Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Behavioral Contextualization for Extracting Occupant's ADL Patterns in Smart-home Environment

스마트 홈 환경에서의 재실자 일상생활 활동 패턴 추출을 위한 행동 컨텍스트화 프로세스에 관한 연구

  • Lee, Bogyeong (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Lee, Hyun-Soo (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Park, Moonseo (Department of Architecture and Architectural Engineering, Seoul National University)
  • Received : 2017.10.24
  • Accepted : 2017.11.28
  • Published : 2018.01.31
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Abstract

The rapid increase of the elderly living alone is a critical issue in worldwide as it leads to a rapid increase of a social support costs (e.g., medical expenses) for the elderly. In early stages of dementia, the activities of daily living (ADL) including self-care tasks can be affected by abnormal patterns or behaviors and used as an evidence for the early diagnosis. However, extracting activities using non-intrusive approach is still quite challenging and the existing methods are not fully visualized to understand the behavior pattern or routine. To address these issues, this research suggests a model to extract the activities from coarse-grained data (spatio-temporal data log) and visualize the behavioral context information. Our approach shows the process of extracting and visualizing the subject's spaceactivity map presenting the context of each activity (time, room, duration, sequence, frequency). This research contributes to show a possibility of detecting subject's activities and behavioral patterns using coarse-grained data (limited to spatio-temporal information) with little infringement of personal privacy.

고령자 가구의 급격한 증가는 전 세계적 추세이며 의료비 등 사회적 비용 또한 급격히 증가할 것으로 예상된다. 치매와 같은 노인성 기능 질환의 경우 고령자의 일상생활 활동 (ADL) 패턴을 상시적으로 모니터링하고 평소와 다르거나 비정상적인 패턴이 발생하는 경우 이를 치매 조기진단의 근거로 활용할 수 있다. 그러나 사생활 침해의 우려가 큰 기존의 직접적 센싱 방식과 달리 간접적 센싱 방식 (Non-intrusive approach)을 활용하여 재실자의 최소한의 정보 (Coarse-grained data)만을 수집하고, 이를 통해 활동 정보를 추출하는 연구는 거의 이루어지지 않았다. 또한 추출된 활동 및 활동패턴을 이해하기 위해 활동의 맥락적 정보를 시각화하는 방법 또한 추가적인 연구가 필요하다. 이를 위해 본 연구에서는 재실자의 정보 중 시 공간 데이터 로그만을 활용하여 재실자의 수행 활동을 추출하고 컨텍스트화 된 행동 정보를 공간-활동 지도 (Space-Activity Map)로 시각화한다. 본 연구는 재실자의 일상생활 활동 패턴을 추출하는 데 기반이 되는 연구로서, 향후 고령자를 위한 상시적인 건강 모니터링 기술의 도입에 기여할 수 있다.

