title: Multimodal sensor-based fall detection within the domestic environment of elderly people.
abstract: BACKGROUND
  Falls represent a major threat to the health of the elderly and are a growing burden on the healthcare systems. With the growth of the elderly population within most societies efficient fall detection becomes increasingly important; however, existing fall detection systems still fail to produce reliable results.
  
  
  OBJECTIVE
  A study was carried out on sensor-based fall detection, analysis of falls with the help of fall protocols and the analysis of user acceptance of fall detection sensor technology through questionnaires.
  
  
  METHODS
  A total of 28 senior citizens were recruited from a German community-dwelling population. The primary goal was a sensor-based detection of falls with accelerometers, video cameras and microphones. Details of the falls were analyzed with the help of medical geriatric assessments and standardized fall protocols. The study duration was 8 weeks and required a maximum of nine visits per subject.
  
  
  RESULTS
  The study participants were 28 subjects with a mean age of 74.3 and a standard deviation (SD) of ± 6.3 years of which 12 were male and 16 female. A total of 1225.7 measurement days were recorded from all participants and the algorithms detected 2.66 falls per day. During the study period 15 falls occurred and 12 of these falls were correctly recognized by the fall detection system.
  
  
  CONCLUSIONS
  Current fall detection technologies work well under laboratory conditions but it is still problematic to produce reliable results when these technologies are applied to real life conditions. Acceptance towards the sensors decreased after study participation although the system was generally perceived as useful or very useful.
authors.affiliation: Geriatrics Research Group, Department of Geriatric Medicine, Charité Universitätsmedizin Berlin, Reinickendorfer Str. 61, 13447, Berlin, Germany, florian.feldwieser@charite.de.
authors.forename: F
authors.initials: F
authors.lastname: Feldwieser
authors.forename: M
authors.initials: M
authors.lastname: Gietzelt
authors.forename: M
authors.initials: M
authors.lastname: Goevercin
authors.forename: M
authors.initials: M
authors.lastname: Marschollek
authors.forename: M
authors.initials: M
authors.lastname: Meis
authors.forename: S
authors.initials: S
authors.lastname: Winkelbach
authors.forename: K H
authors.initials: KH
authors.lastname: Wolf
authors.forename: J
authors.initials: J
authors.lastname: Spehr
authors.forename: E
authors.initials: E
authors.lastname: Steinhagen-Thiessen
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods; D000058:Accidental Falls / Q000517:prevention & control* / Q000706:statistics & numerical data*; D000162:Acoustics / Q000295:instrumentation; D056044:Actigraphy / Q000295:instrumentation* / Q000379:methods; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006707:Homes for the Aged; D006801:Humans; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018511:Systems Integration
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