Identification of Built Environment Walking Barriers for the Elderly: Mining Stress Hotspots Based on Physiological Signals

Mobility is essential for the life quality of the elderly. With the physical and cognitive decline, various barriers in public infrastructure trigger stress in their daily walking and reduce their mobility. While hybrid methods for detecting walking stress hotspots in the built environment using wearable physiological sensors have been developed, there has been limited exploration of the correlation between these stress hotspots and specific types of barriers throughout the lifecycle of public infrastructure. Therefore, this study proposed a research framework from the perspective of facility lifecycle management that integrated physiological signals, machine learning algorithms, and geographical analysis. The framework systematically identified the critical environmental barriers affecting elderly mobility throughout the public infrastructure lifecycle, providing a deeper understanding of how these barriers collectively impact mobility. Sample data were collected with wearable biosensors, and the walking stress hotspots were detected in ArcGIS. Then the identified hotspots were correlated to seven types of barriers in planning, designing, and operation stages of public infrastructure to discover critical barriers. Lastly, the identified critical barriers were validated with another experiment in a different neighborhood. Results showed that the proposed method successfully revealed the most important public infrastructure barriers in the built environment for the mobility of the elderly, exploring the combined impact of multiple environmental barriers on elderly mobility and providing a deeper understanding of their collective influence. Optimization strategies in the three stages of public infrastructure lifecycle were then proposed for the improvement of life quality and well-being of the elderly.