title: Human fall detection on embedded platform using depth maps and wireless accelerometer.
abstract: Since falls are a major public health problem in an aging society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.
authors.affiliation: AGH University of Science and Technology, 30 Mickiewicza Av., 30-059 Kraków, Poland. Electronic address: bkw@agh.edu.pl.
authors.forename: Bogdan
authors.initials: B
authors.lastname: Kwolek
authors.affiliation: University of Rzeszow, 16c Rejtana Av., 35-959 Rzeszów, Poland.
authors.forename: Michal
authors.initials: M
authors.lastname: Kepski
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods; D000058:Accidental Falls / Q000517:prevention & control*; D000203:Activities of Daily Living; D000328:Adult; D005160:Facility Design and Construction; D006801:Humans; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D009068:Movement; D015203:Reproducibility of Results; D060388:Support Vector Machine
keywords: Assistive technology; Depth image analysis; Fall detection; Sensor technology for smart homes