## 4 Results and Discussion
### Two-Sensor Configurations
#### 4.3.2 Confusion Matrix Analysis for Two-Sensor Non-Overlapping Configuration  
By examining the confusion matrices presented in Figure 10 we can gain deeper insights into how effectively each model classifies different activity categories, revealing specific strengths and weaknesses in handling certain tasks. The confusion matrices provide insightful details about the performance of different models in the two-sensor non-overlapping configuration:DT: As shown in Figure 10a, the DT model demonstrates relatively consistent performance across most activity classes, correctly classifying 221 instances of _wlk_wos_. However, the model struggles with 36 misclassifications of _stn_ws_, suggesting difficulties in distinguishing between subtle differences in stationary and non-stationary activities. This pattern highlights DT's known tendency to overfit, making confident but incorrect predictions, especially in cases with smaller datasets or less distinct class boundaries [26]. KNN: As depicted in Figure 10b, KNN shows sensitivity to closely spaced classes in the feature space, misclassifying 28 instances of _stn_wos_. KNN correctly classifies 222 instances of _wlk_wos_, but its instance-based approach struggles in high-dimensional sensor data without overlap, where the feature space becomes more complex [35]. RF: Figure 10c shows RF's consistent performance, especially in classifying stationary activities like _stn_wos_ (481 correct classifications). However, it misclassifies 40 instances of _stn_ws_, indicating room for improvement in handling subtle distinctions in movement. RF's ensemble learning structure provides stability, which is reflected in its balanced performance across various activity classes [26]. SVC: The confusion matrix in Figure 10d illustrates the strong performance of SVC, with minimal misclassifications. SVC's capacity to maximize margins effectively separates the classes, correctly identifying 228 instances of _run_wos_ and 477 instances of _stn_wos_, confirming its suitability for non-linear data in non-overlapping setups [39]. XGBoost: As seen in Figure 10e, XGBoost excels in distinguishing complex activity patterns, with only a few misclassifications, such as three instances of _run_wos_ and 40 of _stn_ws_. Its gradient-boosting approach enhances its capacity for handling complex data structures, making it a strong contender in the non-overlapping configuration [27]. GRU: The GRU model, shown in Figure 10f, demonstrates efficient classification of sequential activities, correctly classifying 136 instances of _run_wos_ and 229 of _stn_ws_. Although  
Figure 10: Confusion Matrices for models using a two-sensor configuration with non-overlapping data segments: (**a**) DT, (**b**) KNN, (**c**) RF, (**d**) SVC, (**e**) XGBoost, (**f**) GRU, (**g**) LSTM, (**h**) TCN, (**i**) Transformer. it has a few misclassifications in _stn_vos_, GRU's simplicity and lower computational cost make it suitable for real-time ITS applications [37]. LSTM: As indicated in Figure 10g, LSTM misclassifies 31 instances of _stn_ws_, reflecting its limitation in non-overlapping data. However, it still performs well across other activities, showing flexibility in handling sequential data despite the lack of continuity [38]. TCN: As shown in Figure 10h, TCN provides the highest classification accuracy, especially for dynamic activities, with minimal misclassifications. It is particularly effective in correctly classifying activities like _run_vos_ (234 correct classifications) and _wk_vos_ (228 correct classifications). Its convolutional structure allows efficient handling of non-overlapping segments, reinforcing TCN as the top-performing model in this setup [28]. Transformer: The Transformer model, illustrated in Figure 10i, performs well in classifying _stn_vos_ with 502 correct classifications but shows slight limitations with dynamic activities, misclassifying 44 instances of _stn_ws_.