Article

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.

Langue : ANGLAIS