## 4 Results and Discussion
### Four-Sensor Configurations
#### 4.4.3 Four-Sensors: Model Performance with 50% Overlapping Data  
In this configuration, four sensors--accelerometer, gyroscope, linear accelerometer, and gravity--are used with a 50% overlapping strategy applied to the data segments. The overlapping segments provide additional continuity and capture transitions between activities more effectively, enhancing the models' ability to recognize patterns across time and improving overall performance.  
The evaluation results in Table 19 reveal that the **TCN** model achieves the highest performance across most metrics, with an F1 Score of **0.9762** on the test set. This model demonstrates strong consistency across training, validation, and test sets, underscoring its robustness in handling overlapping configurations for human activity recognition.  
DT and KNN models provide reliable baseline performance, with DT achieving an F1 Score of 0.9714 on the test set. However, these simpler models tend to lag behind more advanced architectures, particularly when managing high-dimensional sensor data [33,34]. RF and XGBoost show solid performance. RF achieves a test F1 Score of 0.9741, while XGBoost attains a slightly higher score of 0.9760, reflecting the effectiveness of ensemble learning in capturing complex data interactions [26,27].  
SVC also performs well, reaching a test F1 Score of 0.9745. Its ability to handle non-linear relationships makes it a strong contender for activity recognition in overlapping data configurations. The deep learning models GRU and LSTM offer consistent results, with F1 Scores of 0.9762 and 0.9760, respectively, demonstrating their capacity to manage sequential data processing effectively [37,38].  
While the Transformer model shows robust capabilities, achieving an F1 Score of 0.9761, it falls just slightly below the TCN in this configuration. This outcome suggests that while the Transformer's self-attention mechanism is advantageous for long-range dependencies, the TCN may be better suited to handle structured, sequential data in overlapping setups [29].  
In conclusion, although the TCN and GRU models achieve identical high scores in overall metrics, the TCN model is superior due to its lower misclassification rates revealed in the confusion matrices. This means TCN more effectively distinguishes between similar activities in overlapping data, capturing temporal dependencies and subtle variations better than GRU. Consequently, TCN's enhanced class-wise performance and superior generalization make it the most robust choice for human activity recognition in overlapping data settings, meeting the demands for high accuracy and reliability in practical applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{2-11}  & **Train** & **Validation Test** & **Train** & **Validation Test** & **Train** & **Validation Test** & **Train** & **Validation Test** \\ \hline DT & 0.9752 & 0.9671 & 0.9714 & 0.9752 & 0.9671 & 0.9714 & 0.9752 & 0.9671 & 0.9714 & 0.9752 & 0.9671 & 0.9714 \\ \hline KNN & 0.9681 & 0.9725 & 0.9632 &