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Design of Fall Test System Based on Arduino 101

  • Nan WangEmail author
  • Yaxia Liu
Conference paper
Part of the Internet of Things book series (ITTCC)

Abstract

This paper designs a fall detection system based on Arduino 101. It is mainly composed of NNs (Neural Networks) and IMU (Inertial measurement unit). It is used to detect if an oldman falls down. When an old man falls, It can call the police to help the man get help in time. The core chip of Arduino 101 is the Intel Curie module. Intel Curie module microprocessor is Intel x86 Quark SE. It also carries GPRS wireless communication and GPS satellite positioning module. It analyzes and studies the characteristic parameters of the old man when he falls and does daily activities. It mainly uses the RBF (Radial Basis Function) algorithm to identify if a fall occurs. Experimental results show that: the system is able to identify most of the motion states correctly, with low reporting and false alarm rate. And it can quickly distinguish between daily activities and falls. For the old man, the detection accuracy rate can reach 95.5%. It has a high recognition rate, reliability and stability.

Keywords

Fall detection Arduino 101 GSM NNs RBF 

References

  1. 1.
    Dong, L., Zhou, J., Xu, G.: Progress in the research on the fall care of elderly residents. China Rehabil. Theor. Pract. (01), 30–32 (2012)Google Scholar
  2. 2.
    Cai, Q., Xiang, L.: Analysis of the current situation of rural aging in China and the analysis of development trends—based on the data analysis of the sixth censuses. J. Hubei Vocat. Tech. Coll. (01), 99–104 (2013)Google Scholar
  3. 3.
    Jiecheng, H.: The current situation, trend and suggestion of population aging in China. China Econ. Trade Guide (12), 59–62 (2017)Google Scholar
  4. 4.
    Yu, X.: Approaches and principles of fall detection for elderly and patient. In: 10th International Conference One-Health Networking, Applications and Services, 2008. Health Com. IEEE, pp. 42–47 (2008)Google Scholar
  5. 5.
    Vaidehi, V., Ganapathy, K., Mohan, K., et al.: Video based automatic fall detection in indoor environment. In: 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, pp. 1016–1020 (2011)Google Scholar
  6. 6.
    Li, Y., Ho K.C., Popescu, M.: A microphone array system for automatic fall detection. IEEE Trans. Biomed. Eng. 59(5), 1291–1301 (2012)CrossRefGoogle Scholar
  7. 7.
    Scott, T.E.: Bed exit detection apparatus. US Patent 6,067,019 (2000)Google Scholar
  8. 8.
    Hwang, J.Y., Kang, J.M., Jang, Y.W., et al.: Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly. In: Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th Annual International Conference of the IEEE. IEEE, vol. 1, pp. 2204–2207 (2004)Google Scholar
  9. 9.
    XiaohuaQin, C., Yuan, K., et al.: A mobile health monitor system for the elderly. Chin. J. Med. Phys. 28(1), 2407–2410 (2011)Google Scholar
  10. 10.
    Luo, Q., ZhenYan, X., Peng, Y., Peng, X.: A dynamic distance estimation method based on sliding window mode. J. Instrum. 36(03), 499–506 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information EngineeringBeijing Institute of Fashion TechnologyBeijingChina

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