A novel algorithm for human fall detection using height, velocity and position of the subject from depth maps

Abstract

Human fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches include some sort of wearable devices, ambient based devices or non-invasive vision-based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on the height, velocity and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information. Finally position of the subject is identified for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 94.81% with sensitivity of 100% and specificity of 93.33%.