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While recent advancements in motor learning have emphasized the critical role of systematic task scheduling in enhancing task learning, the heuristic design of task schedules remains predominant. Random task scheduling can lead to sub-optimal motor learning, whereas performance-based scheduling might not be adequate for complex motor skill acquisition. This paper addresses these challenges by proposing a model-based approach for online skill estimation and individualized task scheduling in de-novo (novel) motor learning tasks. We introduce a framework utilizing a personalized human motor learning model and particle filter for skill state estimation, coupled with a stochastic nonlinear model predictive control (SNMPC) strategy to optimize curriculum design for a high-dimensional motor task. Simulation results show the effectiveness of our framework in estimating the latent skill state, and the efficacy of the framework in accelerating skill learning. Furthermore, a human subject study shows that the group with the SNMPC-based curriculum design exhibited expedited skill learning and improved task performance. Our contributions offer a pathway towards expedited motor learning across various novel tasks, with implications for enhancing rehabilitation and skill acquisition processes.
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Human-machine-human interaction in motor control and rehabilitation: a review
Abstract BackgroundHuman-human (HH) interaction mediated by machines (e.g., robots or passive sensorized devices), which we call human-machine-human (HMH) interaction, has been studied with increasing interest in the last decade. The use of machines allows the implementation of different forms of audiovisual and/or physical interaction in dyadic tasks. HMH interaction between two partners can improve the dyad’s ability to accomplish a joint motor task (task performance) beyond either partner’s ability to perform the task solo. It can also be used to more efficiently train an individual to improve their solo task performance (individual motor learning). We review recent research on the impact of HMH interaction on task performance and individual motor learning in the context of motor control and rehabilitation, and we propose future research directions in this area. MethodsA systematic search was performed on the Scopus, IEEE Xplore, and PubMed databases. The search query was designed to find studies that involve HMH interaction in motor control and rehabilitation settings. Studies that do not investigate the effect of changing the interaction conditions were filtered out. Thirty-one studies met our inclusion criteria and were used in the qualitative synthesis. ResultsStudies are analyzed based on their results related to the effects of interaction type (e.g., audiovisual communication and/or physical interaction), interaction mode (collaborative, cooperative, co-active, and competitive), and partner characteristics. Visuo-physical interaction generally results in better dyadic task performance than visual interaction alone. In cases where the physical interaction between humans is described by a spring, there are conflicting results as to the effect of the stiffness of the spring. In terms of partner characteristics, having a more skilled partner improves dyadic task performance more than having a less skilled partner. However, conflicting results were observed in terms of individual motor learning. ConclusionsAlthough it is difficult to draw clear conclusions as to which interaction type, mode, or partner characteristic may lead to optimal task performance or individual motor learning, these results show the possibility for improved outcomes through HMH interaction. Future work that focuses on selecting the optimal personalized interaction conditions and exploring their impact on rehabilitation settings may facilitate the transition of HMH training protocols to clinical implementations.
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- Award ID(s):
- 2024488
- PAR ID:
- 10361188
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of NeuroEngineering and Rehabilitation
- Volume:
- 18
- Issue:
- 1
- ISSN:
- 1743-0003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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