SCH: INT: Collaborative Research: Aging In Place Through Enhanced Mobility and Social Connectedness: An Integrated Robot and Wearable Sensor Approach

SCH：INT：协作研究：通过增强移动性和社交联系进行就地老龄化：集成机器人和可穿戴传感器方法

• 批准号: 1838799
• 负责人: Yi Guo
• 金额: $ 92.72万
• 依托单位: Stevens Institute of Technology
• 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: 2019-01-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838799&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Aging

The concept of "aging in place", using advanced technology to improve the health and well-being of older adults at home, has become popular as away to reduce costs and allow older adults the dignity of living their final years in the community. This project will use an integrated autonomous system that consists of a mobile robot and smart insole sensors to assist older adults to independently live in their own homes and interact with their communities. The system will have two initial goals: supporting exercise and enhancing social connections. The target exercise is walking, the most preferred and accessible exercise modality among older adults. Regular walking exercises may result in enhanced balance, increased muscle strength, and reduced risk of falling. The system guides individuals in regular walking exercises, autonomously assesses gait states, and provides real-time personalized feedback to engage older adults into the exercise. The robot will also be used to connect older adults with family and friends through a virtual connection. The project team will evaluate the system at a senior center in New York City using objective and subjective performance criteria measuring older adults' experiences with the system. This project serves the national interest because the integrated social assistive robot and wearable sensor system enhances mobility and social connectedness of older adults thus should improve their health and well-being. The project will involve an educational component that provides engineering and research method training to graduate and undergraduate students, as well as STEM outreach to middle and high-school students. Additional efforts will be made to attract and retain women and underrepresented minorities into careers in science and engineering.This research investigates human-robot-sensor interaction, and aims to enhance mobility and social connectedness of older adults through the use of an assistive service robot. Methods to be developed include autonomous gait analysis combining sensing capability of robot onboard image sensors and the smart insoles through parametric and learning-based calibration models. The use of inverse reinforcement learning to explore cost function representation for robot motion planning. The use of dynamic recurrent neural networks and vibrotactile rhythmic stimuli for collaborative human-robot walking tasks. The project also examines the use of autonomous task scheduling to increase social contacts through telepresence robotic techniques. Finally, the implementation and experimental testing of the integrated system will be conducted in a senior center using objective and subjective performance criteria. The project fills a gap in aging-in-place research by providing an integrated robot and wearable sensor solution for enhancement of mobility and social connectedness. The developed methods will be implemented on open-source platforms, and the experimental and evaluation data will be made available for public use.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.

“居家养老”的概念，即利用先进技术改善居家老年人的健康和福祉，已成为流行，因为它可以降低成本，让老年人有尊严地在社区度过晚年。该项目将使用由移动机器人和智能鞋垫传感器组成的集成自主系统，帮助老年人独立生活在自己的家中并与社区互动。 该系统将有两个初始目标：支持锻炼和增强社交联系。目标锻炼是步行，这是老年人最喜欢、最容易接受的锻炼方式。定期步行锻炼可以增强平衡能力、增强肌肉力量并降低跌倒风险。 该系统指导个人进行定期步行锻炼，自动评估步态状态，并提供实时个性化反馈以吸引老年人参与锻炼。该机器人还将用于通过虚拟连接将老年人与家人和朋友联系起来。项目团队将使用衡量老年人使用该系统的体验的客观和主观绩效标准来评估纽约市一家老年人中心的系统。该项目符合国家利益，因为集成的社会辅助机器人和可穿戴传感器系统增强了老年人的移动性和社会联系，从而改善了他们的健康和福祉。该项目将涉及教育部分，为研究生和本科生提供工程和研究方法培训，并为中学生和高中生提供 STEM 推广。我们将做出更多努力来吸引和留住女性和代表性不足的少数族裔进入科学和工程领域。这项研究调查了人机传感器交互，旨在通过使用辅助服务机器人来增强老年人的移动性和社会联系。待开发的方法包括通过参数化和基于学习的校准模型结合机器人机载图像传感器和智能鞋垫的传感能力的自主步态分析。使用逆强化学习探索机器人运动规划的成本函数表示。使用动态循环神经网络和振动触觉节律刺激来执行人机协作步行任务。该项目还研究了如何使用自主任务调度，通过远程呈现机器人技术来增加社交联系。最后，集成系统的实施和实验测试将在高级中心使用客观和主观性能标准进行。该项目通过提供集成机器人和可穿戴传感器解决方案来增强移动性和社会联系，填补了就地老龄化研究的空白。所开发的方法将在开源平台上实施，实验和评估数据将可供公众使用。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，认为值得支持标准。

项目成果

Gait Analysis with an Integrated Mobile Robot and Wearable Sensor System Reveals Associations Between Cognitive Ability and Dynamic Balance in Older Adults

使用集成移动机器人和可穿戴传感器系统进行步态分析揭示了老年人认知能力与动态平衡之间的关联

• DOI: 10.1109/biorob52689.2022.9925280
• 发表时间: 2022-08
• 期刊: IEEE International Conference on Biomedical Robotics & Biomechatronics
• 作者: Zhao, Qingya;Chen, Zhuo;Landis, Corey D.;Lytle, Ashley;Rao, Ashwini K.;Guo, Yi;Zanottot, Damiano
• 通讯作者: Zanottot, Damiano

Learning Decentralized Control Policies for Multi-Robot Formation

学习多机器人编队的分散控制策略

• DOI: 10.1109/aim.2019.8868898
• 发表时间: 2019-07
• 期刊: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM
• 作者: Jiang, Chao;Chen, Zhuo;Guo, Yi
• 通讯作者: Guo, Yi

Multi-robot formation control: a comparison between model-based and learning-based methods

多机器人编队控制：基于模型和基于学习的方法之间的比较

• DOI: 10.1080/23307706.2019.1697970
• 发表时间: 2019-12
• 期刊: Journal of Control and Decision
• 影响因子: 1.7
• 作者: Jiang, Chao;Chen, Zhuo;Guo, Yi
• 通讯作者: Guo, Yi

Optimization of Merging Pedestrian Flows Based on Adaptive Dynamic Programming

基于自适应动态规划的合流人流优化

• 发表时间: 2019-07
• 期刊: Proceedings of the ... American Control Conference
• 作者: Jiang, Chao;Ni, Zhen;Guo, Yi;He, Haibo
• 通讯作者: He, Haibo

Gait monitoring for older adults during guided walking: An integrated assistive robot and wearable sensor approach

引导步行期间老年人的步态监测：集成辅助机器人和可穿戴传感器方法

• DOI: 10.1017/wtc.2022.23
• 发表时间: 2022-01
• 期刊: Wearable Technologies
• 作者: Zhao, Qingya;Chen, Zhuo;Landis, Corey D.;Lytle, Ashley;Rao, Ashwini K.;Zanotto, Damiano;Guo, Yi
• 通讯作者: Guo, Yi