Research of Humen Fall Detection Algorithm Based on Tri-Axis Accelerometer


Article Preview

The real-time monitoring of human movement can provide valuable information regarding an individual’s degree of functional ability and general level of activity. This paper presents a fall-detection technology based on tri-axial accelerometer sensor was introduced. The acceleration data of the activities with the characteristic quantity SVM and SMA was analyzed. A method based on SVM and SMA, that takes the human body activity (erectness/lies down) as the auxiliary criterion to distinguish fall and ADL is proposed, and concrete threshold value and related parameters are summarized in this article. The experiment results proved that this scheme can obtain highly rate of accuracy and this algorithm has very good timeliness.



Edited by:

David Wang




Y. Gao et al., "Research of Humen Fall Detection Algorithm Based on Tri-Axis Accelerometer", Key Engineering Materials, Vol. 500, pp. 623-628, 2012

Online since:

January 2012




[1] Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med 1988; 319(26): 1701-7.


[2] M. J. Mathie, A. C. F. Coster, B.G. Celler, and N. H. Lovell, Classification of basic daily movements using a triaxial accelerometer, Med. Biol. Eng. Comput., vol. 42, pp.670-687, (2004).


[3] D. Karantonis, M. Narayanan, N. Lovell and B. Celler, Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring, IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 1, pp.156-167, January (2006).


[4] Mathie M. J, Coster A. C. F, Celler B. G. and etal. Classification of Basic Daily Movements Using a Triaxial Accelerometer . Med. Biol. Eng. Comput. 2004, 42: 670-687.


[5] Suhuai Luo, Qingmao Hu. A Dynamic Motion Pattern Analysis Approach to Fall Detection [C]. IEEE International Workshop on Biomedical Circuits & Systems. 2004, 32: 53-56.


[6] Doughty K, Cameron K. Primary and secondary sensing techniques for fall detection in the home[R]. Proceedings of Hospital without Walls, City University, London, 1999: 104-116.