Sensitivity and specificity of fall detection in people aged 40 years and over

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Sjukgymnastik (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Physiotherapy (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)

Nyckelord

Caring sciences

Physiotherapy

Vårdvetenskap

Sjukgymnastik/fysioterapi

Physiotherapy

Sjukgymnastik

Inbyggda system

Embedded Systems

Centre - eHealth Innovation Centre (EIC)

Centrumbildning - Centrum för Innovation och eHälsa (EIC)

Publikations- och innehållstyp

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