## 2 Related Work
Since CSI data streams vary in terms of time and correlate with target actions over a certain period, we can therefore assume them to be CSI images with temporal dependencies and leverage the extraordinary capabilities of CNNs for identifying such actions as an image recognition problem. There has been extensive research work in the last few years regarding the very same idea of representing CSI data streams in images and making the most of CNN-based architectures for device-free HAR. In the study of [55], a four-stage fall detection mechanism was presented. (1) They collected raw CSI data. (2) They denoised these data using discrete wavelet transform (DWT) [56]. DWT increased the signal-to-noise ratio and reduced the mean square error. (3) Short-time Fourier transformation [57] was applied to obtain CSI time-frequency diagrams for model training. Frequency domain analysis is effective for eliminating environmental influences and can identify the same activities performed at different venues. (4) A pre-trained GoogLeNet model [58] was further trained using time-frequency spectrograms for new domain classifications. Likewise, the work of [59] exploited the time-frequency spectrograms generated from a principle component analysis (PCA) of the first 20 impactful data features extracted from multiple sub-carriers. A generator and two asymmetric classifiers were operated in an adversarial fashion to maximize the discrepancy near the decision boundaries of the classifiers. Finally, the generator was optimized to produce target data features in support of source samples. This increased model accuracy in domain-shifting variations caused by unseen users and new locations. Recent solutions for cross-domain Wi-Fi sensing are intended to align a single source domain to a single target domain or a multi-source combined domain to a single target domain. Fidora [60] is a wireless localization system designed to overcome Wi-Fi fingerprint inconsistencies caused by factors like body shape variations, background objects, and environmental changes. The novelty of their approach is the generation of augmented samples from collected CSI data using a data augmenter consisting of eight variational auto-encoders (VAE), one for each location. The augmented and original data were passed through feature extraction layers followed by classification and reconstruction layers. The classifier predicted location labels, while the reconstruction layers regenerated the original input samples. Compared to baseline models such as AutoFi [61], VAE-only, and FiDo [62], Fidora demonstrated a significant performance improvement, achieving 17.8% and 23.1% higher F1 scores in cross-user and cross-environment scenarios, respectively. WiGR [45] is a Wi-Fi-based few-shot learning system for gesture recognition, capable of domain adaptation across different environments. It employs supervised learning to generalize across new tasks using limited data. The CSI phase values, preprocessed using phase unwrapping and an FIR filter, were used for training. The model consists of a feature extraction subnetwork and a similarity discrimination subnetwork. The extracted features are combined, and similarity scores determine gesture classifications. WiGR outperformed competing models, including WiGeR [63], WiCatch [64], SignFi [65], and Siamese-LSTM [66], across different users, environments, and locations. In our previous study [50], we examined the impact of transitioning users from the training phase to the testing phase on the predictive performance of an adversarial model named DASAN-MMD. This model utilized CSI data in a single-source-domain to single-target-domain setup. However, our proposed approach struggled to achieve high accuracy in some of the cross-user tasks due to the limited number of per-domain samples available in the public dataset [49].