Fall Detection Method Based on Wirelessly-Powered Sensing Platform

Falls are one of the common reasons that affect the health of the elderly. Because of its high incidence and high occasionality, the assistance rate of the elderly is lower. Therefore, the fall detection method with an accurate and timely research and development can better help patients get effective assistance. We use a wirelessly-powered sensing platform to easily wear, which can convert RF signal as its own power without replacing any battery, as well as, it can work in non-line-of-sight environment and design a fall detection method based on wirelessly-powered sensing platform. Firstly, the wirelessly-powered sensing platform collects the acceleration data of the human waist and obtains the motion acceleration and its corresponding Euler angle information. Then, combining with Discrete Wavelet Transform and Hilbert-Huang Transform, a algorithm for decomposing acceleration signals is proposed to extract signal information. Finally, an abnormal detection algorithm for Euler angle is proposed, we use the Support Vector Machine algorithm with the abnormal detection algorithm for Euler angle to detect a behavior of the fall. At the same time, in order to alleviate the pressure of power consumption, a sampling factor is set to dynamically change the sampling frequency and reduce power consumption. Experiments show that this method has a higher accuracy, which is over 94.7% of accuracy of the lowest sampling frequency. In the meantime, it has important meaning for the assistance of patients with the fall.