## 1 Introduction  
The vision of a smart city relies on interactive data dissemination among smart devices through human interaction. Human involvement in commanding machines has traditionally involved manual switching. In recent years, we have entered a developed era of audiovisual techniques involving little or no human obtrusion, bringing the concept of an ultra-modern society closer to reality. Audiovisual and sensor-based networks can monitor an environment continuously for long periods with an unobtrusive sensing mechanism. This unobtrusive sensing forms a key contribution to our ability to recognize human gestures by using smart technologies, motivating the role of an advanced field of sensing, termed human activity recognition (HAR). HAR-based systems are helpful in propagating instructions from humans to computers through gestures/activities without making physical contact. Initial research on HAR-oriented systems mainly concerns audiovisual-based technologies using static or moving image data captured through cameras [1, 2, 3]. This kind of recognition task has serious privacy issues since the collected data are interpreted into human-perceivable form. In- and on-body sensors have also been intensively studied for human motion tracking in coarse- and fine-grain applications. However, they have technical requirements, in particular sensor-mounted devices such as accelerometers, and gyrosensors, and rely on a pre-installed setup with sensing devices attached to or placed inside a person's body [4; 5; 6; 7; 8; 9]. This is not a convenient way of monitoring, and constant examination is impossible because users struggle to carry such cumbersome devices all the time [10]. Wi-Fi-based systems have led to a breakthrough in the field of sensing due to their ubiquitous availability for data communication. This scenario has been successfully leveraged for a wide range of operations including, but not limited to, health monitoring [11; 12; 13; 14], fall detection [15; 16], human gesture and location identification [17; 18; 19; 20], activity recognition [21; 22; 23], object detection [24; 25], and humidity estimation [26]. Inspiration for this modern device-free sensing comes from (i) an easy and convenient way of sensing the environmental impacts of an activity with the help of a multi-path radio wave propagation, (ii) infrastructure-less operation with readily and extensively accessible radio devices, and (iii) high adherence to privacy and governance standards, as the wireless sensor data are complex and cannot be easily understood by the intended or unintended users. Over the past few years, wireless-fidelity (Wi-Fi) sensor-based networks have seen significant demand and rapid growth for transmitting data via radio signals. This is due to their ease of large-scale deployment and high throughput capabilities. Recently, these sensor networks have been utilized for tracking human motion in laboratory environments using device-free wireless sensing (DFWS) technology. Wi-Fi communication is a multi-path propagation of radio waves. These radio signals arrive at the receiving antenna after following several paths with different time delays and undergo reflection, refraction, and scattering by the static and moving objects present in a sensing area. The radio signals emitted by these devices experience fluctuations caused by movement within the sensing environment. These fluctuations create distinct activity patterns at the receiving end which can be leveraged for motion detection. However, the effectiveness of this technology depends on several factors, including sensitivity to changes in the sensing environment, the presence of previously unseen subjects, the ability to track multiple targets, data collection configurations, and the specific activities being monitored [27]. In other words, differences in domain distributions between the training and testing phases arise due to these variations, which are highly likely to occur in real-world scenarios.