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Walkability Evaluation for Elderly People using Wearable Sensing

웨어러블 센싱 기반 고령자를 위한 보행 편의성 평가

  • 양강혁 (전남대학교 건축학부) ;
  • 황성주 (이화여자대학교 건축도시시스템공학전공) ;
  • 김현수 (경남과학기술대학교 건축공학과)
  • Received : 2019.04.08
  • Accepted : 2019.07.05
  • Published : 2019.07.30

Abstract

The active living of the elderly leads to improve their lives and enhance social networks. In the view of the active living, the walkability is an essential factor for the elderly's daily life. To support the active living, making age-friendly environment is important. Considering that the elderly mainly carry out activities through walking, making the age-friendly walking environment is a preliminary action. The existing studies applied various methods such as surveys by experts. In spite of the benefits in theirs, there is still a limitation that current walkability measurement methods did not incorporate the actual elderly's walking activity. Thus, the purposes of this study is to measure the elderly's walking quantitatively using a wearable sensor, and to investigate the feasibility of comparing several walking environments based on the data collected from the actual elderly's walking. To do this, experiment was conducted in four types environments with 22 senior subjects. The walkability was measured by walking stability represented quantitatively as Maximum Lyapunov Exponent (MaxLE). Through the experiment results, it was confirmed that the stability of the elderly walking was different according to the walking environment, which also meant that bodily responses (walking stability) is highly related to walkability. The results will provide an opportunity for the continuous diagnosis of walking environments, thereby enhancing the active living of the elderly.

Keywords

Acknowledgement

Supported by : 경남과학기술대학교

References

  1. Cerin, E., Saelens, B. E., Sallis, J. F., & Frank, L. D. (2006). Neighborhood Environment Walkability Scale: validity and development of a short form. Medicine & Science in Sports & Exercise, 38(9), 1682-1691. https://doi.org/10.1249/01.mss.0000227639.83607.4d
  2. Clifton, K. J., Smith, A. D. L., & Rodriguez, D. (2007). The development and testing of an audit for the pedestrian environment. Landscape and Urban Planning, 80(1-2), 95-110. https://doi.org/10.1016/j.landurbplan.2006.06.008
  3. Hoehner, C. M., Ivy, A., Ramirez, L. K. B., Handy, S., & Brownson, R. C. (2007). Active neighborhood checklist: a user-friendly and reliable tool for assessing activity friendliness. American Journal of Health Promotion, 21(6), 534-537. https://doi.org/10.4278/0890-1171-21.6.534
  4. Joo, S., & Oh, C. (2013). A novel method to monitor bicycling environments. Transportation research part A: policy and practice, 54, 1-13. https://doi.org/10.1016/j.tra.2013.07.001
  5. Kelly, J. R. (1987). Freedom to Be: A New Sociology of Leisure, N.Y., Macmillan.
  6. Kelly, J. R.(1992). Activity and Aging, Newbury Park, C.A., Sage.
  7. Kim, G., & Lee, J. (2016). Pedestrian Cognition and Satisfaction on the Physical Elements in Pedestrian Space, The Journal of Urban Design Institute of Korea, 17(3), 89-93.
  8. Kim, H., Ahn, C. R., & Yang, K. (2017). A people-centric sensing approach to detecting sidewalk defects. Advanced Engineering Informatics, 30(4), 660-671. https://doi.org/10.1016/j.aei.2016.09.001
  9. Kim, J. (2009). Reliability and Validity of Gait Assessment Tools for Elderly Person. The Journal Korean Society of Physical Therapy (JKPT), 21(1), 41-48.
  10. Korea Institute for Health and Social Affairs (2017) The Socioeconomic Impact of Low Fertility and Population Aging on Family Structure from https://www.kihasa.re.kr/common/filedown.do?seq=39714
  11. Lee, S., Lee, Y. S., & Lee, C. (2014). An Analysis of Street Environment Affecting Pedestrian Walking Satisfaction for Different Age Groups, The Journal of Korea Planners Association, 49(8), 91-105. https://doi.org/10.17208/jkpa.2014.12.49.8.91
  12. Mancini, J. A., & Orthner, D. K. (1982). Leisure time, activities, preferences, and competence: Implications for the morale of older adults. Journal of Applied Gerontology, 1(1), 95-103. https://doi.org/10.1177/073346488200100113
  13. Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., & Selavo, L. (2011). Real time pothole detection using android smartphones with accelerometers. In 2011 International conference on distributed computing in sensor systems and workshops (DCOSS) (pp. 1-6). IEEE.
  14. Mehdizadeh, S. (2018). The largest Lyapunov exponent of gait in young and elderly individuals: a systematic review. Gait & posture, 60, 241-250. https://doi.org/10.1016/j.gaitpost.2017.12.016
  15. Millington, C., Thompson, C. W., Rowe, D., Aspinall, P., Fitzsimons, C., Nelson, N., ... & SPARColl-the Scottish Physical Activity Research Collaboration. (2009). Development of the Scottish walkability assessment tool (SWAT). Health & place, 15(2), 474-481. https://doi.org/10.1016/j.healthplace.2008.09.007
  16. Mourcou, Q., Fleury, A., Dupuy, P., Diot, B., Franco, C., & Vuillerme, N. (2013, July). Wegoto: A Smartphone-based approach to assess and improve accessibility for wheelchair users. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1194-1197). IEEE.
  17. Park, S., Choi,. & Seo,. H. (2008). Measuring Walkability in Urban Residential Neighborhoods: Development of Walkability Indicators, Seoul, Korea. Journal of the Architectural Institute of Korea Planning & Design. 24(1), 161-172.
  18. Pikora, T., Bull, F., Jamrozik, K., Knuiman, M., Giles-Corti, B., & Donovan, R. (2002). Systematic Pedestrian and Cycling Environmental Scan (SPACES). Survey of the Physical Environment in Local Neighborhoods: Observer's Manual, Nedlands, Western Australia: University of Western Australia.
  19. Riddick, C. C. (1986). Leisure satisfaction precursors, Journal of Leisure research, 18(4), pp. 259. https://doi.org/10.1080/00222216.1986.11969664
  20. Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1-2), 117-134. https://doi.org/10.1016/0167-2789(93)90009-P
  21. World Health Organization. (2007). Global Age-friendly Cities: A Guide
  22. Wolf, A., Swift, J. B., Swinney, H. L., & Vastano, J. A. (1985). Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 16(3), 285-317. https://doi.org/10.1016/0167-2789(85)90011-9
  23. Yang, K., Ahn, C. R., & Kim, H. (2019). Validating ambulatory gait assessment technique for hazard sensing in construction environments. Automation in Construction, 98, 302-309. https://doi.org/10.1016/j.autcon.2018.09.017
  24. Yang, K., Ahn, C. R., Vuran, M. C., & Kim, H. (2017). Collective sensing of workers' gait patterns to identify fall hazards in construction. Automation in Construction, 82, 166-178. https://doi.org/10.1016/j.autcon.2017.04.010