A NEURAL NETWORK-BASED SENSOR FOR ELDER FALLING DETECTION

Jiann-I Pan, Cheng-Jie Yung, Chung Chao Liang

2006

Abstract

Falling down is going to be a crucial problem to an elder today. In many countries, unintentional injury was being one of the leading causes of death in persons over age 65 years. As the society now, there are more and more solitary elders of life alone and because of the isolation, it is necessary to design an intelligent and sensitive falling detector for the elderly people. In this paper, we present an intelligent and portable fall detection device based on artificial neural network technology. This fall detector consists of two main components: accelerometer and microprocessor. The tri-axis accelerometer is used to continuously measure the variation of elder’s 3 ways acceleration. The microprocessor reads the signals from the accelerometer and performs the fall activity recognition through a back-propagation neural network model. This device is integrated in a small box which can be holding on the belt for elder.

References

  1. Blake, A.J., 1992. Fall in the elderly. Br J Hosp Med, 47, 268-72.
  2. Degen, T. and Jaeckel, H., 2003. SPEEDY: a fall detector in a wrist watch. In Proceedings of 7th IEEE International Symposium on Wearable Computers. 21-23 Oct.. pp.184 - 187
  3. Doughty, K., 2000. Fall prevention and management strategies based on intelligent detection, monitoring and assessment. Presented at New Technologies in Medicine for the Elderly, Charing Cross Hospital, 30th, Nov.
  4. Haga, H., Shibata, H., Mitsuzaki, T., and Hatano, S., 1986. Falls in the institutionalized elderly in Japan. Arch Gerentol. Geriatr. Vol.5, pp.1-9
  5. Mathie, M.J., Basilakis, J., and Celler, B.G., 2001. A system for monitoring posture and physical actitity using accelerometers. In Proceedings of the 23rd Annual EMBS International Conference, 25-28 Oct. Turkey. Pp.3654-3657
  6. Noury, N., 2002. A smart sensor for the remote follow up of activity and fall detection of the elderly. 2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine & Biology. May Madison, Wisconsin. USA. pp.314-317
  7. Tinetti, M.E., 1994. Prevention of falls and fall injuries in elderly persons: a research agenda. Preventive Medicine. Vol 23. pp.756-762
  8. Williams, G., 1998. Doughty, K., Cameron,K. and Bradley, D.A.. A smart fall and activity monitor for telecare applications. In International Conference of IEEE-EMBS, HongKong, pp.1151-1154
  9. Wu, G., 2000. Distinguishing fall activities from normal activities by velocity characteristics. Journal of Biomechanics, Vol. 33. pp.1497-1500
  10. Yamaguchi, A., 1998. Monitoring behavior in home using positioning sensors. In International Conference of IEEE-EMBS, HongKong, pp.1977-1979
Download


Paper Citation


in Harvard Style

Pan J., Yung C. and Chao Liang C. (2006). A NEURAL NETWORK-BASED SENSOR FOR ELDER FALLING DETECTION . In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-59-7, pages 203-206. DOI: 10.5220/0001210002030206


in Bibtex Style

@conference{icinco06,
author={Jiann-I Pan and Cheng-Jie Yung and Chung Chao Liang},
title={A NEURAL NETWORK-BASED SENSOR FOR ELDER FALLING DETECTION},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2006},
pages={203-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001210002030206},
isbn={978-972-8865-59-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A NEURAL NETWORK-BASED SENSOR FOR ELDER FALLING DETECTION
SN - 978-972-8865-59-7
AU - Pan J.
AU - Yung C.
AU - Chao Liang C.
PY - 2006
SP - 203
EP - 206
DO - 10.5220/0001210002030206