Fall detection via human posture representation and support vector machine

Kaibo Fan, Ping Wang, Yan Hu and Bingjie Dou

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 5, 1550147717707418

Abstract: Accidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision–based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique is located by a minimum area-enclosing ellipse. Then, a normalized directional histogram is developed around the center of the ellipse to represent a human posture by multi-directional statistical analysis. After that, 12 static and 8 dynamic features are derived from the normalized directional histogram. These features are fed into a directed acyclic graph support vector machine to distinguish four closely related human postures (standing, crouching, lying, and sitting). A fall-like accident is detected by counting the occurrences of lying postures in a short temporal window. After conducting majority voting, a fall event is determined by immobility verification. From the experimental results, an overall accuracy of 97.1% is obtained for recognition of the four postures, and only 1.0% of postures are misclassified as lying postures. Our fall detection system achieves up to 95.2% fall detection accuracy on a public fall dataset.

Keywords: Fall detection; object extraction; posture representation; support vector machine; majority voting (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:5:p:1550147717707418

DOI: 10.1177/1550147717707418

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