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DOI 10.1017/wtc.2025.10028

Медицинская библиотека · Wearable technologies · 2025

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

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

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Аннотация

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 N = 10 participants) against a control group (N = 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.

Для цитирования: Ghonasgi Keya, Mirsky Reuth, Haith Adrian M, Stone Peter, Deshpande Ashish D. Joint coordination constraints using an upper limb exoskeleton impact novel skill acquisition.. Wearable technologies. 2025. DOI: 10.1017/wtc.2025.10028.
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