| Summary: | This research introduces a Wi-Fi passive radar approach to address key challenges in current fall detection systems, including privacy concerns associated with camera-based systems, and the inconvenience of wearable sensors. By leveraging existing Wi-Fi signals, this research offers a non-intrusive and low-cost solution for continuous fall monitoring in home environments. The primary objective is to enhance safety for individuals at risk of falling by developing a sensor capable of accurately differentiating between fall and non-fall incidents. The methodology involves analyzing a dataset of 10 radar signals, equally distributed between falls and non-falls by using effective thresholding techniques. The system examines features such as voltage drop and signal envelope characteristics to identify fall incidents. The threshold-based classification method demonstrates remarkable accuracy, successfully distinguishing between falls and non-falls with 100% precision in our experimental dataset. This research contributes to the development of non-intrusive, reliable fall detection systems, potentially improving the safety and independence of elderly individuals living alone. © 2024 IEEE.
|
|---|