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

Bibliographic Data

ID21314795
AuthorsAlex Torku (0000-0002-2509-9962, Kingston University London, Kingston upon Thames, UK, corresponding author), Albert P C Chan (0000-0002-4853-6440, The Hong Kong Polytechnic University, Hung Hom, Hong Kong), Esther H K Yung (0000-0003-0028-9062, The Hong Kong Polytechnic University, Hung Hom, Hong Kong), JoonOh Seo (0000-0002-5377-7142, The Hong Kong Polytechnic University, Hung Hom, Hong Kong), Maxwell Fordjour Antwi‐Afari (0000-0002-6812-7839, Aston University), Maxwell F Antwi-Afari (Aston University, Birmingham, UK)
Year2022
Volume54
Issue6
Pages1005-1057
Publication date2022-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Behavior (JOURNAL)
Journal identifiersISSN: 0013-9165 • E-ISSN: 1552-390X
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/00139165221114894
OpenAlexW4288697531
LanguageEN
Citations received3
References cited84

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

Cognition · Electroencephalography · Environmental stress · Gait · Photoplethysmogram · Physical medicine and rehabilitation · Random forest · Wearable computer · Wearable technology · Artificial Intelligence · Computer Science · Context-Aware Activity Recognition Systems · Ecology · Medicine · Noise Effects and Management · Physical Activity and Health · Psychology

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