Fall prevention using head velocity extracted from visual based VDO sequences

More than ten millions elderly people fall each year. Falls are the important cause of injury related to death and set of symptoms. Most of "fall detection" systems are focused on critical and post-fall phase which mean that the faller may already be injured. In this study, we propose an early fall detection in critical fall phase using velocity characteristics, collected by Kinect sensor with 30 frames per second. A series of normal and falling activities were performed by 5 volunteers in first experiment and 11 volunteers in second experiment. The fall velocity based point was calculated by the first experiment as 50 postures, 2210 frames recorded. The result from the first experiment, velocity ratio 90.35 pixels per millisecond was calculated by adding μ with σ, was defined as velocity fall detection based point for the second experiment. In the second experiment, the fall activities were detected at 85.07 percent from 134 fall activities. The mean time of fall activities that was detected before dash to the ground is 391.15 milliseconds. The detected mean time may be useful to developing a preventive fall system to protect the faller before injured i.e. wearable airbag system. For high accuracy prediction, automatically adjusted vertical fall based point by train dataset of individual person will be investigated in future work.