## 1 Introduction
Unlike RSSI, CSI is a fine-grained value and captures signal strength and phase information for orthogonal frequency-division multiplexing (OFDM) subcarriers between each pair of transmitter and receiver antennas. Moreover, CSI offers detailed information to capture the intelligent change in the signal quality between transmitter and receiver devices. Therefore, CSI-based Wi-Fi sensing has better performance [15]. CSI denotes the behavior of wireless signal propagation from the transmitter to the receiver at a particular carrier frequency along various multiple paths. It is a matrix of complex number values representing the amplitude attenuation and phase shifts triggered by path loss and multipath effects that occurred during scattering, diffraction, and distance attenuation [16]. Therefore, the time series characteristics of CSI can simultaneously recognize and localize a certain activity in a specific location. Most existing studies focus on human activity recognition using Wi-Fi CSI in indoor settings, where multipath propagation enhances signal stability. However, outdoor environments introduce greater signal variability due to fewer reflections, making joint activity recognition and localization a more challenging and underexplored area. This study addresses this gap by developing a hybrid deep learning model that is optimized for outdoor scenarios. Hence, this study investigated joint tasks in an outdoor environment. The rationale for examining joint human activity recognition and localization is to enhance the performance in recognizing the individual's action within their outdoor location. Localization requires substantial knowledge of the context in which the user is explicitly located [17]. Additionally, exploring joint tasks has revealed numerous beneficial human-computer interaction applications in outdoor environments. The joint task consisted of two main objectives as follows: identifying activities carried out at different locations and localizing individuals based on their activities. A significant challenge we encountered is the potential for data leakage in our study, particularly concerning the CSI of Wi-Fi signals. Analyzing patterns in Wi-Fi signals, specifically the dynamics of CSI, allows us to differentiate between various activities and pinpoint their specific locations. However, collecting CSI data presents a significant challenge due to the associated costs of the required equipment. In this research paper, we utilize synthetic data as a strategic approach to reduce the need for actual measured CSI. By incorporating synthetic data into our study, we aim to address the challenges of relying solely on real-world CSI measurements. This approach helps us expand our dataset, offering a more comprehensive and cost-effective solution for analyzing Wi-Fi signal patterns and CSI dynamics in distinguishing between different activities and their locations. We propose a deep learning-based joint task to tackle the aforementioned issues by leveraging the advantageous properties of deep learning architectures. Therefore, the main contributions of this work can be summarized as follows:  
1. A joint approach to human activity recognition and localization that utilizes Wi-Fi signal information and an LSTM-BIGRU network for enhanced accuracy. 2. The utilization of CTGAN to generate synthetic data and prevent CSI leakage. 3. The use of a real-world dataset obtained through the PicoScene Wi-Fi sensing device, which includes magnitude and phase information. The proposed approach enhances accuracy in activity detection and localization tasks by integrating both types of data. The subsequent sections of the paper are structured as follows: Section 2 presents the literature on human activity recognition and localization using deep learning algorithms and identifies gaps in the research. Section 3 outlines the research methodology. Section 4 discusses the proposed system architecture. Sections 5 and 6 discuss results and provide a discussion. Section 7 concludes the paper and suggests future research directions.