# Adaptive Feedback-Driven Segmentation for Continuous Multi-Label Human Activity Recognition
## 3 Methodology  
### Feedback-Driven Adaptive Segmentation  
The proposed feedback-driven segmentation system is designed to optimize both computational efficiency and the performance of HAR. The system operates in two distinct phases, as illustrated in Figure 2:  
* A 20% subset of the dataset is used to minimize computational demands while ensuring a diverse representation of activities is maintained.
* From this subset, three distinct data representations are generated: spectrogram, range-Doppler, and range-time.
* Individual neural networks are trained on each of these representations to capture modality-specific features.
* Segmentation parameters, including segment length (L) and overlap (O), are iteratively optimized using BO and RL separately. This optimization is guided by metrics such as accuracy and F1-score [31]. The overlap is set to one-third (\(\approx\)33%) of the segment length, an optimal value based on [9; 28], ensuring that activities occurring at the edges of one window are fully captured in the overlapping portion of the subsequent window.  
Phase 2: Full-Scale Application and Result Fusion
* Once the optimal segmentation parameters are identified, they are applied across the entire dataset using the modified Resnet18, to ensure consistency and robustness in activity segmentation.
* Classification results from the individual modalities are then fused in a post-processing step. This fusion leverages the strengths of each representation, combining them to produce the final multi-label predictions. This structured approach allows the feedback-driven system to effectively achieve the highest performance.  
This structured approach allows the feedback-driven system to effectively achieve the highest performance. The cornerstone of our methodology is the feedback-driven adaptive segmentation system, which begins by selecting an initial segmentation configuration tailored to the characteristics of the dataset used. Specifically, the initial segment length is determined based on the data stream duration and the average activity duration present in the dataset, while the overlap ratio is set to one-third (approximately 33%) of the segment  
Figure 2: The feedback-driven segmentation workflow.  
length to ensure that activities occurring at segment boundaries are effectively captured. This crucial initial step sets the foundation for the subsequent dynamic adjustments. In the first phase of our methodology, we carry out the following steps:  
* We apply BO and RL separately to dynamically adjust segmentation parameters such as segment length (L) and overlap (O). The effectiveness of these adjustments is then evaluated by comparing the results from both techniques to determine the most effective approach for parameter optimization.
* The adjustments are governed by a reward system, which is crucial in this phase and evaluates performance metrics including accuracy and F1-score. The reward function used for both optimization techniques is defined as follows: \[Reward=\alpha.Accuracy+\beta.F1.Score\] (1) where \(\alpha\) and \(\beta\) are weights determining the relative importance of each metric. In our experiments, these weights were set to \(\alpha\) = 0.25 and \(\beta\) = 0.75 to provide greater emphasis to the F1-score, which balances precision and recall and is essential for addressing nuances in imbalanced classes, while still considering overall accuracy, which provides a measure of overall correctness.