## 2 Literature Review  
Human activity recognition and localization are crucial aspects facilitating human-computer interaction applications. They have many applications in security, surveillance, smart homes, daily activity monitoring, and fall detection for elderly people [18], and traditional approaches have used computer vision, wearable sensor-based, and smartphones. However, conventional methods, such as computer vision-based techniques, have defects in security and are exposed to environmental impacts like light occlusion and interference [19]. Likewise, sensor-based techniques usually require individuals to garb and attach smart sensors, which makes this process more tiresomeness [20]. On the other hand, Wi-Fi signal-based sensing has the ability to overcome the aforementioned challenges, since it does not require any wearable or attachable device for individuals. Additionally, Wi-Fi signal-based techniques provide various advantages due to their easy installation, low cost, and private security protection. Consequently, Wi-Fi signal-based devices are preferable for sensing human activities. Moreover, Wi-Fi technology exploits CSI and RSSI signals [21];hence, it is susceptible to multipath interference and narrow-band interference, so it may cause very low recognition accuracy. Contrasting RSSI, CSI provides amplitude attenuation and phase shift in multipath propagation at distinct frequencies. Therefore, CSI affords ampler and steadier frequency parameters for preeminent recognition and localization accuracy. CSI implies the variation in signals propagating from the transmitter to the receiver at a particular carrier frequency. Amplitude attenuation and phase shift may occur because of the impact of surrounding locations and nearby human motion. Therefore, CSI is used for Wi-Fi signal-based sensing. Several studies have explored Wi-Fi CSI for human activity recognition and localization, but they often focus on either indoor environments or treat these tasks independently rather than jointly. Below, we discuss key research in these domains and highlight existing gaps. For instance, [PERSON]. [22] investigated joint tasks in an indoor environment by introducing amplitude attenuation. Similarly, [PERSON]. [23] employed UWB for joint human activity recognition and localization in indoor locations. In contrast to indoor environments, there is inadequate multipath propagation in an outdoor environment. Hence, simultaneous activity recognition and localization tasks are challenging. This study examined simultaneous activity recognition and localization for a certain activity by employing CSI in an outdoor environment. A limited number of research studies have been conducted in joint tasks in indoor locations. However, none has tackled the challenge of environmental factors on CSI variation and repeated tiresome data collection in outdoor environments. Moreover, the previous related works only coarsely recognize and localize humans, and the sensing outcomes commonly lack fine details because of insufficient CSI data. Naturally, Wi-Fi signals are sequential data, which are often collected from different access points over time. Therefore, a recurrent neural network (RNN) is the type of neural network broadly applied for time-sequential signals; its architecture is a combination of the input, output, and a hidden layer. Therefore, it is essential for CSI-based human activity recognition and localization, and several works have been conducted [24] to extract features and apply the long-short-term memory recurrent neural network over this network; several features in CSI signals can be effectively learned and extracted. However, if the size of the input is too large, which causes a large training time, it is time-consuming to train the RNN. This employs three deep learning networks together, including an auto-encoder, a convolutional neural network (CNN), and LSTM for human activity recognition [25]. It can sanitize annoying factors in the received raw CSI data.