## 5 Results
### Classification Task  
Accuracy, macro-averaged precision, macro-averaged recall and macro-averaged F1 score are used to evaluate the classification performance of the model. Accuracy is defined as follows:  
\[\text{Accuracy}=\frac{\sum_{i=1}^{k}\text{TP}_{i}}{\sum_{i=1}^{k}(\text{TP}_{i}+ \text{FP}_{i})} \tag{14}\]  
The accuracy rate shows the proportion of activities that were correctly identified. \(\text{TP}_{i}\) is the number of true instances in category \(i\), \(\text{FP}_{i}\) is the number of false positive instances in category \(i\), and \(k\) is the total number of categories. In activity recognition, high accuracy means that the model can reliably tell the difference between different types of activities. Macro average precision, macro average recall and macro average F1 score are defined as follows:  
\[\text{Precision}_{\text{macro}}=\frac{1}{k}\sum_{i=1}^{k}\frac{\text{TP}_{i}}{ \text{TP}_{i}+\text{FP}_{i}} \tag{15}\]  
\[\text{Recall}_{\text{macro}}=\frac{1}{k}\sum_{i=1}^{k}\frac{\text{TP}_{i}}{ \text{TP}_{i}+\text{FN}_{i}} \tag{16}\]  
\[\text{F1}_{\text{macro}}=\frac{1}{k}\sum_{i=1}^{k}\frac{2\times\text{Precision }_{i}\times\text{Recall}_{i}}{\text{Precision}_{i}+\text{Recall}_{i}} \tag{17}\]  
Precision measures the reliability of the model to categorize samples into a particular class, where \(\text{TP}_{i}\) and \(\text{FP}_{i}\) denote the number of true and false positive cases in class \(i\), respectively. Recall measures the model's ability to recognize all samples in a given category, where \(\text{FN}_{i}\) denotes the number of false negative cases in category \(i\). The macro-averaging approach considers all activity categories equally and is not affected by category imbalance. The F1 score is a reconciled average of precision and recall, where \(\text{Precision}_{i}\) and \(\text{Recall}_{i}\) denote the precision and recall of category \(i\), respectively. Table 2 presents a comprehensive comparison of the overall classification performance across all evaluated models. Notably, CoTD-VAE outperformed all baseline methods on all classification metrics reported in Table 2, demonstrating its robust capability on the classification task. Table 3 lists the F1 Score, Precision, and Recall for different models across each activity category. Variations in performance between models and activity categories can be observed. CoTD-VAE consistently shows strong performance, ranking in the top two in  
Figure 3: Qualitative analysis of CoTD-VAE’s reconstruction capabilities on selected illustrative channels. Comparison of original signals (blue curves) and signals reconstructed by the CoTD-VAE model (red curves) for a selected sample from the UCI HAR test set. To provide a more focused and insightful analysis, three illustrative channels were selected to demonstrate the model’s distinct capabilities and behaviors. most activity categories. CVAE performs exceptionally well in SITTING and STANDING, but shows significant weaknesses in other categories such as WALKING DOWNSTAIRS. LSTM-AE also performs prominently in LAYING and WALKING DOWNSTAIRS. LAYING appears to be easier for most models to predict, achieving very high or perfect metric scores. To further validate our model's effectiveness in a real-world clinical setting, we conducted a comprehensive evaluation on the MIMIC-IV dataset for the task of predicting thrombus events. Given the significant class imbalance inherent in clinical data, where thrombus events are rare, we focused on metrics that provide a more nuanced view than accuracy alone, such as F1-Score and the Area Under the Receiver Operating Characteristic Curve (AUC). Table 4 presents the detailed classification results. While all baseline models achieved excellent performance, CoTD-VAE demonstrated the best performance. It achieved the highest F1-Score (0.9939) and a perfect AUC (1.0000), showcasing its superior predictive power. Notably, the performance of CoTD-VAE surpasses that of the standard end-to-end LSTM classifier (F1-Score 0.9939 vs. 0.8968).