Introducing the use of depth data for fall detection

Authors:
Rainer Planinc;Martin Kampel
Affiliations:
Computer Vision Lab, Vienna University of Technology, Vienna, Austria 1040;Computer Vision Lab, Vienna University of Technology, Vienna, Austria 1040
Venue:
Personal and Ubiquitous Computing
Year:
2013

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Abstract

Current emergency systems for elderly contain at least one sensor (button or accelerometer), which has to be worn or pressed in case of emergency. If elderly fall and loose their consciousness, they are not able to press the button anymore. Therefore, autonomous systems to detect falls without wearing any devices are needed. This paper presents three different non-invasive technologies: the use of audio, 2D sensors (cameras) and introduces a new technology for fall detection: the Kinect as 3D depth sensor. Our fall detection algorithms using the Kinect are evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living. The evaluation results are compared with State-of-the-Art approaches using 2D sensors or microphones.