Fall Detection in RGB-D Videos for Elderly Care
Paper i proceeding, 2015

This paper addresses issues in fall detection from videos. Since it has been a broadly accepted intuition that a falling person usually undergoes large physical movement and displacement in a short time interval, the study is thus focused on measuring the intensity and temporal variation of pose change and body motion. The main novelties of this paper include: (a) characterizing pose/motion dynamics based on centroid velocity, head-to-centroid distance, histogram of oriented gradients and optical flow; (b) extracting compact features based on the mean and variance of pose/motion dynamics; (c) detecting human by combining depth information and background mixture models. Experiments have been conducted on an RGB-D video dataset for fall detection. Tests and evaluations show the effectiveness of the proposed method.


Fall detection

elderly care

RGB-D video



optical flow


Yixiao Yun

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Christopher Innocenti

Chalmers, Signaler och system

Gustav Nero

Chalmers, Signaler och system

Henrik Lindén

Chalmers, Signaler och system

Irene Yu-Hua Gu

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

17th IEEE Int'l conf. on E-Health, Networking, Application & Services (HealthCom'15), 2015



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