title: Towards automatic detection of falls using wireless sensors.
abstract: Accurate detection of falls leading to injury is essential for providing timely medical assistance. In this paper, we describe a wireless sensor network system for automatic fall detection. To detect falls, we use a combination of a body-worn triaxial accelerometer with motion detectors placed in the monitored area. While accelerometer provides information about the body motion during a fall, motion detectors monitor general presence or absence of motion. From all sensors, the data is transmitted wirelessly using the IEEE 802.15.4 protocol to a central node for processing. We use an implementation of Carrier Sense Multiple Access-Collision Avoidance scheme for channel reuse. A simple forwarding scheme is used to provide an extended coverage for a home environment. Fall detection is accomplished by a two-stage algorithm that utilizes the triaxial acceleration and the motion data sequentially. In the first stage, the algorithm detects plausible falls using a measure of normalized energy expenditure computed from the dynamic acceleration values. In the second stage, falls are confirmed based on the absence of motion. Systematic evaluation on simulated falls using 15 adult subjects shows that the proposed system provides a highly promising solution for real-time fall detection.
authors.affiliation: Research and Technology Center, Robert Bosch LLC, Pittsburgh, PA 15212, USA. soundar.srinivasan@us.bosch.com
authors.forename: Soundararajan
authors.initials: S
authors.lastname: Srinivasan
authors.forename: Jun
authors.initials: J
authors.lastname: Han
authors.forename: Dhananjay
authors.initials: D
authors.lastname: Lal
authors.forename: Aca
authors.initials: A
authors.lastname: Gacic
mesh_terms: D000054:Acceleration; D000058:Accidental Falls / Q000517:prevention & control*; D000203:Activities of Daily Living; D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009043:Motor Activity / Q000502:physiology; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D013686:Telemetry / Q000295:instrumentation*; D014159:Transducers