title: Wavelet-Based Sit-To-Stand Detection and Assessment of Fall Risk in Older People Using a Wearable Pendant Device.
abstract: Wearable devices provide new ways to identify people who are at risk of falls and track long-term changes of mobility in daily life of older people. The aim of this study was to develop a wavelet-based algorithm to detect and assess quality of sit-to-stand movements with a wearable pendant device.
  
  
  
  The algorithm used wavelet transformations of the accelerometer and barometric air pressure sensor data. Detection accuracy was tested in 25 older people performing 30 min of typical daily activities. The ability to differentiate between people who are at risk of falls from people who are not at risk was investigated by assessing group differences of sensor-based sit-to-stand measurements in 34 fallers and 60 nonfallers (based on 12-month fall history) performing sit-to-stand movements as part of a laboratory study.
  
  
  
  Sit-to-stand movements were detected with 93.1% sensitivity and a false positive rate of 2.9% during activities of daily living. In the laboratory study, fallers had significantly lower maximum acceleration, velocity, and power during the sit-to-stand movement compared to nonfallers.
  
  
  
  The new wavelet-based algorithm accurately detected sit-to-stand movements in older people and differed significantly between older fallers and nonfallers.
  
  
  
  Accurate detection and quantification of sit-to-stand movements may provide objective assessment and monitoring of fall risk during daily life in older people.
authors.affiliation: Group of Assistive Healthcare Information Technology, Austrian Institute of Technology, Vienna, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Ejupi
authors.affiliation: Neuroscience Research AustraliaUniversity of New South Wales.
authors.forename: Matthew
authors.initials: M
authors.lastname: Brodie
authors.affiliation: Neuroscience Research AustraliaUniversity of New South Wales.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Lord
authors.affiliation: Philips Research Europe.
authors.forename: Janneke
authors.initials: J
authors.lastname: Annegarn
authors.affiliation: Graduate School of Biomedical EngineeringUniversity of New South Wales.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
authors.affiliation: Neuroscience Research AustraliaUniversity of New South Wales.
authors.forename: Kim
authors.initials: K
authors.lastname: Delbaere
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; 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; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D004856:Postural Balance / Q000502:physiology*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis