## 5 Materials and Methods
### Contrastive Semantic Alignment
We also used the same pre-trained model ResNet50 [80] as the initial block for all the baselines and fed them with combined source domains into a traditional single-source vs target setting to explore whether the multiple sources were valuable to exploit in a single-source domain adaptation (SUDA) setting or in the proposed M-FUDA in a multiple-source domain adaptation (MUDA) setting applied to cross unaligned domain-specific distributions in device-free human activity classification. The experimental micro- and macro-F1 results reported in Tables 4 and 5 show that M-FUDA (MMD) is 13.78% better in terms of averaged micro-F1 score and 15.47% better in terms of averaged macro-F1 score than the combined-source MCD considering 3 domain shifting tasks created by 3 different users. Each task is repeated for 10 trials and an average figure is reported. These readings further validate that multi-source M-FUDA (MMD) is 3.85% higher in averaged micro-F1 score and 4.13% higher in averaged macro-F1 score than that of combined-source M-FUDA (MMD) calculated for 3 cross-user tasks run for 10 trials each. This substantiates that M-FUDA (MMD) can achieve a steadier recognition accuracy in a MUDA setting than its counterpart combined-source SUDA setups for cross-user domain shifting tasks in device-free HAR. Similarly, M-FUDA (MMD) achieved very high recognition accuracy compared to CORAL [72] and Wasserstein [89], which ranked second-last and last in predictive performance, respectively. In contrast, M-FUDA (MK-MMD) outperformed M-FUDA (MMD) by 2.47% in averaged micro-F1 and macro-F1 scores. However, this improvement came at the cost of longer training times due to the higher computational complexity of multi-kernel evaluation, as shown in Table 10. It is evident that there was a big difference in the performance of multi-source M-FUDA vs combined-source SUDA methods, validating the superiority of the proposed architecture in cross-user conditions using device-free Wi-Fi CSI data. A detailed analysis of various cross-user tasks on the Parisafm dataset is presented \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & **Micro-F1** & & & \\ \hline \multicolumn{3}{c}{**Source Domains**} & \multicolumn{3}{c}{**Target Domain**} & \multicolumn{3}{c}{**Proposed Multi-Source Models**} & \multicolumn{3}{c}{**Combined-Source Models**} \\ \hline
**Source 1** & **Source 2** & **Source 3** & **Target 1** & **M-FUDA (MMD)** & **M-FUDA (MK-MMD)** & **M-FUDA (MMD)** & **CORAL [72]** & **Wasserstein [89]** & **MCD [59,88]** \\ \hline
**S1** & **S2** & **S1 + S2** & **S3** & 0.86 & 0.86 & 0.81 & 0.83 & 0.83 & 0.78 \\ \hline
**S2** & **S3** & **S2 + S3** & **S1** & 0.73 & 0.83 & 0.64 & 0.62 & 0.62 & 0.59 \\ \hline
**S1** & **S3** & **S1 + S3** & **S2** & 0.84 & 0.79 & 0.88 & 0.66 & 0.66 & 0.77 \\ \hline \multicolumn{3}{c}{**Average**} & \multicolumn{3}{c}{**0.81**} & \multicolumn{3}{c}{**0.83**} & \multicolumn{3}{c}{**0.78**} & \multicolumn{3}{c}{**0.70**} & \multicolumn{3}{c}{**0.70**} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Micro-F1 scores for multi-source M-FUDA and baseline models across cross-user tasks. Green background indicates the best accuracy for an individual task; blue background indicates best average accuracy; yellow background and bold font highlight the average performances.