Wearable Biosensor and Collective Sensing-Based Approach for Detecting Older Adults' Environmental Barriers

Abstract

In this rapidly aging society, the mobility of older adults is critical for the prosperity and well-being of communities. Despite such importance, various types of environmental barriers (e.g., steep slopes and uneven sidewalks) have limited their mobility. Recent wearable biosensors have shown the potential to less invasively, less laboriously, and continuously detect environmental barriers by measuring stress in older adults' daily trips. However, stress alone could not be indicative of environmental barriers because various stress stimuli (e.g., emotions and physical fatigue) are mixed up in their daily trips. To fill this gap, the authors propose and test a computational approach that spatially identifies stress resulting from environmental barriers by aggregating multiple people's physiological and location data. The proposed approach measures stress commonly sensed from multiple people in a specific location (collective stress) as an indication of environmental barriers, applying wearable biosensors, signal processing, and geocoding. To test the feasibility of the proposed approach, collective stress was compared between locations with and without environmental barriers based on 2 weeks of field data collected from the daily trips of 16 subjects. As a result, the collective stress was statistically higher in the locations with environmental barriers than without. This result shows that the proposed approach is feasible to compute collective stress measures that are indicative of environmental barriers. This finding contributes to the body of knowledge by confirming the feasibility of a new computational approach that understands locational stress-inducing factors by spatially aggregating multiple people's physiological signals using wearable biosensors, signal processing, and geocoding. Given the feasibility of the proposed approach to detect environmental barriers, future studies can generate and validate a less invasive, less laborious, and continuous method to detect environmental barriers, which can facilitate mobility improvement.