Browse > Article
http://dx.doi.org/10.7471/ikeee.2013.17.1.022

A simulation on fall detection system for the elders  

Kim, Dong-Wan (Dept. of Information & communication Engineering, Myongji University)
Ryu, Jong-Hyun (Wonkwang University)
Beack, Seung-Hwa (Dept. of Information & communication Engineering, Myongji University)
Publication Information
Journal of IKEEE / v.17, no.1, 2013 , pp. 22-28 More about this Journal
Abstract
According to a survey, more than 50% of the elders fall which is the most frequent daily safety accident of the elders takes place at home. Furthermore, the elders fall is anticipated to increase as more elderly people are expected to live alone since, 67.1% of the elders of 65 or more do not hope to live with their children. This research aims to verify the fall by measuring and analyzing the floor vibration, and the hardware system was also designed was Piezo Film Sensor, Op-Amp, and DAQ. The system is consists of signal processing part for measuring floor vibration and alarm part for identifying the consciousness of the user when the fall occurs. The fall detection by vibration signals verified by k-Nearest Neighbor verification, and the results showed the error rate of 3.8%.
Keywords
Fall detection; Floor vibrations; Elder; k-NN; Piezo sensor;
Citations & Related Records
연도 인용수 순위
  • Reference
1 "Investigation of accidents of life of the elderly in 2007", Korea Consumer Agency, 2007
2 Jong-Min Kim, Myung-Sun Lee, "Risk Factors for Falls in the Elderly Population in Korea:An Analysis of the Third Korea National Health and Nutrition Examination Survey data", Joural of Korea Society for Health Education and Promotion Vol.24, No 4, pp. 23-39, 2007
3 Chia-Wen Lin, Zhi-Hong Ling, Yuan-Cheng Chang, Chung J. Kuo, "Compressed-domain fall incident detection for intelligent home surveillance", IEEE International Symposium on Circuits and Systems, Vol. 4, pp.3781-3784, May 2005
4 C. F. Juang and C. M. Chang, "Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application," IEEE Trans. Systems, Man and Cybernetics, Part A: Systems and Humans, Vol. 37, No. 6, pp. 984-994, Nov. 2007.   DOI   ScienceOn
5 T. Zhang, J. Wang, L. Xu and P. Liu, Detection by Wearable "Fall Sensor and One-Class SVM Algorithm," in Lecture Notes in Control and Information Sciences, pp. 858-863, 2006.
6 T. Zhang, J. Wang, P. Liu and J. Hou, Journal "Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm," IJCSNS International of Computer Science and Network Security, Vol. 6, No. 10, pp. 277-284, Oct. 2006.
7 J. Y. Hwang, J. M. Kang, Y. W. Jang, and H. C. Kim, "Development of Novel Algorithm and Real-time Monitoring Ambulatory System Using Bluetooth Module for Fall Detection in the Elderly," in Proc. 26th Annu. Int. Conf. IEEE EMBS, pp. 2204-2207, Sep. 2004.
8 U. Lindemann, A. Hock, M. Stuber, W. Keck and C. Becker, "Evaluation of a fall detector based on accelerometers: A pilot study," Medical and Biological Engineering and Computing, Vol. 43, No. 5, pp.548-551, Jun. 2005.   DOI   ScienceOn
9 T. Degen, H. Jaeckel, M. Rufer and S. Wyss, "SPEEDY:a fall detector in a wrist watch," in Proc. 7th IEEE Int. Symp. Wearable Computers, pp. 184-187, Oct. 2005.