Pre-Impact Fall Detection Approach Using Dynamic Threshold Based and Center of Gravity in Multiple Kinect Viewpoints

One of the primary reasons of injury related to death not only in elderlies but also in young people is slip trip and fall accidents. It will be very useful if fall accidents can be detected in pre-fall and critical fall phase long enough before the human body impact to the floor. Pre-impact fall detection method and lead time before impact are very important factors that has been used to save a person who takes risks. In this paper, multiple viewpoints in vision based sensing that provided by multiple Kinect (c) sensors using dynamic threshold based and center of gravity are proposed. Pre-fall detection alert will be triggered by acceleration of head position compared with dynamic threshold based approach and range of center of gravity compared with base of support area. Moreover using blinding technique without video stream recording is one of useful feature in our vision based approach for reducing privacy issue. Not only fall actions but also a series of normal activities in daily living such as sitting, bending and laying were performed by 10 young adult volunteers. Results from the experiments indicate that the proposed method lead time is about 500 milliseconds which is faster than previous proposition and it is appropriate for airbags inflation triggering.