## 4 Experiments
### Discussion  
The experimental findings substantiate the algorithmic merit of the proposed IK-AUG framework across diverse model architectures and training configurations. Notably, inverse kinematics-driven augmentation consistently enhances both convergence stability and generalization capacity particularly in Transformer-based models, which are otherwise susceptible to overfitting and data inefficiency in low-resource regimes. By synthesizing biomechanically constrained yet variationally rich gesture sequences, IK-AUG effectively expands the data manifold while preserving the semantic integrity of motion classes, thereby serving as an implicit regularizer in temporal representation learning.  
Temporal convolutional architectures such as TCN demonstrate robust performance under this augmentation regime, exhibiting efficient convergence and favorable trade-offs between model complexity and recognition accuracy. In contrast, attention-centric models like Sparse Transformer attain high asymptotic accuracy but display greater sensitivity to hyperparameter configurations and training dynamics, suggesting that they are more reliant on augmentation-induced structural priors to capture gesture regularity.  
Despite these improvements, confusion matrix analysis reveals persistent misclassification in semantically adjacent gesture classes. This indicates that further disambiguation may require the integration of complementary modalities, e.g., depth maps, surface cues, or multiview projections, as well as temporal alignment mechanisms that better capture inter-frame dependencies and gesture onset-offset transitions.  
In addition, we recognize that the current study is constrained by the limited scale of the dataset, which includes 10 gesture classes recorded in a controlled environment. This limitation may restrict the generalization of the observed findings to more diverse, real-world signing scenarios. We plan to address this by scaling up the dataset in future work, incorporating a broader vocabulary, more signer diversity, and varied contextual conditions. Such efforts will enable a more comprehensive evaluation of the framework's robustness and applicability.  
Although IK-AUG integrates multiple perturbation modules namely spatial jitter, depth modulation, and solver sensitivity control these components are intrinsically coupled within a unified simulation loop that operates on inverse kinematics-reconstructed trajectories. As such, removing individual components would compromise the biomechanical plausibility or semantic consistency of the synthesized gestures. In future work, we plan to develop a modular variant of the pipeline to facilitate interpretable component-wise evaluation and principled ablation analysis.  
Overall, the results validate IK-AUG not merely as a data expansion tool, but as a principled algorithmic mechanism for generating anatomically faithful, semantically grounded, and training-efficient gesture corpora. These insights underscore the importance of geometry-aware augmentation in deep sequence modeling, and lay a foundation for hybrid simulation-sensor pipelines in future sign language understanding systems.