## 1 Introduction
Figure 1 illustrates the workflow of our proposed methodology, detailing the process from collecting raw CSI measurements to classifying the activities being performed. (1) We first extract the raw CSI magnitude values. (2) We normalize them to a scale of [0-255] RGB scale. (3) We apply PCA for selecting features with maximum variation. (4) We use t-SNE embedding to find local relationships within high-dimensional data by projecting them into a lower-dimensional space. (5) Spectrograms are generated leveraging STFT and are fed to the model as input. (6) The model use such spectrograms generated from diverse sources and transforms the model to adopt a new target domain. (7) Finally, after being optimized, the model classifies activities on this new domain. The motivation behind our proposed model stems from the advantages of leveraging diverse source distributions. Models trained with multiple sources develop richer feature representations and exhibit greater robustness when adapting to unseen target domains. This is because having multiple sources increases the likelihood of overlapping features with the target domain, facilitating smoother adaptation and reducing overfitting to a single source. Moreover, extracting common domain-invariant representations becomes more challenging in a SUDA (single-source unsupervised domain adaptation) setting when dealing with highly diverse source domains. Additionally, public CSI datasets are generally unsuitable for cross-domain analysis due to data scarcity within individual domains. This limitation can potentially be mitigated by incorporating multiple sources, which provides a larger training dataset and improves model performance in low-data scenarios. The main contributions of our work in this paper are summarized as follows:* Multi-source M-FUDA outperforms all the baseline methods for most of the transfer learning tasks performed on three publicly available CSI datasets utilized for creating cross-user, cross-environment, and/or cross-atmospheric conditions using device-free HAR. * The proposed model is applied to a multi-source unsupervised domain adaptation (MUDA) setup and contrasted against a single-source unsupervised domain adaptation (SUDA) setup designed with all the underlined sources combined in a single-source vs target setting. The proposed model produces promising results, surpassing traditional domain adaptation methods for device-free sensing. Our findings suggest that aligning multiple domain-invariant representations with domain-specific classifiers near class boundaries improves generalization. This alignment is particularly effective for each pair of source and target domains. As a result, the model performs well across various domain-shifting tasks. * Empirical evaluation of various distance minimization approaches on one of the selected CSI datasets for each pair of source and target distributions indicates the suitability of maximum mean discrepancy (MMD) over others. * Extensive evaluation shows that the predictive outputs of classifiers from different CSI sources capture target samples far from the support of underlying sources with the involvement of discrepancy and contrastive semantic alignment losses. This shows the role of proposed alignment losses in reducing the gap between the classifiers.