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Wearable Sensor Data-Driven Walkability Assessment for Elderly People

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  • Hyunsoo Kim

    (Department of Architectural Engineering, Dankook University, Yongin-si 16890, Korea)

Abstract

Active living improves the lives and social networks of the elderly. In terms of active living, walkability is an essential element in the daily life of the elderly. To support active living, it is important to create an age-friendly environment. Considering that the elderly carry out a large part of their activities by walking, a good walkable environment is one of the most important elements of an age-friendly environment. Existing studies have involved surveys of experts, audit tools, and questionnaires. However, despite their merits, current methods of measuring walkability remain limited as they do not include the actual walking activity of the elderly. Therefore, the purpose of this study is to investigate the possibility of using a wearable sensor to measure the walking of the elderly quantitatively, and to compare different walking environments based on data collected from their actual walking. To accomplish this, experiments were conducted in four types of environments with 30 elderly subjects. During the experiments, the subjects were asked to attach a smartphone that includes an inertial measurement unit (IMU). The IMU sensor collected the body movement using tri-axial accelerations. The collected data were used to calculate walkability by investigating how constant a subject’s walking pattern is. The consistency of pattern can be regarded as gait stability that can be quantitatively measured via the maximum Lyapunov exponent (MaxLE—a metric used for measuring the stability of human body during locomotion. As a result of the experiment, it was found that the stability of walking of elderly people differs according to the walking environment, which means that by investigating the stability the current conditions of a specific walking environment can be inferred. This result helps improve the active life of the elderly by providing opportunities for continuous diagnosis of the walking environment.

Suggested Citation

  • Hyunsoo Kim, 2020. "Wearable Sensor Data-Driven Walkability Assessment for Elderly People," Sustainability, MDPI, vol. 12(10), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4041-:d:358307
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    References listed on IDEAS

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    1. Nolan, Robert & Park, Hyunsoo & Hagen, Leigh Ann Von & Chatman, Daniel, 2014. "A mode choice analysis of school trips in New Jersey," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 111-133.
    2. Park, Hyunsoo & Noland, Robert B. & Lachapelle, Ugo, 2013. "Active school trips: associations with caregiver walking frequency," Transport Policy, Elsevier, vol. 29(C), pages 23-28.
    3. Joo, Shinhye & Oh, Cheol, 2013. "A novel method to monitor bicycling environments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 1-13.
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    Cited by:

    1. Anna Visvizi & Shahira Assem Abdel-Razek & Roman Wosiek & Radosław Malik, 2021. "Conceptualizing Walking and Walkability in the Smart City through a Model Composite w 2 Smart City Utility Index," Energies, MDPI, vol. 14(23), pages 1-20, December.
    2. Stefania Bandini & Francesca Gasparini, 2021. "Social and Active Inclusion of the Elderly in the City Through Affective Walkability," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 557-573, November.
    3. Jihwan Yoon & Jaeyoul Chun & Hyunsoo Kim, 2020. "Investigating the Relation between Walkability and the Changes in Pedestrian Policy through Wearable Sensing," Sustainability, MDPI, vol. 12(24), pages 1-19, December.

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