CAREER: Scalable and Adaptable Cross-Domain Autonomous Health Assessment

职业：可扩展且适应性强的跨域自主健康评估

• 批准号: 1750936
• 负责人: Nirmalya Roy
• 金额: $ 55.03万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750936&HistoricalAwards=false
• 关键词: CAREER; Scalable; Adaptable; Cross; Domain

The wide availability of commodity smart home sensor systems (Google Home, Amazon Echo, etc.) and internet-of-things (IoT) devices (Fitbit, Actigraph, etc.) is making it easier to continuously monitor individuals' health-related vital signals, activities, and behaviors to provide just-in-time health intervention to the aging population. This CAREER project seeks to design, implement, and evaluate heterogeneous sensor systems in smart homes that help ameliorate the progressive functional and behavioral health decline of older adults. The work specifically looks at cross-domain approaches that can accommodate variability in behavior, activity, and physiological health conditions across a large population and diverse set of smart home sensor systems. The inability to build scalable and adaptable activity and behavior monitoring models across domains such as multi-occupant homes with heterogeneous internet-of-things devices is a major impediment to adoption of smart home technologies for healthcare applications. The project develops novel deep transfer learning techniques, optimization-based heuristics, opportunistic sensing architecture, and spatiotemporal dynamical systems-based approaches to address the diversity, adaptability, and reliability of activity and behavior recognition models across different users and technologies, while leveraging a human-in-the-loop control for improving the performance of the sensor systems. These techniques will help automate activity and physiological health monitoring at scale, and thereby improve the design and study of adaptive interventions for elderly people, their families, and professional caregivers. In order to realize autonomous health assessment methodologies in practice, it is necessary to build an activity and behavior recognition system across multiple inhabitants and various connected consumer devices that can select, adapt, and cope with device and user heterogeneities, privacy characteristics, resource constraints and scarcity of labeled data. To address the above-mentioned problems, this research project contributes to new methodology in four ways. First, it is introducing deep transfer learning activity recognition model and multi-user multi-device optimization-based heuristics that automatically help adapt the inherent variations across different domains, including user/device-type/device-instance. Second, it is designing a spatio-temporal dynamical system approach based on fractal dynamics to mitigate the variability in various sensor signals, and capture the self-similarity of human physiological health markers and establish the parametric task performance dependency between functional and behavioral health measurements. Third, it posits an opportunistic sensing architecture and human-in-the loop activity model for real-time data sharing and annotation that help optimize the user interruption and system performance. Fourth, it is designing a distributed implementation of tailored-computational techniques in actual smart home deployments, and evaluating the effectiveness of sensor-based functional and behavioral models and algorithms for just-in-time health assessment in actual living environments. In addition to the targeted focus on education, an ongoing collaboration with the University of Maryland, School of Nursing is being leveraged for real deployment of smart home sensor systems and technologies at three retirement community centers and senior homes in the greater Baltimore area to compound the impact of proposed evidence-based research efforts.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.

商品智能家居传感器系统（Google Home、Amazon Echo 等）和物联网 (IoT) 设备（Fitbit、Actigraph 等）的广泛使用，使得持续监测个人健康相关生命体变得更加容易。为老龄化人口提供及时健康干预的信号、活动和行为。该职业项目旨在设计、实施和评估智能家居中的异构传感器系统，以帮助改善老年人功能和行为健康状况的逐渐衰退。这项工作特别着眼于跨领域方法，这些方法可以适应大量人群和不同智能家居传感器系统的行为、活动和生理健康状况的变化。无法跨领域（例如具有异构物联网设备的多住户家庭）构建可扩展且适应性强的活动和行为监控模型，是医疗保健应用采用智能家居技术的主要障碍。该项目开发新颖的深度迁移学习技术、基于优化的启发式、机会感知架构和基于时空动态系统的方法，以解决不同用户和技术之间的活动和行为识别模型的多样性、适应性和可靠性，同时利用人类用于提高传感器系统性能的在环控制。这些技术将有助于大规模自动化活动和生理健康监测，从而改进针对老年人、其家人和专业护理人员的适应性干预措施的设计和研究。为了在实践中实现自主健康评估方法，有必要建立一个跨多个居民和各种连接的消费设备的活动和行为识别系统，该系统可以选择、适应和应对设备和用户的异质性、隐私特征、资源限制和标记数据的稀缺。为了解决上述问题，该研究项目在四个方面为新方法做出了贡献。首先，它引入了深度迁移学习活动识别模型和基于多用户多设备优化的启发式方法，可以自动帮助适应不同域（包括用户/设备类型/设备实例）之间的固有变化。其次，它正在设计一种基于分形动力学的时空动力系统方法，以减轻各种传感器信号的变异性，捕获人体生理健康标记的自相似性，并建立功能和行为健康测量之间的参数任务绩效依赖性。第三，它提出了机会感测架构和人机交互活动模型，用于实时数据共享和注释，有助于优化用户中断和系统性能。第四，它正在设计在实际智能家居部署中的定制计算技术的分布式实施，并评估基于传感器的功能和行为模型以及算法在实际生活环境中进行即时健康评估的有效性。除了有针对性地关注教育之外，我们还利用与马里兰大学护理学院的持续合作，在大巴尔的摩地区的三个退休社区中心和高级住宅中实际部署智能家居传感器系统和技术，以增强该奖项反映了 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标准进行评估，被认为值得支持。

项目成果

SocialAnnotator: Annotator Selection by Exploiting Social Relationships in Activity Recognition

SocialAnnotator：通过在活动识别中利用社会关系来选择注释器

• 发表时间: 2018-10
• 期刊: Oct 2018
• 作者: Hossain, S;Roy, N.
• 通讯作者: Roy, N.

CamSense: A Camera-Based Contact-less Heart Activity Monitoring

CamSense：基于摄像头的非接触式心脏活动监测

• DOI: 10.1016/j.smhl.2021.100240
• 发表时间: 2022-03
• 期刊: Smart health
• 作者: Hasan, Zahid;Ramamurthy, Sreenivasan;Roy, Nirmalya
• 通讯作者: Roy, Nirmalya

DeActive: Scaling Activity Recognition with Active Deep Learning

DeActive：通过主动深度学习扩展活动识别

• DOI: 10.1145/3214269
• 发表时间: 2018-07
• 期刊: Wearable and Ubiquitous Technologies
• 作者: Hossain, H. M.;Al Haiz Khan, MD Abdullah;Roy, Nirmalya
• 通讯作者: Roy, Nirmalya

CoDEm: Conditional Domain Embeddings for Scalable Human Activity Recognition

CoDEm：用于可扩展人类活动识别的条件域嵌入

• DOI: 10.1109/smartcomp55677.2022.00017
• 发表时间: 2022-06-01
• 期刊: 2022 IEEE International Conference on Smart Computing (SMARTCOMP)
• 作者: A. Faridee;Avijoy Chakma;Zahid Hasan;Nirmalya Roy;Archan Misra
• 通讯作者: Archan Misra

STAR-Lite: A light-weight scalable self-taught learning framework for older adults’ activity recognition

STAR-Lite：针对老年人活动识别的轻量级可扩展自学学习框架

• DOI: 10.1016/j.pmcj.2022.101698
• 发表时间: 2022-12
• 期刊: Pervasive and Mobile Computing
• 影响因子: 4.3
• 作者: Ramasamy Ramamurthy, Sreenivasan;Ghosh, Indrajeet;Gangopadhyay, Aryya;Galik, Elizabeth;Roy, Nirmalya
• 通讯作者: Roy, Nirmalya