# Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition
## 4 Experimental Setup
### Results and Discussion  
As we explained earlier, we applied the modified ResNet18 to a key dataset subset within our feedback-driven segmentation system. Building on this methodology, we have documented the outcomes for RL and BO in Figures 6 and 7, respectively. These figures showcase the effectiveness of each optimization technique in refining our segmentation parameters and enhancing the overall performance of the system.  
The BO results in Figure 6 demonstrate the relationship between segment length (L) and the objective function value across the range L = 20 to L = 40 s. As we can see, the optimal segment length is identified as L = 38, marked by a red star where the objective function reaches its minimum value of \(-\)0.88. This indicates that L = 38 s optimally enhances the performance. The initial exploration phase (L = 20 to 30 s) shows wide fluctuations, reflecting BO's search for high-reward regions, while the convergence phase (L = 35 to 40 s) stabilizes around L = 38 s. This result highlights BO's capability to navigate complex trade-offs, providing a robust, data-driven approach for optimizing the segmentation of the data.  
Figure 7 depicts the optimization of segment length (L) using RL. The optimal segment length was also identified at L = 38 s and marked by a black dot, which indicates where the RL algorithm determined that no further adjustments were necessary to enhance performance. This decision point represents the effectiveness of RL in systematically exploring and selecting the optimal action. The graph effectively illustrates RL's capability to fine-tune parameters dynamically, ensuring that segmentation is ideally suited for accurate activity recognition.  
Following the confirmation that both BO and RL identified the same optimal segment length, we applied this parameter to the full dataset. The comparative results are presented in Table 1, where we contrast the performance using L = 38 s with L = 30 s, which is the segment length utilized in previous research. The results show a clear enhancement in system performance: accuracy improved from 94.66% to 95.58% and F1-score from 89.90% to 91.68%. These improvements not only highlight the efficacy of our feedback-driven segmentation process in refining the accuracy and efficiency of continuous HAR systems but also underscore the substantial potential of the deep network ResNet18 in HAR. This architecture has proven highly effective in handling the complex demands  
Figure 5: Data collection configuration: five radars arranged in a semicircle for dataset acquisition [32].  
range of features extracted from radar data, while L = 38 s emerged as the segment length where accuracy peaks at 95.58% and F1-score peaks at 91.68%, demonstrating superior classification performance. The primary takeaway is not the segment length itself but the system's capability to pinpoint it based on classification performance. These consistent results across key metrics underscore the precision of our methodology in adapting to the complexities of real-world scenarios, effectively optimizing parameters to ensure optimal performance without being constrained to a predefined length.