Wearable Biosensor and Hotspot Analysis-Based Framework to Detect Stress Hotspots for Advancing Elderly's Mobility
Wearable Biosensor and Hotspot Analysis-Based Framework to Detect Stress Hotspots for Advancing Elderly's Mobility
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DOI：
10.1061/(asce)me.1943-5479.0000753
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发表时间：
2020-05-01
影响因子：
7.4
通讯作者：
Lee, SangHyun
中科院分区：
文献类型：
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作者：
Lee, Gaang;Choi, Byungjoo;Lee, SangHyun
随着老年人口持续快速增长，老年人的流动性不仅成为他们个人福祉的主要关注点，而且也是我们社会繁荣的主要关注点。尽管如此重要，老年人的流动性仍然有限，因为各种类型的紧张互动与建筑环境在他们的日常旅行。最近，智能城市数字孪生模型的引入已经证明了模拟和优化干预措施的潜力，这些干预措施可以最大限度地减少老年人与建筑环境之间的压力互动。尽管有这样的潜力，但目前数字孪生中的城市感知只收集了基本水平的交互数据，例如人们的位置和轨迹。可穿戴生物传感器的最新进展使我们能够在不干扰老年人日常生活的情况下测量他们的压力，这可以大大增强当前Digital Twins分析平台的能力。在本文中，作者提出了一种可穿戴生物传感器和热点分析为基础的框架，以持续监测老年人的压力与建筑环境的相互作用。具体而言，本研究旨在：（1）创建一个计算模型，以识别来自使用可穿戴生物传感器在日常旅行环境中收集的不同生理信号的个体压力;（2）开发基于GIS的热点分析，以检测压力热点，老年人与建筑环境有压力的相互作用。为了验证所提出的框架，压力热点检测的基础上收集的数据，在2周的日常旅行的30名老年人。然后通过现场检查和与受试者的访谈对检测到的压力热点进行调查。结果表明，压力热点与老年人与建筑环境的压力互动存在空间相关性。研究结果表明，可穿戴生物传感器的热点分析可以检测老年人和建筑环境之间的时空压力相互作用。所提出的传感框架加强了智慧城市数字孪生范例，以更加以人为中心的模拟可视化老年人与建筑环境的紧张互动，这可以成为优化干预措施以改善老年人流动性的基础。
As the elderly population continues to grow rapidly, the mobility of elderly individuals has become a primary concern for not only their individual well-being, but also our social prosperity. Despite such importance, the elderly's mobility remains limited because of various types of stressful interactions with the built environment in their daily trips. Recently, the introduction of a Smart City Digital Twins paradigm has demonstrated the potential to simulate and optimize interventions that minimize stressful interactions between elderly individuals and the built environment. Despite such potential, the current urban sensing in the Digital Twins has only gathered a rudimentary level of interaction data, such as people's locations and trajectories. Recent advancements in wearable biosensors enable us to measure stress in elderly people without interfering with their daily lives, which can greatly strengthen the capability of the current Digital Twins' analytics platform. In this paper, the authors propose a wearable biosensor and hotspot analysis-based framework to continuously monitor the elderly's stressful interactions with the built environment. Specifically, this study aims to: (1) create a computational model to identify individual stress from different physiological signals collected in daily trip contexts using wearable biosensors; and (2) develop a GIS-based hotspot analysis to detect stress hotspots, on which elderly individuals have stressful interactions with the built environment. To test the proposed framework, stress hotspots were detected based on 30 elderly subjects' data collected during 2 weeks of their daily trips. The detected stress hotspots were then investigated by site inspections and interviews with subjects. The results showed that the detected stress hotspots are spatially correlated with the elderly subjects' stressful interactions with the built environment. The findings demonstrate that a hotspot analysis with wearable biosensors can detect spatiotemporal stressful interactions between the elderly and the built environment. The proposed sensing framework strengthens the Smart City Digital Twins paradigm for more human-centered simulation visualizing elderly individuals' stressful interactions with the built environment, which can be a basis for optimizing interventions to improve the elderly's mobility.