SCH: INT: TAILORED: Training for Independent Living through Observant Robots and Design

SCH：INT：定制：通过观察机器人和设计进行独立生活培训

• 批准号: 1915734
• 负责人: Laurel Riek
• 金额: $ 120万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915734&HistoricalAwards=false
• 关键词: SCH; INT; TAILORED; Training; Independent

The goal of this project is to create human-centered robotics technology to provide personalized neurorehabilitation to support older adults with mild cognitive impairment. The project investigates innovative approaches to this research area, and will make the following contributions to smart and connected health: create new approaches to support longitudinal, personalized robot learning in real-world environments; pioneer novel methods for delivering and sustaining cognitive neurorehabilitation, currently one of the only known treatments to prolong independence and slow the onset of disability caused by MCI; and contribute new methods to the fields of human-robot interaction (HRI), aging science, and behavioral science to support the co-creation of new technologies and new intervention delivery methods with older adults with cognitive impairments, their caregivers, and their providers. Harnessing technology to provide cognitive support and rehabilitation for older adults could potentially assist millions of people to maintain or improve their functioning and quality of life, and maintain their ability to live independently. Ultimately, these improvements could alleviate significant human suffering and lower healthcare costs for millions of people. This project will inform multiple key research questions including: uncovering new methods for longitudinal preference learning, particularly with regard to how time-varying contextual bandits under concept drift can be employed across multimodal datasets; identifying principles for engaging in community-focused, stakeholder-centered research with people with MCI, with a particular focus on designing for resilience and autonomy; discovering how hybrid approaches to cognitive training delivered via a robot can inform cognitive functioning; and exploring how to design interventions for sustainability, both for people with MCI and other populations. The project team will also engage the public in intergenerational research between older adults and college students, empower older adults with cognitive impairments and their family members by giving them a voice in technology creation, recruit research students from groups underrepresented in computing and behavioral science, and broadly disseminate the research via publications, representations, and publicly available software frameworks with models, algorithms, and evaluation metrics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目的是创建以人为本的机器人技术来提供个性化的神经康复，以支持有轻度认知障碍的老年人。该项目调查了该研究领域的创新方法，并将为智能和互联健康做出以下贡献：创建新的方法来支持现实世界环境中的纵向，个性化的机器人学习；先锋提供和维持认知神经康复的新方法，目前是延长独立性并减缓MCI引起的残疾发作的唯一已知治疗方法之一；并为人类机器人相互作用（HRI），老化科学和行为科学的领域贡献新方法，以支持新技术的共同创建以及与患有认知障碍，护理人员及其提供者的老年人的新干预方法。 利用技术为老年人提供认知支持和康复，有可能帮助数百万人维持或改善其功能和生活质量，并保持独立生活的能力。最终，这些改善可以减轻重大的人类苦难，并降低数百万人的医疗保健成本。该项目将为多个关键的研究问题提供信息，包括：揭示纵向偏好学习的新方法，尤其是关于如何在多模式数据集中使用概念上的随时间变化的上下文匪徒；确定与MCI患者以社区为中心的以社区为中心的以社区为中心的研究原则，特别着眼于设计韧性和自治；发现通过机器人提供的认知训练的混合方法如何为认知功能提供信息；并探索如何针对有MCI和其他人群的人设计干预措施。项目团队还将通过向他们发表技术创造的声音，招募计算和行为科学中代表性不足的团体的研究学生，并通过出版物，表达式，陈述，以及评估的模型，Algorith Mettryms，Algorns和Algorith Mynems和Algorith Mynguation Metsriith招募，从而使公众参与老年人和大学生之间的代际研究，赋予他们具有认知障碍的老年人及其家人的能力。法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。

项目成果

Somebody That I Used to Know: The Risks of Personalizing Robots for Dementia Care

我曾经认识的人：个性化机器人进行痴呆症护理的风险

• 发表时间: 2021
• 期刊: In Proceedings of We Robot 2021
• 作者: Kubota, A.;Pourebadi, M;Banh, S;Kim, S;Riek, L.D.
• 通讯作者: Riek, L.D.

Methods for Robot Behavior Adaptation for Cognitive Neurorehabilitation

• DOI: 10.1146/annurev-control-042920-093225
• 发表时间: 2021-09
• 期刊: Annu. Rev. Control. Robotics Auton. Syst.
• 作者: A. Kubota;L. Riek

Compensatory Cognitive Training for Mild Cognitive Impairment

轻度认知障碍的补偿性认知训练

• 发表时间: 2019
• 期刊: UCSD
• 作者: Huckans, M.;Twamley, E;Tun, S.M.;Hutson, L.;Noonan, S.;Savla, G.;Jak, A.;Schiehser, D;Storzbach, D.
• 通讯作者: Storzbach, D.

Thompson Sampling for Robust Transfer in Multi-Task Bandits

• DOI: 10.48550/arxiv.2206.08556
• 发表时间: 2022-06
• 作者: Zhi Wang;Chicheng Zhang;Kamalika Chaudhuri

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

通过异构反馈聚合进行多任务强盗学习

• 发表时间: 2021
• 期刊: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS
• 作者: Wang, Z.;Chaudhuri, K.
• 通讯作者: Chaudhuri, K.