## 5 Conclusions  
This work introduces IK-AUG, a principled augmentation framework that unifies inverse kinematics, simulation-based gesture modeling, and parametric perturbation into a coherent algorithmic process for sign language recognition. By transforming sparse keypoint trajectories into anatomically valid motion sequences within a controllable virtual environment, IK-AUG addresses key limitations of existing gesture datasets in scalability, structural diversity, and biomechanical fidelity.  
Through the integration of 3D pose estimation, forward-backward IK solvers, and multi-modal augmentation operators parameterized by spatial, depth, and kinematic factors, the framework yields gesture variants that span a rich data manifold whilepreserving semantic consistency. Experimental evaluations across multiple deep temporal architectures validate the efficacy of IK-AUG in enhancing classification accuracy, reducing overfitting, and improving class-level discriminability particularly for high-capacity attention-based models such as Transformers. Moreover, the augmentation benefits persist across varied training budgets, confirming the robustness and transferability of the synthetic-real gesture loop.  
Beyond empirical gains, this research contributes a generalized computational abstraction of gesture synthesis, whereby virtual motion priors can be encoded, perturbed, and operationalized for model training. The framework thus serves not merely as a data augmentation technique, but as a reusable generative substrate for simulation-driven learning paradigms.  
Looking ahead, the integration of multi-sensor glove streams featuring inertial, haptic, and flexion feedback offers a compelling pathway toward hybrid supervision regimes, enabling the joint modeling of physical and virtual motion distributions. Additionally, advancing cross-domain adaptation techniques may allow IK-AUG-generated sequences to serve as pretraining anchors for real-world SLR, thereby narrowing the sim-to-real gap through structured algorithmic alignment.  
While the IK-AUG framework demonstrates consistent improvements across diverse model architectures, it is important to acknowledge the current study's limitations. The experimental dataset comprises 10 gesture classes collected under constrained conditions, which may limit the generalizability of the findings to broader, real-world scenarios. To address this, future work will focus on scaling up the dataset to include a more extensive vocabulary, diverse signers, and varied environmental contexts. We also plan to evaluate the framework on large-scale benchmarks such as RWTH-PHOENIX-Weather and How2 Sign, and explore the integration of multimodal inputs to support hybrid simulation-sensor learning pipelines.  
In total, IK-AUG exemplifies the algorithmic fusion of kinematic reasoning, generative simulation, and data-driven learning laying the foundation for a new class of geometry-aware, sensor-informed learning frameworks situated at the intersection of embodied AI and multimodal human-computer interaction.  
Conceptualization, X.L.; methodology, B.W.; software, B.W.; validation, B.W., X.L. and L.J.; formal analysis, B.W.; investigation, B.W.; resources, L.J.; data curation, B.W.; writing--original draft preparation, B.W.; writing--review and editing, B.W.; visualization, B.W.; supervision, X.L.; project administration, X.L.; funding acquisition, L.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JKA and its promotion funds from KEIRIN RACE.  
Not applicable.  
The datasets presented in this article are not readily available because they are part of an ongoing study and currently being used for follow-up experiments. Requests to access the datasets should be directed to the corresponding author's project page.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.