## 1 Introduction  
The implementation of wireless sensing applications using Wi-Fi signals is both straightforward and cost-effective, leveraging the existing infrastructure designed for wireless communication [1]. Consequently, Wi-Fi signals have become extensively utilized for non-intrusive sensing applications in recent years. In modern Wi-Fi source devices, achieving high performance relies on integrating key technologies such as Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM), which are essential for optimal functionality and efficiency [2]. MIMO-OFDM offers Channel State Information (CSI), indicating power attenuation and phase shift from transmitter to receiver at specific carrier frequencies. Beyond enhancing Wi-Fi network performance, CSI is valuable for Wi-Fi-based sensing applications, capturing signal travel through objects. These variations can be input into predefined models or machine learning algorithms for analysis [3]. This study used signal variations for simultaneous human activity recognition and localization. Nowadays, commercial Wi-Fi devices have been demonstrated as the primary source of radio frequency signals [4], beyond transmitting information such as audio, video, voice, and data over the internet. Recently, Wi-Fi signal-incorporated devices have been pervasive in different urban outdoor environments. Through diligently investigating CSI from the radio spectrum, human activity recognition, gesture recognition, motion detection, fall detection, and breath monitoring become feasible [5]. In addition, some manufacturing practices have already started to provide Wi-Fi infrastructure-based outdoor localization services [6], which are widely used in various areas, including location-based and context-aware services. Meanwhile, human activity recognition is the most motivating research area in ubiquitous computing and human-computer interactions. Human activity recognition plays a crucial role in applications such as security surveillance, smart healthcare for the elderly, and smart city planning. Accurate localization enhances these applications by enabling real-time monitoring and automated decision making [7]. Different approaches have been conducted to human activity recognition techniques using computer vision, wearable sensor devices, and radars [8]. However, there are several limitations in deploying conventional techniques to sense human activities. Computer vision-based sensing technologies have constraints related to operating in a certain range of line-of-sight (LoS) environments and being predisposed to obstacles, sensitive to lighting conditions, and prone to the problem of dead angles [9]. Moreover, the installation and maintenance costs of cameras are exorbitant, besides the privacy and security issues [10]. Similarly, wearable sensor-based techniques regularly require individuals to wear a particular device to monitor human activities. It is tiring to carry it, and it can be forgotten. Additionally, wearable devices needed for battery recharging were also seen as a major bottleneck for wearable device-based sensing technology [11]. On the other hand, in contrast to wearable devices, radar, and computer vision-based sensing techniques, Wi-Fi signal-based sensing is neither intrusive nor sensitive to lighting conditions. In addition, the usage of Wi-Fi signals turned out to be very promising for reasons such as its practical bandwidth, wide coverage, equitable transmitted power, and improved signal compared to short-range sensing technology [12]. Wi-Fi signal-based sensing techniques mainly utilize two different kinds of signals, CSI and received signal strength indication (RSSI) [13]. RSSI is the measurement of the average power of a received signal. It is also a coarse-grained radio channel measurement, and the performance of the RSSI-based system is affected by the signal quality. In an intricate environment, fluctuations in the signal quality arise because of multipath propagation [14].