Towards a Real-Time Fall Detection System using Kinect Sensor

Towards a Real-Time Fall Detection System using Kinect Sensor

Nadia Baha (University of Science and Technology Houari Boumediene, Bab Ezzouar, Algeria), Eden Beloudah (University of Science and Technology Houari Boumediene, Bab Ezzouar, Algeria) and Mehdi Ousmer (University of Science and Technology Houari Boumediene, Bab Ezzouar, Algeria)
Copyright: © 2016 |Pages: 18
DOI: 10.4018/IJCVIP.2016010104
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Falls are the major health problem among older people who live alone in their home. In the past few years, several studies have been proposed to solve the dilemma especially those which exploit video surveillance. In this paper, in order to allow older adult to safely continue living in home environments, the authors propose a method which combines two different configurations of the Microsoft Kinect: The first one is based on the person's depth information and his velocity (Ceiling mounted Kinect). The second one is based on the variation of bounding box parameters and its velocity (Frontal Kinect). Experimental results on real datasets are conducted and a comparative evaluation of the obtained results relative to the state-of-art methods is presented. The results show that the authors' method is able to accurately detect several types of falls in real-time as well as achieving a significant reduction in false alarms and improves detection rates.
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The demand for monitoring systems, especially for fall detection, has increased within the healthcare industry with the rapid growth of the population of the elderly in the world. It has become very important to develop intelligent monitoring systems, especially vision-based systems, which can automatically monitor and detect falls. It has been proved that the medical consequences of a fall are highly contingent upon the response and rescue time (Mubashir et al 2012).

The goal aimed by researchers is to realize a monitoring system that is effectively able to distinguish between an activity of daily living (ADL) and a fall even with complex indoor scenarios that include occlusions and obstacles. Several methods have been proposed and the challenge is to detects fall in real-time. Many works have been devoted for elderly persons fall detection. (Igual et al.2013), Webster and Celik (2014), Mubashir et al. (2012) and Noury et al. (2008) reviewed the principal methods used in existing fall detection approaches. We can classify the different approaches according to the used equipment into two categories: Wearable device based and camera (vision) based. Detailed surveys can be found in Igual et al. (2013), Webster and Celik, (2014) and Mubashir et al. (2012). In the following, we summarize the different methods.

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