Detection And Analysis On Fall Behavior Based On Kinect Sensor V2

With the progress of society,the aging of the population has become an inevitable problem in the development of society in China and the world.The elderly are prone to fall due to the gradual decline of physiological functions,the weakening of the body coordination ability,and the blurring of the line of sight.Falling is not only a physical trauma,but also a psychological burden.Therefore,the establishment of an intelligent home monitoring system for discovering the fall of the elderly,and the timely notification of relevant contacts for treatment has become a trend of development.In this paper,the Kinect v2 camera is used to obtain the bone point data and color map information of the elderly for research and analysis,and the conditions for judging the fall behavior of the elderly are obtained.Based on the analysis and summary of the current fall detection method,this paper uses the Kinect v2 fall detection method to predict the fall behavior of the elderly.Based on the skeleton point data and color image acquired by Kinect v2,the research content is divided into two parts,namely,the fall detection based on the skeleton point feature and the fall detection based on Kalman filter.The fall detection method based on skeletal point features mainly uses bone tracking technology to select three skeletal points such as the center point of the human body,the center point of the two hips,and the right foot.The spatial position of the center point of the human body,the speed of motion,and the center point of the two hips are calculated in real time.Positional relationship,height from the ground and other parameters,first of all a large number of experimental analysis of the speed threshold and height threshold at the time of fall,the threshold range is obtained,and the initial judgment of whether the elderly has a fall behavior,due to the single dimension of the threshold judgment criteria,may be ignored The characteristics of the movement of the fall behavior are also greatly influenced by the individual.Therefore,the acceleration sequence is obtained by using the velocity changes of adjacent 10 frames during the descending process of the human center point,and then the hidden observation sequence is trained by using the hidden Markov model to calculate the matching probability P,thereby judging whether the elderly has a fall event.The fall detection based on Kalman filter mainly uses the Kinect v2 camera to acquire the video sequence image,then adopts the adaptive background model strategy,uses the background difference method to detect the elderly,and applies the Kalman filter to predict the trajectory of the elderly.The motion trajectory prediction model and the actual motion model are matched to determine whether the elderly person has a fall event.Finally,we use two detection methods to construct the same fall detection system,and verify the reliability of the system through experiments.After experimental analysis,the method is feasible.