title: Fall detection for elderly using anatomical-plane-based representation.
abstract: Falls are a common cause of injuries and traumas for elderly and could be life threatening. Delivering a prompt medical support after a fall is essential to prevent lasting injuries. Therefore, effective fall detection could provide urgent support and dramatically reduce the risk of such mishaps. In this paper, we propose a hierarchical classification framework based on a novel anatomical-plane-based representation for elderly fall detection. The framework obtains human skeletal joints, using Microsoft Kinect sensors, and transforms them to a human representation. The representation is then utilized to classify the sensor input sequences and provide a semantic meaning of different human activities. Evaluation results of the proposed framework, using real case scenarios, demonstrate the efficacy of the framework in providing a feasible approach towards accurately detecting elderly falls.
authors.forename: Rami
authors.initials: R
authors.lastname: Alazrai
authors.forename: Ahmad
authors.initials: A
authors.lastname: Zmily
authors.forename: Yaser
authors.initials: Y
authors.lastname: Mowafi
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000368:Aged; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008991:Monitoring, Physiologic; D009068:Movement; D011187:Posture; D012306:Risk