# Device-Free Indoor Activity Recognition System
## 1 Introduction  
Human activity recognition (HAR) has increasingly attracted intense academic and industrial interest due to its various applications in real life such as elderly monitoring. In general, traditional HAR systems have been considered as device-based approaches such as vision-based [1], body-worn sensors [2], and smartphone interior sensors [3]. However, both vision-based and sensor-based activity recognition systems have certain drawbacks. For instance, vision-based systems cannot work through walls and require good lighting conditions. Moreover, they invade human privacy. Sensor-based approaches are inappropriate for the user to carry a device that is sometimes easy to forget or inconvenient in some conditions. With the rapid development of wireless communication, mobile computing and artificial intelligence, the common broadband wireless signal has been gradually extended from pure communication and networking to environment perception and localization services, forming a new research direction, namely: device-free wireless-based sensing technology [4]. Unlike special complex ultra-wideband (UWB) wireless sensing systems or radar perception technologies [4, 5], wireless-based sensing systems principally refer to the use of ordinary broadband wireless equipment (such as WLAN) to achieve the general context-aware technology. This technology is primarily called device-free sensing technology. It gains this label owing to the fact that perceived environments do not require to be supplied with sensor devices or cameras. Furthermore, the perceived objects do not require carrying wireless devices or wearing sensor devices. Earlier, [PERSON] et al. [6] introduced the concept of device-free passive (DfP) localization, which enables locating and tracking entities that do not carry a device nor participate in the localization process. They leveraged the fluctuation of the received signal strength indicators (RSSI) as a metric for a human appearance in the observed area. Therefore, RSSI has been adopted in vast approachessuch as localization [7, 8], activity recognition [9, 10], and gesture recognition [11]. However, RSSI is an unstable metric due to the severe performance degradation of RSSI in complex environments because of multipath fading and temporal dynamics [4]. Recently, researchers have tried to leverage wireless physical layer channel state information (CSI) instead of wireless medium access layer received signal strength indicators (RSSI), since CSI achieves finer sensing granularity and can use information dimensions to obtain a qualitative improvement. Unlike RSSI, CSI is measured from radio links per orthogonal frequency-division multiplexing (OFDM) subcarriers of each received packet. Therefore, CSI has more information since RSSI value is only per received packet. Moreover, CSI is more robust to complex environments. Therefore, CSI helps in building a more sophisticated, more sensitive, and more robust sensing model. Such progress also makes more sophisticated sensing applications become more possible. Accordingly, in recent years, CSI has been leveraged in different device-free sensing approaches such as human localization [12, 13], motion detection [14, 15], people counting [16], activity recognition [17, 18], and gesture recognition [19, 20]. In this paper, we present a device-free human activity recognition system which leverages the fluctuation of CSI at the receiver end of the wireless system. The proposed human activity recognition method detects human activity without specific devices. The human activity recognition method proposed in this paper is motivated by a need for decreasing the installation complexity and development costs induced by the traditional device-based activity recognition systems. The system recognizes activities by analyzing the channel state information variation at the receiver end of wireless system. We exploit the fact that human motion in an indoor environment interferes with wireless signals and causes fading and shadowing effects.