## 4 Experimental Results and Analysis
### Structured Pruning Analysis
Model 2 shows the most dramatic relative improvement, starting at 8.16% and reaching 42.18%, demonstrating  
Figure 5: Pruning progression for NTU-Fi dataset demonstrating remarkable resilience to parameter reduction. successful optimization even from a challenging baseline. Model 3 achieves the highest absolute improvement, with accuracy increasing from 18.37% to 55.10%, indicating particularly effective parameter refinement for capturing individual gait characteristics. Similarly, Model 4 shows consistent improvement from 14.29% to 43.54%, aligning with the overall pattern. The uniformity in improvement patterns across all models (improvement ranging from +29.25% to +36.73%) provides compelling evidence for the effectiveness of our pruning strategy in the context of human identification tasks. This consistency is particularly noteworthy given the challenging nature of human identification from WiFi signals, where subtle individual characteristics must be preserved for accurate classification. The uniformity in improvement patterns across all models (\(\Delta\) ranging from +29.25% to +36.73%) provides compelling evidence for the effectiveness of our pruning strategy in the context of human identification tasks. This consistency is particularly noteworthy given the challenging nature of human identification from WiFi signals, where subtle individual characteristics must be preserved for accurate classification. Several key observations emerge from this analysis. First, all models demonstrate substantial accuracy improvements despite significant parameter reduction, with a remarkably consistent range of enhancement. Second, the improvements appear largely independent of initial accuracy levels, suggesting the effectiveness of robust pruning across varying architectural configurations. Third, the consistent positive impact of pruning indicates successful preservation of critical features necessary for distinguishing individual characteristics while eliminating redundant parameters. This systematic improvement pattern strongly suggests that our pruning methodology effectively optimizes the parameter space for human identification tasks, successfully retaining and potentially enhancing the network's ability to capture subtle discriminative features in WiFi signal patterns. The UT-HAR dataset demonstrates sophisticated pruning dynamics with distinct behavioral patterns across different architectural variants shown in Figure 7. Detailed analysis reveals compelling evidence of systematic performance improvements through multiple pruning stages. Figure 6: Pruning progression for NTU-Fi-HumanID dataset showing consistent accuracy improvements. Model 1 exhibits a progressive improvement trajectory, with accuracy increasing from 24.30% to 69.88%. The pruning progression reveals particularly notable improvements at 24% pruning (reaching 54.92%) and 42% pruning (reaching 65.96%), suggesting critical threshold points where redundant parameters are effectively eliminated, leading to enhanced feature representation. Model 2 demonstrates steady performance enhancement, improving from 24.30% to 70.88% at 42% pruning ratio, though showing a slight decline to 68.57% in the final stage. This pattern suggests an optimal pruning threshold around 42%, beyond which the model begins to lose some discriminative capacity. Model 3 presents the most robust and consistent improvement pattern, achieving significant progress from 29.42% to 77.41%. This steady enhancement throughout the pruning process demonstrates the effectiveness of gradual parameter reduction in maintaining and improving feature extraction capabilities, suggesting a particularly well-balanced architecture that responds positively to progressive pruning. Model 4 shows an interesting delayed improvement pattern, starting from 29.42% with modest initial gains but achieving substantial improvements in later stages, ultimately reaching 68.17%.