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AtHoCare: An Intelligent Elder Care at Home System

  • Tao XuEmail author
  • Yun Zhou
  • Zhe Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9745)

Abstract

In recent years, the shortage of nursing home and the demand from elders have made the balance inclined. Additionally, the increased numbers of elders per year have not deemed fit to wait for growth rate of nursing home. Therefore, more and more elders have to stay at home and live alone, which easily leads them to be in danger, especially when unexpected emergency occurring like falling. To investigate this issue, we have designed AtHoCare, an intelligent elder care at home system, which employs Microsoft depth camera sensor Kinect to detect fall and an intelligent sever to send alarms to nurses’ smart phones. In this way, medical staffs could easily monitor several elders at the same time, which greatly increases work efficiency. It is worth stressing that AtHoCare also proposes an algorithm of fall detection based on skeleton data of elders only. It protects elders’ privacy much more than other vision based algorithm of fall detection. Results from our preliminary lab-environment test showed that AtHoCare has a well-done performance on detection.

Keywords

Healthcare Fall detection Intelligent system 

References

  1. 1.
    Abbate, S., Avvenuti, M., Corsini, P., Light, J., Vecchio, A.: Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: a survey. In: Tan, Y.K. (ed.) Wireless Sensor Networks: Application-Centric Design. InTech, Rijeka (2010)Google Scholar
  2. 2.
    Mubashir, M., Shao, L., Seed, L.: A survey on fall detection: principles and approaches. Neurocomput. 100, 144–152 (2013)CrossRefGoogle Scholar
  3. 3.
    Bevilacqua, V., Nuzzolese, N., Barone, D., Pantaleo, M., Suma, M., D’Ambruoso, D., Volpe, A., Loconsole, C., Stroppa, F.: Fall detection in indoor environment with kinect sensor. In: Proceedings of 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 319–324 (2014)Google Scholar
  4. 4.
    Stone, E.E., Skubic, M.: Fall detection in homes of older adults using the Microsoft Kinect. IEEE J. Biomed. Health Inform. 19, 290–301 (2015)CrossRefGoogle Scholar
  5. 5.
    Miaou, S.-G., Sung, P.-H., Huang, C.-Y.: A Customized human fall detection system using omni-camera images and personal information. In: 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, D2H2, pp. 39–42 (2006)Google Scholar
  6. 6.
    Wu, G.: Distinguishing fall activities from normal activities by velocity characteristics. J. Biomech. 33, 1497–1500 (2000)CrossRefGoogle Scholar
  7. 7.
    Williams, A., Ganesan, D., Hanson, A.: Aging in place: fall detection and localization in a distributed smart camera network. In: Proceedings of the 15th International Conference on Multimedia (2007)Google Scholar
  8. 8.
    Igual, R., Medrano, C., Plaza, I.: Challenges, issues and trends in fall detection systems. Biomed. Eng. OnLine. 12, 1–24 (2013)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    de Leva, P.: Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters. J. Biomech. 29, 1223–1230 (1996)CrossRefGoogle Scholar
  11. 11.
    Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  12. 12.
    Li, N., Hou, Y., Huang, Z.: Implementation of a real-time fall detection algorithm based on body’s acceleration. J. Chin. Comput. Syst. 33(11), 2410–2413 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Software and MicroelectronicsNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.State Key Laboratory for Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  3. 3.School of EducationShaanxi Normal UniversityXi’anPeople’s Republic of China

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