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Lifelog Generation Based on Informationally Structured Space

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Intelligent Robotics and Applications (ICIRA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11742))

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Abstract

As the problem of the increased number of elderly people and the decreased number of children in Japan has arisen recently, the development of robot partner and intelligent room for monitoring and measurement system has become a main topic. On the other hand, the stability of both social rhythm and biological rhythm is very important for extension of healthy life expectancy. It is difficult for elderly people to understand the current stability of social rhythm and biological rhythm in daily life. First of all, we have to generate detail lifelog. We define lifelog composed of daily human behavior. In this paper, first, we show different types of classification methods for human activity. We explain our computation model for elderly care. Next, we introduce several human behavior measurement methods for lifelog. And we show lifelog generation in indoor, outdoor and by using robot partner. Finally, we discuss the effectiveness of the proposed methods and future works.

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References

  1. Chernbumroong, S., et al.: Elderly activities recognition and classification for applications in assisted living. Expert Syst. Appl. 40(5), 1662–1674 (2013)

    Article  Google Scholar 

  2. Rueangsirarak, W., et al.: Fall-risk screening system framework for physiotherapy care of elderly. Expert Syst. Appl. 39(10), 8859–8864 (2012)

    Article  Google Scholar 

  3. Acampora, G., et al.: A survey on ambient intelligence in healthcare. Proc. IEEE 101(12), 2470–2494 (2013)

    Article  Google Scholar 

  4. Continua Health Alliance: Continua health alliance (2015). http://www.continua.jp/

  5. Pantelopoulos, A., et al.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 10(1), 1–12 (2010)

    Google Scholar 

  6. Tang, D., et al.: Informationally structured space for community-centric systems. In: Proceedings of the 2nd International Conference on Universal Village, UV 2014, Boston, USA (2014)

    Google Scholar 

  7. Tang, D., et al.: A novel multimodal communication framework using robot partner for aging population. Expert Syst. Appl. 42, 4540–4555 (2015)

    Article  Google Scholar 

  8. McTear, M., et al.: Introducing the Conversational Interface. The Conversational Interface, pp. 1–7. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32967-3

    Book  Google Scholar 

  9. Georgieff, P.: Ambient assisted living. Marktpotenziale IT-unterstützter Pflege für ein selbstbestimmtes Altern, FAZIT Forschungsbericht 17, 9–10 (2008)

    Google Scholar 

  10. Gubbi, J., et al.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  11. Kubota, N., et al.: Topological environment reconstruction in informationally structured space for pocket robot partners. In: Proceedings of the 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 165–170 (2009)

    Google Scholar 

  12. Kubota, N., et al.: Localization of human based on fuzzy spiking neural network in informationally structured space. In: Proceedings of the IEEE World Congress on Computational Intelligence, Spain, pp. 2209–2214 (2010)

    Google Scholar 

  13. Szalai, A., et al.: The Use of Time: Daily Activities of Urban and Suburban Populations in Twelve Countries. Mouton, The Hague (1972)

    Google Scholar 

  14. Gabriele, B., et al.: Outcome measures in older persons with acquired joint contractures: a systematic review and content analysis using the ICF (International Classification of Functioning, Disability and Health) as a reference. BMC Geriatr. 16(1), 40 (2016)

    Article  Google Scholar 

  15. Gabriele, B., et al.: Development of an International Classification of Functioning, Disability and Health (ICF)-based standard set to describe the impact of joint contractures on participation of older individuals in geriatric care settings. Arch. Gerontol. Geriatr. 61(1), 61–66 (2015)

    Article  Google Scholar 

  16. Kobayashi, T., et al.: National survey on schedule of a daily life in Japan. NHK Mon. Rep. Broadcast Res. 2–21 (2011)

    Google Scholar 

  17. Kobayashi, T., et al.: Sleeping time keeps decreasing, male housework time is increasing. From the 2010 NHK Japanese Time Use Survey (2010). https://www.nhk.or.jp/bunken/english/reports/pdf/report_110401.pdf

  18. Alloy, L.B., et al.: Low social rhythm regularity predicts first onset of bipolar spectrum disorders among at-risk individuals with reward hypersensitivity. J. Abnorm. Psychol. 24(4), 944–952 (2015)

    Article  Google Scholar 

  19. Margraf, J., et al.: Social rhythm and mental health: a cross-cultural comparison. PloS one 11(3), e0150312 (2016)

    Article  Google Scholar 

  20. Candas, J.L.C., et al.: An automatic data mining method to detect abnormal human behavior using physical activity measurements. Pervasive Mob. Comput. 15, 228–241 (2014)

    Article  Google Scholar 

  21. Fang, H., He, L., Si, H., et al.: Human activity recognition based on feature selection in smart home using back-propagation algorithm. ISA Trans. 53(5), 1629–1638 (2014)

    Article  Google Scholar 

  22. Afsar, P., Cortez, P., Santos, H.: Automatic visual detection of human behavior: a review from 2000 to 2014. Expert Syst. Appl. 42(20), 6935–6956 (2015)

    Article  Google Scholar 

  23. Maass, W., Bishop, C.M.: Pulsed Neural Networks. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  24. Dalai, T., et al.: Social rhythm management support system based on informationally structured space. In: International Conference on Human System Interactions. IEEE (2016)

    Google Scholar 

  25. Dalai, T., János, B., Naoyuki, K.: Supervised learning based multi-modal perception for robot partners using smart phones. Acta Polytechnica Hungarica Journal of Applied Sciences 11(8), 139–159 (2014)

    Google Scholar 

  26. Shuai, S., et al.: A fuzzy spiking neural network for behavior estimation by multiple environmental sensors. In: IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Japan, TuAM-R05 (2018)

    Google Scholar 

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Correspondence to Dalai Tang .

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Tang, D., Kubota, N. (2019). Lifelog Generation Based on Informationally Structured Space. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11742. Springer, Cham. https://doi.org/10.1007/978-3-030-27535-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-27535-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27534-1

  • Online ISBN: 978-3-030-27535-8

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