Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation
Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation
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DOI：
10.1146/annurev-control-042920-093225
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
A. Kubota;L. Riek
中科院分区：
文献类型：
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作者：
A. Kubota;L. Riek
据估计，11%的成年人报告经历了某种形式的认知能力下降，这可能与中风或痴呆等疾病有关，并可能影响他们的记忆、认知、行为和身体能力。虽然没有已知的药物治疗这些疾病中的许多，行为治疗，如认知训练可以延长认知障碍的人的独立性。这些治疗方法教授元认知策略，以弥补他们日常生活中的记忆困难。个性化这些治疗，以适应个人的喜好和目标是至关重要的，以提高他们的参与和维持，以及最大限度地提高治疗的有效性。机器人有很大的潜力来促进这些训练方案，并支持有认知障碍的人，他们的照顾者和临床医生。本文探讨了机器人如何在认知神经康复的背景下适应他们的行为，以个性化的个人。我们概述了用于支持神经康复的现有机器人，并确定了在这一领域工作的关键原则。然后，我们研究国家的最先进的技术方法，使纵向行为适应。最后，我们讨论了我们最近的工作，使社交机器人能够自动适应他们的行为，并探索纵向行为适应的开放性挑战。这项工作将有助于指导机器人社区，因为它将继续在人与机器人之间提供更具吸引力，有效和个性化的互动。《控制、机器人和自主系统年度评论》第5卷的预计最终在线出版日期为2022年5月。请访问http://www.annualreviews.org/page/journal/pubdates了解修订后的估计数。
An estimated 11% of adults report experiencing some form of cognitive decline, which may be associated with conditions such as stroke or dementia and can impact their memory, cognition, behavior, and physical abilities. While there are no known pharmacological treatments for many of these conditions, behavioral treatments such as cognitive training can prolong the independence of people with cognitive impairments. These treatments teach metacognitive strategies to compensate for memory difficulties in their everyday lives. Personalizing these treatments to suit the preferences and goals of an individual is critical to improving their engagement and sustainment, as well as maximizing the treatment's effectiveness. Robots have great potential to facilitate these training regimens and support people with cognitive impairments, their caregivers, and clinicians. This article examines how robots can adapt their behavior to be personalized to an individual in the context of cognitive neurorehabilitation. We provide an overview of existing robots being used to support neurorehabilitation and identify key principles for working in this space. We then examine state-of-the-art technical approaches for enabling longitudinal behavioral adaptation. To conclude, we discuss our recent work on enabling social robots to automatically adapt their behavior and explore open challenges for longitudinal behavior adaptation. This work will help guide the robotics community as it continues to provide more engaging, effective, and personalized interactions between people and robots. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.