Indoor Human Activity Recognition Method Using CSI of Wireless Signals

Abstract

Human activity recognition has been studied for decades by leveraging vision-based and sensor-based technologies. However, the drawback of such techniques such as short-range area, and invasion of human privacy in vision-based technology, and inappropriate usage for sensor devices or inconvenient feeling of the user to carry sensor devices. All of these reasons encouraged researchers to wireless-based sensing technology or called device-free because the user needn't carry devices nor monitoring with a camera. In this regard, we present a device-free activity recognition system by exploiting the Channel State Information (CSI) of Wi-Fi signals which recognizes three dynamic activities. We build our prototype with a high efficient feature extraction algorithm and an agile and accurate classification algorithm. To examine the feasibility and performance of the proposed system, hundreds of experiments have been implemented in LOS and NLOS scenarios in a dynamic environment with different volunteer users. The experiment results show that the proposed method has gained a high-accuracy rate in both LOS and NLOS.

Device free human activity and fall recognition using WiFi channel state information (CSI)

“…In recent years, many articles have emerged that have used CSI for detecting human activity, for a comprehensive survey, see (Wang et al 2017c). In particular, we refer to Wi-Hear (Wang et al 2016a), Wi-Eyes , CARM (Wang et al 2015a), for gesture recognition to (Pu et al 2013;Al-Qaness et al 2017), and for fall to RT-Fall (Wang et al 2017a) and WiFall (Han et al 2014). In Xin et al (2018)) the phase differences between waveforms of multiple antennas are used to detect human activities quite generically.…”

“…Recently channel state information from the WiFi network interface cards (NIC) (Al-Qaness et al 2017) has gained a lot of attention. Unlike received signal strength indication (RSSI), CSI is measured from radio links per orthogonal frequency division multiplexing (OFDM) subcarriers for each received packet (Al-Qaness et al 2016Cheng and Chang 2017).…”