## 5 Materials and Methods
### Contrastive Semantic Alignment
We further tested our proposed architectures against two classical distance minimization approaches--correlation alignment (CA) [72; 73] and Wasserstein distance [69; 70]--which have proven their effectiveness in domain adaptation tasks in earlier research works [72; 89]. For these two baseline methods, we used the same architecture as in single-source M-FUDA, with the only difference being that the maximum mean discrepancy (MMD) loss after the domain-specific feature extractor was replaced by these distance minimization techniques. For better clarity, refer to Figure 5. We reported micro- and macro-F1 scores to assess the performance of different models. The best results for each specific task were colored in light green, and the best average of all the tasks was colored in light blue. Micro-F1 reports the models' statistics based on the overall performance of the model, treating every instance as having equal importance, which is preferable in our imbalanced data distribution. Meanwhile, macro-F1 treats minority classes with the same importance as majority classes and reports the model performance across all the classes based on the weighted average of F1 scores for individual classes. Each table in our reported results presents the average of individual micro- and macro-F1 scores, calculated for each domain-shifting task performed on the selected datasets and displayed at the end of the table. ## 7 Results and Discussion

This section presents and discusses our results in three cross-domain experiments performed on three selected datasets. All experiments conducted in this study were performed on an Intel Core i7-9700K CPU equipped with 32 GB of internal memory and an Nvidia RTX 3060 GPU. ### Experiments with Varying Users

Our first experiment was to assess the model accuracy across different users to test how well it was able to generalize various users' activities, each having different physical characteristics, and not become overfitted on any specific user in a pool of diverse source distributions. For this analysis, we chose the Parisafm dataset, which contains data from three users performing seven activities in a laboratory environment; see Table 1. This dataset was suitable for the underlined experiments because we were able to train our model on the features of any two users to investigate the learning adaptability of the third user as a target domain. We utilized three dissimilar source domains. Two of them belonged to single-source subjects 1, 2, or 3, and the third source contained combined data features of previously chosen subjects to achieve better commonality between diverse sources and target distributions. We first examined the significance of different alignment losses at various stages of neural network architecture. This started with the discrepancy (disc) loss, then we added contrastive semantic alignment (CSA) loss and included numerous distance minimization losses as we moved from left to right in Tables 2 and 3. We finally deciphered the recommended settings for our proposed model, with discrepancy loss for aligning multiple classifiers' outputs, contrastive semantic alignment loss for reducing the distance between samples of the same classes, and maximum mean discrepancy (MMD)/multiple-kernel maximum mean discrepancy (MK-MMD) loss for mapping target distribution onto the paired source domain. We called this selected model Multi-Feature Unsupervised



In Tables 4 and 5, we compared variants of multi-source M-FUDA to its combined-source version with MMD loss, CORAL [72], Wasserstein [89], and Wi-Adaptor [59], also known as MCD [88], trained on M-FUDA configurations taken for defining feature extractor and classifier architectures.