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Introducing the use of depth data for fall detection

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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.

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Notes

  1. The sensor data can either be accessed with the official Microsoft SDK or with the open source SDK OpenNI.

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Authors and Affiliations

  1. Computer Vision Lab, Vienna University of Technology, Favoritenstrasse 9-11/183-2, 1040, Vienna, Austria

    Rainer Planinc & Martin Kampel

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  1. Rainer Planinc
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  2. Martin Kampel
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Correspondence to Rainer Planinc.

Additional information

This work was supported by the European Union within the AAL-JP project "fearless" under grant AAL 2010-3-020.

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Planinc, R., Kampel, M. Introducing the use of depth data for fall detection. Pers Ubiquit Comput 17, 1063–1072 (2013). https://doi.org/10.1007/s00779-012-0552-z

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