## 4 Results and Discussion
### Two-Sensor Configurations
#### 4.3.4 Confusion Matrix Analysis for Two-Sensor 50%-Overlapping Configuration
The model's reliance on proximity for classification can lead to misclassifications, particularly when different activity classes have similar features or when noise is present in the data. RF: Figure 11c shows that the RF model effectively handles the majority of classes, particularly _stn_wos_, with 956 correctly classified instances. However, it faces some difficulty with closely related classes, such as _wkl_wos_, where it misclassifies seven instances. This performance indicates that while ensemble learning provides robustness, RF may still struggle with nuanced distinctions in overlapping configurations [26]. SVC: Figure 11d demonstrates the robustness of the SVC model. It correctly classifies 951 instances of _stn_wos_ and 455 instances of _run_wos_, with only nine and one misclassifications, respectively. The SVC's performance here highlights its ability to manage overlapping data effectively, particularly in scenarios with non-linear decision boundaries [39]. Its effectiveness in capturing the relevant features of the data allows it to achieve high accuracy, even in the presence of overlapping segments. Additionally, SVC's capacity to use kernel functions can help in transforming the feature space to better separate classes, further enhancing its classification accuracy. XGBoost: As shown in Figure 11e, XGBoost achieves strong classification performance, particularly in _stn_wos_, with 958 correct classifications. However, it encounters challenges in distinguishing _wkl_wos_, misclassifying only one instance as _wkl_wos_, which may result from the complexity of interactions in overlapping sequences. This outcome underlines the effectiveness of boosting in refining predictions across diverse activity classes [27]. GRU: Figure 11f reveals GRU's strengths in handling temporal data with overlapping segments, as it achieves 446 correct classifications for _run_wos_ and 459 for _stn_wos_. Despite its sequential learning ability, GRU misclassifies 69 instances of _stn_wos_, and it may still benefit from additional temporal smoothing in stationary categories [37]. LSTM: As shown in Figure 11g, the LSTM model effectively classifies both stationary and dynamic activities, achieving 445 correct classifications for _run_wos_ and 923 for _stn_wos_. However, it struggles slightly with _stn_wos_, misclassifying 70 instances. This performance highlights LSTM's strength in capturing long-term dependencies, leveraging overlapping temporal data to improve differentiation between similar activities. The model's architecture enables it to retain past information, making it well-suited for complex activity recognition tasks, especially where temporal overlap enhances classification accuracy [38]. TCN: Similarly, Figure 11h illustrates the high performance of the TCN, which correctly classifies 448 instances of _run_wos_ and 967 instances of _stn_wos_, demonstrating comparable performance to LSTM. TCN benefits from its ability to model both short-term and long-term dependencies through its convolutional architecture, which allows it to process overlapping data more efficiently by capturing spatial and temporal relationships within the data [28]. TCN shows particular strength in activities such as _run_wos_, correctly classifying 224 instances. However, like LSTM, it misclassifies instances of _stn_wos_, suggesting that even with overlapping data, stationary activities remain a challenge for both deep learning models. Transformer: Figure 11i demonstrates the Transformer's strong performance, correctly classifying 451 instances of _run_wos_ and 930 of _stn_wos_. However, the model misclassifies 73 instances of _stn_wos_, suggesting that while its self-attention mechanism captures long-range dependencies effectively, it faces challenges distinguishing closely related stationary activities. This indicates that, despite its proficiency with sequential patterns, the Transformer may benefit from further tuning for overlapping configurations in stationary states [29].