## 4 Results and Discussion
### Four-Sensor Configurations
#### 4.4.2 Confusion Matrix Analysis for Four-Sensor Non-Overlapping Configuration  
The confusion matrices in Figure 12 provide additional insights into the classification performance for each model. The confusion matrices provide insightful details about the performance of different models in the four-sensor non-overlapping configuration:  
DT: The DT confusion matrix in Figure 12a reveals that the model accurately classifies 226 instances of _run_wos_ and 220 instances of _stn_wos_. It also shows strong performance in classifying _wlk_w_s, with 127 instances correctly identified. However, there are notable misclassifications, with 36 instances of _stn_w_ incorrectly labeled as _stn_wos_. This misclassification pattern suggests a tendency for the DT model to overfit, especially when dealing with similar stationary activities where sensor readings may overlap [26]. KNN: The KNN confusion matrix in Figure 12b demonstrates strong performance, particularly for the _run_wos_ activity, where 229 instances are correctly classified. However, KNN struggles with distinguishing between _stn_w_s and _stn_wos_, as evidenced by the 33 misclassifications in the _stn_w_s class. This confusion likely arises from KNN's sensitivity to feature space proximity, particularly in high-dimensional data [35]. RF: The RF model in Figure 12c performs well, accurately classifying 228 instances of _run_wos_ and 480 of _stn_wos_. However, it misclassifies 36 instances of _stn_w_s as _stn_wos_, suggesting limitations in distinguishing subtle temporal features in stationary activities [26]. While effective in managing feature complexity, RF is slightly outperformed by deep learning models in capturing nuanced activity differences. SVC: The SVC confusion matrix in Figure 12c highlights SVC's high accuracy, particularly in _stn_wos_, where it correctly classifies 476 instances. However, the model struggles with distinguishing between _stn_w_s and _stn_wos_, with 42 instances of _stn_w_ misclassified as _stn_wos_ and five instances of _stn_wos_ misclassified as _stn_w_s. This confusion could be due to the overlapping nature of sensor readings between these stationary activities [39]. XGBoost: The XGBoost confusion matrix (Figure 12e) shows strong classification of _run_wos_ (226 instances) and _stn_wos_ (481 instances). However, it struggles with subtle distinctions, misclassifying 40 instances of _stn_w_s as _stn_wos_. While XGBoost's ensemble framework captures complex data patterns, distinguishing closely related stationary activities remains challenging without sequential processing, a strength of temporal models like LSTM and TCN [27]. GRU: The GRU model in Figure 12f shows solid performance, particularly in the classification of _run_wos_ with 234 correctly identified instances and _stn_w_s with 211 correct classifications. However, it has minor challenges in distinguishing between _stn_w_s and _stn_wos_, with 31 instances of _stn_w_s misclassified as _stn_wos_. GRU's sequential processingenables it to retain important context over time, aiding in recognizing both stationary and dynamic activities [37]. Figure 12: Cont. LSTM: The LSTM model in Figure 12g shows strong classification performance across various activities. Specifically, it accurately classifies 203 instances of _run_wos_ and 503 instances of _stn_wos_. However, the model struggles slightly with _stn_ws_, where it misclassifies 37 instances as _stn_wos_. This pattern highlights LSTM's ability to capture both short-term and long-term dependencies in time-series data, which enhances its robustness across activities [38]. The model also performs well in distinguishing between the _run_ws_ and _wlk_ws_ classes, achieving high classification accuracy in these categories, further underlining its effectiveness in handling sequential data. TCN: In Figure 12h, the TCN model shows excellent performance in dynamic activities, accurately classifying 234 instances of _run_wos_ and 500 instances of _stn_wos_.