CAREER: Structures as Sensors: Elder Activity Level Monitoring through Structural Vibrations

这个教师早期职业发展计划（CAREER）项目的目标是使“智能建筑”能够定位和识别特定的个人，并根据脚步声引起的建筑结构振动对他们的活动进行分类。老年人护理设施旨在维持或改善老年人的生活质量和独立性，同时降低护理专业人员的成本和能力需求。实现这一目标的一个关键是了解每个居住者的活动。现有的监视居住者的解决方案，例如视觉、声学、运动和力传感器以及移动的设备，具有严格的安装要求。这些需求导致侵入式和密集部署，或者需要用户主动参与。相反，这个项目是建立在感知居住者在步行活动中产生的振动的基础上的。 使用建筑物振动来监测居住者允许使用廉价的振动传感器进行非侵入式和可扩展的监测。更一般地说，这项研究将使智能建筑能够使用“结构作为传感器”以主要的方式感知，跟踪和预测居住者的状态，从而实现未来的居住者感知应用。同样，该技术可以定位建筑物的部分湿滑或不安全的基础，或检测未经授权的人在限制区域的存在。通过跟踪第一反应者和定位危险的平民，这种系统还将帮助调度员减轻紧急情况。该项目包括在三个不同的老年护理设施中进行概念验证部署。本项目将利用建筑物本身作为活动传感器，通过被动地感知脚步引起的地板振动，并在贝叶斯框架中采用先进的稀疏信号近似，提取个人活动信息。具体的研究方向是：（1）利用分层小波分解和结构稀疏正则化，从由多个人源引起的噪声混合信号中提取个体脚步引起的地板振动信号：（2）通过动态融合来自多个频率分量的信息并利用贝叶斯更新的人的移动和结构振动模式来定位个体脚步;以及（3）通过迭代地融合位置信息和信号分离来提高模型精度。这些努力的关键新奇在于融合信号处理方法和物理约束，以应对真实的世界挑战。

The goal of this Faculty Early Career Development Program (CAREER) project is to enable "smart buildings" that can locate and identify specific individuals, and classify their activity, based only on the vibrations of the building structure caused by footsteps. Elder care facilities aim to maintain or improve the quality of life and independence of elders while reducing costs and capacity needs for care-professionals. One key to achieving this goal is to understand the activities of each occupant. Existing solutions to monitor occupants, such as vision, acoustic, motion, and force sensors and mobile devices, have strict installation requirements. These requirements lead to intrusive and dense deployment or require active user involvements. Instead, this project is built upon sensing the vibrations created by occupants' during their walking activity. Using building vibration to monitor occupants allows non-intrusive and scalable monitoring with inexpensive vibration sensors. More generally, this research will enable smart buildings to sense, track, and predict the status of occupants in a maintainable way using "structures as sensors" and thus enable future occupant-aware applications. Similarly, the technology can locate portions of a building with slippery or unsafe footing, or detect the presence of unauthorized people in restricted areas. By tracking first responders and locating imperiled civilians, such systems will also help dispatchers to mitigate emergencies. The project includes proof-of-concept deployments in three different elder care facilities. Targeted outreach activities will highlight the capabilities of this technology at an appropriate level of detail to appeal to female middle-school students.This project uses structures themselves as activity sensors, by passively sensing footstep-induced floor vibrations, and employing advanced sparse-signal approximation in a Bayesian framework, to extract individual activity information. The specific research thrusts are: (1) extracting individual persons' footstep-induced floor vibration signal from a noisy signal mixture due to multiple human sources, by exploiting hierarchical wavelet decompositions and applying structured sparsity regularization; (2) localizing individual footsteps by dynamically fusing information from multiple frequency components and leveraging human mobility and structural vibration patterns through Bayesian updating; and (3) improving model accuracy by iteratively fusing location information and signal separation. The key novelty in these thrusts lies in fusion of signal processing methods and physical constraints to address real world challenges.