Sensing and Communication Integration Based Fall Detection with Commercial WiFi

With the increase of China's elderly population year by year and the increasing number of elderly people living alone, the problem of elderly people death caused by falls becomes more severe. Conventional vision based approaches suffer from some privacy issues to the elderly or guardians. And a wearable sensor-based fall detection methods would leave the elderly constrained and stressed. Therefore, the fall detection technology based on WiFi wireless network has been proposed. This is owing to its significant advantages that it will not cause privacy disclosure problems, nor will it give the elderly any substantial physical bondage and pressure. In this paper, we construct a Channel State Information (CSI) acquisition platform based on regular communication packets with commercial WiFi to perform the sensing via the integration of sensing and communication. In particular, the WiFi works in the commuication mode and sends the comunication packet. The CSI is obtained from the communication package and is exploited for sensing. Then by pre-processing the collected CSI, the feature classification information related to falls is extracted for model identification. Based on the features, the neural network based fall detection algorithm is performed to realize the fall detection. The fall detection system equipped with the front-end interface based on Vue3 and the back-end service based on Python is implemented. The experiment results demonstrate that the system can achieve real-time and accurate fall detection, even when WiFi works in the communication mode.