title: Falls event detection using triaxial accelerometry and barometric pressure measurement.
abstract: A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).
authors: Department of Biomedical Engineering, Politecnico di Milano, Milano, Italy.
authors: Federico
authors: F
authors: Bianchi
authors: Stephen J
authors: SJ
authors: Redmond
authors: Michael R
authors: MR
authors: Narayanan
authors: Sergio
authors: S
authors: Cerutti
authors: Branko G
authors: BG
authors: Celler
authors: Nigel H
authors: NH
authors: Lovell
mesh_terms: D000054:Acceleration; D000058:Accidental Falls / Q000517:prevention & control*; D056044:Actigraphy / Q000295:instrumentation*; D000465:Algorithms; D001274:Atmospheric Pressure; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D008365:Manometry / Q000295:instrumentation*; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity