Citation

Kangas M, Vikman I, Wiklander J, Lindgren P, Nyberg L, Jämsä T. Gait Posture 2009; 29(4): 571-574.

Affiliation

Department of Medical Technology, Institute of Biomedicine, University of Oulu, Oulu, Finland.

Copyright

(Copyright © 2009, Elsevier Publishing)

DOI

10.1016/j.gaitpost.2008.12.008

PMID

19153043

Abstract

About one third of home-dwelling people over 65 years of age fall each year. Falling, and the fear of falling, is one of the major health risks that affects the quality of life among older people, threatening their independent living. In our pilot study, we found that fall detection with a waist-worn triaxial accelerometer is reliable with quite simple detection algorithms. The aim of this study was to validate the data collection of a new fall detector prototype and to define the sensitivity and specificity of different fall detection algorithms with simulated falls from 20 middle-aged (40-65 years old) test subjects. Activities of daily living (ADL) performed by the middle-aged subjects, and also by 21 older people (aged 58-98 years) from a residential care unit, were used as a reference. The results showed that the hardware platform and algorithms used can discriminate various types of falls from ADL with a sensitivity of 97.5% and a specificity of 100%. This suggests that the present concept provides an effective method for automatic fall detection.

Language: en