## 2 Related Work  
Early reach work in device-free HAR has mainly been developed for a controlled laboratory environment without changing system characteristics from the training to the testing domain [36; 37; 38; 39]. Although the accuracy of these systems is quite high, they cannot be implemented for a domain-shifting task without retraining the model with new target domain-labeled data samples. Without a doubt, is impractical to retrain models for every new domain, and sufficient labeled data for each new domain are not always available. Single-source and combined-source unsupervised domain adaptation (UDA) have been widely adopted in device-free HAR, confronting these shortcomings [40; 27; 41]. FewSense [42] is a few-shot learning (FSL) approach [43; 44] that uses two methods for feature generation. The authors used a trained feature extractor to produce feature embeddings directly from a support set, called direct feature matrix generation. They also utilized an untrained classifier with the feature extractor and optimized their weight matrix using the support set, called fine-tuned feature matrix generation. Then, they computed  
Figure 1: System overflow. Dark red indicates higher amplitude; blue indicates moderate amplitude; yellow and green indicate lower amplitude. the cosine similarity between the latent features of a query set and generated a feature matrix to train the model for novel classes introduced in cross-domains. WiGR [45] and DFGR [46] are also FSL-based deep similarity evaluation networks for activity classification in cross-domain Wi-Fi sensing. Features extracted from a query sample are concatenated with the features of every support sample, and their similarity is computed in a CNN-based similarity evaluation network using episode-based training. Network parameters are updated until the dissimilarity between support and query sets is small. Convolutional neural networks (CNNs) have shown remarkable contributions in the field of computer vision for image recognition tasks. CNN, despite its low preprocessing requirements, can explore the spatial and temporal dependencies in an image, which is also very helpful for HAR applications. [PERSON] et al. [47] used a public raw CSI dataset [48] to generate grayscale CSI images using black-and-white colormaps. These images were fed into two 2D- convolutional layers of 3 \(\times\) 3 filter size, each of them followed by a 2 \(\times\) 2 max pooling layer, ReLU activation, and batch normalization. Finally, a dense neural network with softmax activation was used to produce class predictions. Model overall accuracy was 89.22% with 91% accuracy for fall-like in-domain activities, and it is thus suitable for healthcare applications. [PERSON] et al. [49] produced pseudo-color RGB scale CSI images and fed them to a 2D-CNN model. Model overall accuracy was around 95% better than long short-term memory (LSTM) and bi-directional LSTM regarding training time and recognition accuracy. However, the model applicability was only tested for domain-specific tasks. DASAN [50] is an attempt to use pseudo-color RGB scale CSI images for the analysis of system sustainability in cross-user domain-shifting variations using inter-domain and intra-domain alignment techniques. [PERSON] and [PERSON] [51] performed five transformations on time series CSI data to convert them into images for the first time. These introduced CSI transformations were Gramian angular sum field (GASF) transformation [52], recurrence plot (RP) transformation [53], Gramian angular difference field (GADF) transformation [52], short-time Fourier transportation [54], and Markov transition field (MTF) transformation [52]. A three-layer 2D-CNN architecture was employed to recognize the activity from the converted images. Their results showed the superiority of RT over other techniques; however, this was determined through in-domain testing, which is most likely to achieve good results. The recognition accuracy of these CSI transformations in cross-domains is yet to be explored.