Wearable Biosensor and Hotspot Analysis-Based Framework to Detect Stress Hotspots for Advancing Elderly's Mobility

Abstract

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.