Environment and Behavior

Wearable Sensing and Mining of the Informativeness of Older Adults‘ Physiological, Behavioral, and Cognitive Responses to Detect Demanding Environmental Conditions

Alex Torku, Albert P. C. Chan, Esther H. K. Yung, JoonOh Seo, Maxwell F. Antwi-Afari

Kingston University London, Kingston upon Thames, UK


older adult, environmental demand, wearable sensing, bodily response, information mining

older adult, environmental demand, wearable sensing, bodily response, information mining


Due to the decline in functional capability, older adults are more likely to encounter excessively demanding environmental conditions (that result in stress and/or mobility limitation) than the average person. Current efforts to detect such environmental conditions are inefficient and are not person-centered. This study presents a more efficient and person-centered approach that involves using wearable sensors to collect continuous bodily responses (i.e., electroencephalography, photoplethysmography, electrodermal activity, and gait) and location data from older adults to detect demanding environmental conditions. Computationally, this study developed a Random Forest algorithm—considering the informativeness of the bodily response—and a hot spot analysis-based approach to identify environmental locations with high demand. The approach was tested on data collected from 10 older adults during an outdoor environmental walk. The findings demonstrate that the proposed approach can detect demanding environmental conditions that are likely to result in stress and/or limited mobility for older adults.

Moticon's Schlussfolgerungen

This paper addresses the increasing aging population and the associated functional decline, which impacts mobility and interaction with urban environments. Traditional urban planning often overlooks the unique needs of older adults, leading to reduced mobility and further health decline. The study aims to assess older adults' physiological, behavioral, and cognitive responses to various environmental conditions using wearable sensors in a naturalistic setting. Data from smart wristbands, Moticon sensor insoles, and EEG sensors were analyzed to identify informative features about environmental demand. Results showed that specific physiological responses, particularly those measured by PPG and EDA, effectively captured environmental demands. The study concludes that integrating site audits with people-centric wearable sensing can provide a holistic and timely assessment of urban environments, improving walkability and the overall quality of life for older adults.

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