirsky, Adrian M Haith, Peter
Stone, and Ashish D Deshpande.
In Proceedings of the 35th International Conference on Intelligent Robots and Systems
(IROS), October 2022.
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.
@InProceedings{IROS22-ghonasgi,
author = {Keya Ghonasgi and Reuth Mirsky and Adrian M Haith and Peter Stone and Ashish D Deshpande},
title = {Quantifying Changes in Kinematic Behavior of a Human-Exoskeleton Interactive System},
booktitle = {Proceedings of the 35th International Conference on Intelligent Robots and Systems (IROS)},
location = {Kyoto, Japan},
month = {October},
year = {2022},
abstract = {
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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