A Multi-class Dataset Expansion Method for Wi-Fi-Based Fall Detection

Nowadays, with the wide commercial use of Wi-Fi technology, the use of Wi-Fi channel state information (CSI) for fall detection has gradually become a hot research field. However, many existing fall detection systems based on Wi-Fi lack accurate action classification because of the high acquisition cost of complex action datasets. They cannot accurately identify complex fall actions, and have a high false positive rate. This paper proposes a multi-class dataset expansion method for different fall actions and non-fall actions, which classifies the movements in detail according to fall speed and other limb movements and expands the scale of the data set by dividing and reorganizing the limited data. As a result, the proposed method reaches a recognition accuracy of 91.6%.