## 4 Proposed System Architecture  
As shown in Figure 2, the proposed system architecture is divided into four phases, as follows: Phase I: Data collection from Wi-Fi signals, considering the transmitter (Tx) and receiver (Rx) with the activity, leading to the generation of raw CSI data. Phase II: Data splitting for training and testing, incorporating the fusion of synthetic and original data. Phase III: Real model training and testing, including comparisons between baseline and hybrid models. Phase IV: Training and classification, utilizing ReLU as the activation function, with both activity and location being identified. Figure 2: Proposed system architecture. The details of the proposed flowchart are narrated as follows: PicoScene is a Wi-Fi sensing platform that provides high quality and synchronized CSI data from Wi-Fi devices. PicoScene is designed to collect and process large-scale Wi-Fi CSI datasets for various research purposes, such as human activity recognition, localization, and wireless communication system optimization. Additionally, when compared to other existing tools like the Linux CSI tool and Atheros CSI tool, PicoScene has several advantages. Firstly, it supports a wide range of Wi-Fi chipsets and provides a higher sampling rate (up to 40 MHz) and higher resolution (up to 14 bits). This leads to more accurate and detailed CSI measurements. Additionally, PicoScene provides an easy-to-use and customizable software interface for data collection and processing, which can be adapted to different research scenarios. Lastly, PicoScene offers a powerful and flexible platform for Wi-Fi sensing and provides significant advantages over other tools for collecting Wi-Fi CSI data [39]. Due to the above reasons, we have used the PicoScene Wi-Fi sensing platform to collect CSI data for our joint human activity recognition and localization work. We implement the IEEE 802.11n protocol with a TP-Link Archer C1200 AC1200 wireless dual band gigabit router as a transmitter and used a Toshiba portage R930 laptop equipped with an intel(r) Wi-Fi Link 5300 AGN network interface card (NIC) as the receiver device [40]. In our work, we selected four activities as follows: walking, standing, sitting, and bending. As shown in Figure 3, the aforementioned activities were carried out across 10 distinct LoS locations between the transmitter and receiver devices with the same pattern. After collecting Wi-Fi CSI data, CSI data provides information about the signal's amplitude and phase, which can be used for human activity recognition and localization. The amplitude and phase information of the CSI can be used to extract features, which can be used to distinguish between different human activities. It is important to note that the CSI matrix only contains raw information and thus is not directly applicable to human activity recognition. This is because it includes a lot of irrelevant information that can reduce the quality of the features extracted. Utilizing the CSI matrix to directly detect human activities can be time-consuming and increase system overhead due to its large size. To overcome these challenges, several techniques, such as nonlinear amplitude and phase removal, background reduction, correlation, filtering, and other preprocessing techniques, were used to produce accurate CSI data. Since Wi-Fi sensing passively captures movement data, privacy considerations are critical. This study ensured the compliance with ethical guidelines by obtaining participant consent and anonymizing CSI data before processing. Additionally, no personally identifiable information was collected, and all data were securely stored to prevent unauthorized access. In addition to extracting phase and amplitude from the CSI data, we implemented different pre-processing techniques to enhance data quality. One of the key steps in handling outliers was the Nonlinear Amplitude and Phase Removal method, which addresses  
Figure 3: Working environment in Bahir Dar University Institute of Technology campus and the types of activities conducted in this study.