CAREER: Scalable and Adaptable Cross-Domain Autonomous Health Assessment

商品智能家居传感器系统（Google Home，Amazon Echo等）的广泛可用性和物联网（IoT）设备（Fitbit、Actigraph等）使其更容易连续监测个人的健康相关的生命信号、活动和行为，以向老龄化人口提供及时的健康干预。该CAREER项目旨在设计，实施和评估智能家居中的异构传感器系统，以帮助改善老年人的渐进性功能和行为健康下降。这项工作特别关注跨领域方法，这些方法可以适应大量人口和各种智能家居传感器系统中行为，活动和生理健康状况的变化。无法跨域构建可扩展和自适应的活动和行为监控模型，例如具有异构物联网设备的多住户家庭，这是将智能家居技术用于医疗保健应用的主要障碍。该项目开发了新的深度迁移学习技术，基于优化的算法，机会主义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，同时利用人在回路控制来提高传感器系统的性能。这些技术将有助于大规模自动化活动和生理健康监测，从而改善老年人，他们的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，有必要在多个居民和各种连接的消费者设备上构建活动和行为识别系统，该系统可以选择，适应和科普设备和用户的异质性，隐私特性，资源约束和标记数据的稀缺性。为了解决上述问题，本研究项目从四个方面为新方法论做出了贡献。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算法，这些算法可以自动帮助适应不同领域的固有变化，包括用户/设备类型/设备实例。其次，设计了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化，并捕获人体生理健康标志物的自相似性，并建立功能和行为健康测量之间的参数任务性能依赖关系。第三，它提出了一个机会主义的感知架构和人在环活动模型的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，它正在设计实际智能家居部署中定制计算技术的分布式实现，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实时健康评估的有效性。除了有针对性地关注教育外，还与马里兰州大学进行合作，护理学院正在利用智能家居传感器系统和技术的真实的部署在三个退休社区中心和老年人之家在大巴尔的摩地区，以复合的影响，提出的证据-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.