Joint coordination constraints using an upper limb exoskeleton impact novel skill acquisition

Robotic exoskeletons offer the potential to train novel motor skill acquisition and thus aid physical rehabilitation. Our prior work demonstrated that individuals converge to certain kinematic coordinations as they learn a novel task. An upper-limb exoskeleton controller that constrains individuals to this known coordination was also shown to significantly improve straight-line reaching task performance. This paper studies the impact of variations of this controller on novel skill acquisition. We quantify learning under three variations of the intervention (each group with <i>N</i> = 10 participants) against a control group (<i>N</i> = 13). Our results show that introducing any constraint during learning can hinder the learning process, as this alters the task dynamics that lead to success. However, when presented with a personalized constraint, participants still learn. When presented with a task-specific constraint, rather than a personalized one, participants cannot overcome the differences in the training and target task, suggesting exoskeleton-based training interventions should be personalized. The changes in kinematic behaviors during learning further suggest that participants do not have a statistically consistent performance. While participants respond more to exoskeleton intervention, others may not respond in short training sessions, necessitating further analysis of how strong a response can be encouraged. Our findings emphasize the need for further study of the effects of exoskeleton intervention for motor training and the potential need for personalization.

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Publication Details

Journal
Wearable Technologies
Published
2025-01-01
DOI
https://doi.org/10.1017/wtc.2025.10028
Primary Topic
Motor Control and Adaptation
Type
article
Field-Weighted Citation Impact
0.00
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article

Joint coordination constraints using an upper limb exoskeleton impact novel skill acquisition

Peter Stone, Reuth Mirsky, Ashish D. Deshpande, Adrian M. Haith et al.
Wearable Technologies
Motor Control and Adaptation
article

Joint coordination constraints using an upper limb exoskeleton impact novel skill acquisition

Peter Stone, Reuth Mirsky, Ashish D. Deshpande, Adrian M. Haith, Keya Ghonasgi
article en

Abstract

Robotic exoskeletons offer the potential to train novel motor skill acquisition and thus aid physical rehabilitation. Our prior work demonstrated that individuals converge to certain kinematic coordinations as they learn a novel task. An upper-limb exoskeleton controller that constrains individuals to this known coordination was also shown to significantly improve straight-line reaching task performance. This paper studies the impact of variations of this controller on novel skill acquisition. We quantify learning under three variations of the intervention (each group with <i>N</i> = 10 participants) against a control group (<i>N</i> = 13). Our results show that introducing any constraint during learning can hinder the learning process, as this alters the task dynamics that lead to success. However, when presented with a personalized constraint, participants still learn. When presented with a task-specific constraint, rather than a personalized one, participants cannot overcome the differences in the training and target task, suggesting exoskeleton-based training interventions should be personalized. The changes in kinematic behaviors during learning further suggest that participants do not have a statistically consistent performance. While participants respond more to exoskeleton intervention, others may not respond in short training sessions, necessitating further analysis of how strong a response can be encouraged. Our findings emphasize the need for further study of the effects of exoskeleton intervention for motor training and the potential need for personalization.

Wearable TechnologiesVol. 6
Tufts University (US), Johns Hopkins University (US), Sony Corporation (United States) (US), The University of Texas at Austin (US)
Openalex Percentile: Top 69%
Motor Control and Adaptation
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