## 4 Experimental Results and Analysis
### Structured Pruning Analysis
This suggests an architectural characteristic where significant redundancy is initially maintained but effectively eliminated in later stages of pruning. These varied yet consistently positive responses to pruning across all model variants highlight several key insights. The effectiveness of progressive pruning is demonstrated through sustained accuracy improvements across different architectural configurations. The presence of distinct performance jumps at specific pruning ratios suggests the existence of optimal pruning thresholds that vary by architecture. Moreover, the ability to maintain and often improve accuracy through substantial parameter reduction indicates robust feature extraction capabilities in the base architectures. The Widar dataset, focusing on fine-grained gesture recognition, exhibits distinctive pruning characteristics that reveal crucial insights about parameter sensitivity in gesture recognition tasks shown in Figure 8. Detailed analysis demonstrates varied responses to pruning across different architectural variants. Model 1 demonstrates remarkable stability throughout the progressive pruning stages, maintaining performance between 46.88% and 53.08%. This consistent performance tri  
Figure 7: Pruning progression for UT-HAR dataset exhibiting gradual performance enhancement. tory, with minimal fluctuation across pruning stages, suggests robust feature representation capabilities that remain stable even under significant parameter reduction. The peak accuracy of 53.08% at the third pruning stage indicates an optimal balance between parameter efficiency and model expressiveness. Model 2 exhibits a characteristic peak-and-decline pattern, improving from 36.81% to 50.81% at 24% pruning, before declining to 47.05% in the final stage. The clear performance peak at 24% pruning ratio followed by gradual decline suggests a critical threshold in the parameter space, beyond which the model's ability to capture fine-grained gesture features begins to degrade. Model 3 presents a contrasting pattern, showing a steady decline from 38.12% to 36.44% throughout the pruning process. This gradual decrease in accuracy indicates higher sensitivity to parameter reduction, suggesting that this architectural variant relies more heavily on its complete parameter set for gesture discrimination. Model 4 shows a distinctive two-phase behavior, with a significant improvement from 11.40% to 45.84% in the initial pruning stage, followed by stable performance. This pattern indicates successful elimination of detrimental parameters early in the pruning process, leading to more effective feature extraction. These diverse pruning responses in the Widar dataset provide valuable insights into the relationship between architectural complexity and gesture recognition capability. The varying patterns suggest that gesture recognition tasks require careful consideration of pruning thresholds, as excessive parameter reduction can directly impact the model's ability to capture subtle motion patterns. The stability exhibited by Model 1 and the early improvement shown by Model 4 particularly demonstrate that effective pruning can maintain or enhance gesture recognition performance when appropriately calibrated to the architectural characteristics. Table 3 summarizes the overall impact of our compression strategies on different datasets. Specifically, it shows the percentage reduction in parameters (_Param. Red._) and multiply-accumulate operations (_MAC Red._), both of which indicate how effectively the model's computational complexity has been reduced. Additionally, it reports the change in accuracy (_Acc. Change_) after compression, the final achieved accuracy (_Final Acc._), the initial number of parameters (_Init. Params_), and the final number of MAC operations (_Final MACs_). Figure 8: Pruning progression for Widar dataset illustrating stability in fine-grained gesture recognition.