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This work contributes on the fusion of multiple Kinect based skeletons, based on dynamic threshold and bounding box posture analysis which is the only research work reported so far. As the second leading cause of accidental death extensive is unintentional falls, which is a vital cause of personal harm, particularly with the venerable. Accordingly, many studies in healthcare are achieving on the improvement of the pre-fall detection system to secure the protection of those who are possible to be concerned. Pre-impact fall detection system has to overcome many difficulties to improve an effective system. Some of the particular difficulties are obtrusion, occlusion, and overlap in the vision-based method.
In this research, the purpose of adopting the bounding box and head velocity method compare with a real-time dynamic threshold is for analyzing the fall and non-fall incident accurately. Furthermore, the skeleton joint position provided by multiple Kinect viewpoints are utilized for the reason of resolving in obtrusion, occlusion, and overlap issues without demanding of markers. Though, the various fuzzy rule base methods also are applied for the final decision of lead time detection and triggering fall alarm. The demonstration of subjects completion is performed 1,100 actions were included 700 times for activities of daily living and 400 times for falling. All activities performed by ten different volunteers, seven healthy young males, and three healthy young females.
The results have shown that 98.55% of the proposed method is higher accurately detected. However, the proposed method provided the lowest specificity at 97.71%, vice versa it offered the highest sensitivity at 100.00%. It implies that during system provided higher accuracy and sensitivity in pre-impact fall detection, the recognized precision of normal activities will be reduced. Moreover, the multiple Kinect methods not only provide higher accuracy and sensitivity but also offer higher average lead-time as 505.86 ms.
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