Abstract
In this paper we demonstrate how to accomplish reliable fall detection on a low-cost embedded platform. The detection is achieved by a fuzzy inference system using Kinect and a wearable motion-sensing device that consists of accelerometer and gyroscope. The foreground objects are detected using depth images obtained by Kinect, which is able to extract such images in a room that is dark to our eyes. The system has been implemented on the PandaBoard ES and runs in real-time. It permits unobtrusive fall detection as well as preserves privacy of the user. The experimental results indicate high effectiveness of fall detection.
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Kepski, M., Kwolek, B. (2012). Fall Detection on Embedded Platform Using Kinect and Wireless Accelerometer. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds) Computers Helping People with Special Needs. ICCHP 2012. Lecture Notes in Computer Science, vol 7383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31534-3_60
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DOI: https://doi.org/10.1007/978-3-642-31534-3_60
Publisher Name: Springer, Berlin, Heidelberg
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