Zhangbing Li
School of Computer Science and Engineering, Hunan University of Sci. and Tec., Xiangtan, China
Xiaoyong Zhaong
School of Computer Science and Engineering, Hunan University of Sci. and Tec., Xiangtan, China
Zilan Zhu
School of Computer Science and Engineering, Hunan University of Sci. and Tec., Xiangtan, China
ABSTRACT
With more and more attentions to the aging population problem socially, many monitoring systems have been built to diagnose the health of the old people by the internet. However, there are few systems and methods to forecast or perceive the change of their health by low cost while monitoring and tracking the activity of the aged people for nursing and no model for describing the health index of the elderly people. Making use of the statistical data of the aged people living from the monitoring system based on RFID, this study builds a model to describe the states of the elderly health by the health index. The data from the RFID mainly include the times of physical activities for living, such as dining, going to the toilet, sleeping and so on. According to the natural relations between the activity and the health of the elderly people, the functions of the health index are designed for the model. According to the reflection between the index and the health states, it analyzes the data with a BP neural network and forecasts the health index of the old person in the bead-house. The experiments show that it provides a real-time, low-cost decision support to forecast and perceive the health states of the elderly for their managers and family members.
PDF References Citation
How to cite this article
Zhangbing Li, Xiaoyong Zhaong and Zilan Zhu, 2013. A Model for the Health Index of the Elderly People Forecasting Based on Rfid. Information Technology Journal, 12: 7938-7944.
DOI: 10.3923/itj.2013.7938.7944
URL: https://scialert.net/abstract/?doi=itj.2013.7938.7944
DOI: 10.3923/itj.2013.7938.7944
URL: https://scialert.net/abstract/?doi=itj.2013.7938.7944
REFERENCES
- Ren, C.X. and C.B. Wang, 2013. The Prediction of Short-Term Traffic Flow Based on the Niche Genetic Algorithm and BP Neural Network. In: Proceedings of the 2012 International Conference on Information Technology and Software, Lu, W., G. Cai, W. Liu and W. Xing (Eds.). Vol. 211, Springer, New York, pp: 775-781.
- Anderson, D.T., M. Ros, J.M. Keller, M.P. Cuellar, M. Popescu, M. Delgado and A. Vila, 2012. Similarity measure for anomaly detection and comparing human behaviors. Int. J. Intell. Syst., 27: 733-756.
CrossRef - Wang, Y., D. Zheng, S.M. Luo, D.M. Zhan and P. Nie, 2013. The research of railway passenger flow prediction model based on bp neural network. Adv. Mater. Res., 605-607: 2366-2369.
CrossRef