Elders’ fall detection based on biomechanical features using depth camera
Abstract
An accidental fall poses a serious threat to the health of the elderly. With the advances of technology, an increased number of surveillance systems have been installed in the elderly home to help medical staffs find the elderly at risk. Based on the study of human biomechanical equilibrium, we proposed a fall detection method based on 3D skeleton data obtained from the Microsoft Kinect. This method leverages the accelerated velocity of Center of Mass (COM) of different body segments and the skeleton data as main biomechanical features, and adopts Long Short-Term Memory networks (LSTM) for fall detection. Compared with other fall detection methods, it does not require older people to wear any other sensors and can protect the privacy of the elderly. According to the experiment to validate our method using the existing database, we found that it could efficiently detect the fall behaviors. Our method provides a feasible solution for the fall detection that can be applied at homes of the elderly.
