Human Fall Detection from Depth Images using Position and Velocity of Subject

Fall detection and notification systems play an important role in our daily life, since human fall is a major health concern for many communities in today's aging population. There are different approaches used in developing human fall detection systems for elderly and people with special needs such as disable. The three basic approaches include some sort of wearable, non wearable ambient sensor and vision based systems. This paper proposes a human fall detection system based on the velocity and position of the subject, extracted from Microsoft Kinect Sensor. Initially the subject and floor plane are extracted and tracked frame by frame. The tracked joints of the subject are then used to measure the velocity with respect to the previous location. Fall detection is confirmed using the position of the subject to see if all the joints are on the floor after an abnormal velocity. From the experimental results obtained, our system was able to achieve an average accuracy of 93.94% with a sensitivity of 100% and specificity of 91.3%. (C) 2017 The Authors. Published by Elsevier B.V.