Fall Detection in RGB-D Videos for Elderly Care
Paper in 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

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Christopher Innocenti

Chalmers, Signals and Systems

Gustav Nero

Chalmers, Signals and Systems

Henrik Lindén

Chalmers, Signals and Systems

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

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

978-1-4673-8325-7 (ISBN)

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing





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