Keywords

References

  1. Alirezaie, M., Renoux, J., Kockemann, U., Kristoffersson, A., Karlsson, L., Blomqvist, E., and Loutfi, A. (2017). "An ontology-based contextaware system for smart homes: E-care@ home." Sensors, IEEE, 17(7), pp. 1585-1607.
  2. Andersen, C. K., Wittrup-Jensen, K. U., Lolk, A., Andersen, K., and Kragh-Sorensen, P. (2004). "Ability to perform activities of daily living is the main factor affecting quality of life in patients with dementia." Health and quality of life outcomes, 2(1), pp. 52-58.
  3. Andrienko, N., Andrienko, G., and Gatalsky, P. (2003). "Exploratory spatio-temporal visualization: an analytical review." Journal of Visual Languages & Computing, 14(6), pp. 503-541. https://doi.org/10.1016/S1045-926X(03)00046-6
  4. Bach, B., Dragicevic, P., Archambault, D., Hurter, C., and Carpendale, S. (2014). "A review of temporal data visualizations based on space-time cube operations." Proceedings of Eurographics conference on visualization.
  5. Banovic, N., Buzali, T., Chevalier, F., Mankoff, J., and Dey, A. K. (2016). "Modeling and understanding human routine behavior." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 248-260.
  6. Blankevoort, C. G., Van Heuvelen, M. J., Boersma, F., Luning, H., De Jong, J., and Scherder, E. J. (2010). "Review of effects of physical activity on strength, balance, mobility and ADL performance in elderly subjects with dementia." Dementia and geriatric cognitive disorders, 30(5), pp. 392-402. https://doi.org/10.1159/000321357
  7. Brdiczka, O., Su, N. M., and Begole, J. B. (2010). "Temporal task footprinting: identifying routine tasks by their temporal patterns." Proceedings of the 15th international conference on Intelligent user interfaces, pp. 281-284.
  8. Chen, D., Barker, S., Subbaswamy, A., Irwin, D., and Shenoy, P. (2013). "Non-intrusive occupancy monitoring using smart meters." Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, pp. 1-8.
  9. Covinsky, K. E., Palmer, R. M., Fortinsky, R. H., Counsell, S. R., Stewart, A. L., Kresevic, D., and Landefeld, C. S. (2003). "Loss of independence in activities of daily living in older adults hospitalized with medical illnesses: increased vulnerability with age." Journal of the American Geriatrics Society, 51(4), pp. 451-458. https://doi.org/10.1046/j.1532-5415.2003.51152.x
  10. Davidoff, S., Zimmerman, J., and Dey, A. K. (2010). "How routine learners can support family coordination." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2461-2470.
  11. Ditzler, K. (1991). "Efficacy and tolerability of memantine in patients with dementia syndrome. A double-blind, placebo controlled trial." Arzneimittel-Forschung, 41(8), pp. 773-780.
  12. Galinina, O., Mikhaylov, K., Huang, K., Andreev, S., and Koucheryavy, Y. (2016). "Wirelessly powered urban crowd sensing over wearables: trading energy for data." arXiv preprint arXiv:1611.05910.
  13. Guo, D., Chen, J., MacEachren, A. M., and Liao, K. (2006). "A visualization system for space-time and multivariate patterns (vis-stamp)." Transactions on visualization and computer graphics, IEEE, 12(6), pp. 1461-1474.
  14. Hagerstraand, T. (1970). "What about people in regional science?" Papers in regional science, 24(1), pp. 7-24. https://doi.org/10.1111/j.1435-5597.1970.tb01464.x
  15. Hodgson, G. M. (1997). "The ubiquity of habits and rules." Cambridge journal of economics, 21(6), pp. 663-684. https://doi.org/10.1093/oxfordjournals.cje.a013692
  16. Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., and Jaffe, M. W. (1963). "Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function." Jama, 185(12), pp. 914-919. https://doi.org/10.1001/jama.1963.03060120024016
  17. Kim, J. Y., Liu, N., Tan, H. X., and Chu, C. H. (2017). "Unobtrusive monitoring to detect depression for elderly with chronic illnesses." Sensors, IEEE, 17(17), pp. 5694-5704.
  18. Lee, S., Lim, J., Park, J., and Kim, K. (2016). "Next place prediction based on spatiotemporal pattern mining of mobile device logs." Sensors, IEEE, 16(2), pp. 144-162.
  19. Lee, Y. J., Lee, J., and Nah, J. Y. (2015). "Older adults experience of smart-home healthcare system." The Journal of the Korea Contents Association, 15(5), pp. 414-425. https://doi.org/10.5392/JKCA.2015.15.05.414
  20. National Institute of Dementia. (2016). "Korean dementia observatory 2016."
  21. Nouri, F. M., and Lincoln, N. B. (1987). "An extended activities of daily living scale for stroke patients." Clinical rehabilitation, 1(4), pp. 301-305. https://doi.org/10.1177/026921558700100409
  22. Qi, F., and Du, F. (2013). "Tracking and visualization of space-time activities for a micro-scale flu transmission study." International journal of health geographics, 12(1), pp. 6-21.
  23. Roy, A., Bhaumik, S. D., Bhattacharya, A., Basu, K., Cook, D. J., and Das, S. K. (2003). "Location aware resource management in smart homes." Proceedings of the First IEEE International Conference, IEEE, pp. 481-488.
  24. Roy, A., Das, S. K., and Basu, K. (2007). "A predictive framework for location-aware resource management in smart homes." IEEE Transactions on mobile computing, 6(11). pp. 1270-1283.
  25. Shoval, N., and Isaacson, M. (2007). "Sequence alignment as a method for human activity analysis in space and time." Annals of the Association of American geographers, 97(2), pp. 282-297. https://doi.org/10.1111/j.1467-8306.2007.00536.x
  26. Statistics Korea. (2017). "2017 Statistics of the elderly." (Sep. 26, 2017).
  27. Taylor, R. (1950). "Purposeful and non-purposeful behavior: A rejoinder." Philosophy of Science, 17(4), pp. 327-332. https://doi.org/10.1086/287108
  28. Urwyler, P., Stucki, R., Rampa, L., Muri, R., Mosimann, U. P., and Nef, T. (2017). "Cognitive impairment categorized in community-dwelling older adults with and without dementia using inhome sensors that recognise activities of daily living." Scientific Reports, 7, 42084-9.
  29. Zhang, J., Wei, B., Hu, W., and Kanhere, S. S. (2016). "Wifi-id: Human identification using wifi signal." Distributed Computing in Sensor Systems (DCOSS), IEEE, pp. 75-82.
  30. Ziebart, B. D., Maas, A. L., Dey, A. K., and Bagnell, J. A. (2008). "Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior." Proceedings of the 10th international conference on Ubiquitous computing, pp. 322-331.
  31. Zoha, A., Cluhak, A., Imran, M. A., and Rajasegarar, S. (2012). "Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey." Sensors, IEEE, 12(12), pp. 16838-16866.