TY - JOUR
T1 - Wearable Biosensor and Collective Sensing-Based Approach for Detecting Older Adults' Environmental Barriers
AU - Lee, Gaang
AU - Choi, Byungjoo
AU - Jebelli, Houtan
AU - Ahn, Changbum Ryan
AU - Lee, Sang Hyun
N1 - Funding Information:
This study was supported by the Exercise and Sport Science Initiative (ESSI-2018-4), the Urban Collaboratory in the University of Michigan, and the National Science Foundationa-United States (No. 1800310).
Funding Information:
This study was supported by the Exercise and Sport Science Initiative (ESSI-2018-4), the Urban Collaboratory in the University of Michigan, and the National Science Foundation—United States (No. 1800310). Also, the authors wish to acknowledge Brenda Stumbo, Ypsilanti Township Supervisor, and Denise M. McKalpain, Service Coordinator at Clark East Tower, for their help in data collection. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the aforementioned organizations.
Publisher Copyright:
© 2020 American Society of Civil Engineers.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - Aging population
KW - Collective sensing
KW - Electrodermal activity
KW - Environmental barrier
KW - Mobility
KW - People-centric sensing
KW - Wearable biosensor
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U2 - 10.1061/(ASCE)CP.1943-5487.0000879
DO - 10.1061/(ASCE)CP.1943-5487.0000879
M3 - Article
AN - SCOPUS:85077750850
SN - 0887-3801
VL - 34
JO - Journal of Computing in Civil Engineering
JF - Journal of Computing in Civil Engineering
IS - 2
M1 - 04020002
ER -