Analysis and Prediction of Elderly Fall Behavior Based on ZigBee Signal Strength Features

The issue of elderly people’s travel safety has attracted widespread attention in society. To address this problem and in line with current research trends, this study proposes an analysis and prediction of elderly people’s fall behavior based on ZigBee signal strength features. Due to the significant changes in radial range caused by movements, this paper investigates how ZigBee signal attenuation features can be used to perceive different angles. Various phenomena such as refraction, diffraction, and scattering can cause different degrees of interference in the normal signal propagation when ZigBee signals encounter different situations. By analyzing the signs of signal reception and detecting changes in signal strength, the physical condition of individuals during signal transmission can be determined. Furthermore, to address the issue of low accuracy in fall detection estimation based on broader spectral indices, this paper proposes an improvement. It presents an algorithm for extracting fall features based on a wider range of spectral indices, namely the fall behavior recognition algorithm.