Sehyeok Yoo  Kyungmin Kim  Youngho Chai

10.5626/JOK.2026.53.3.239

Gesture Classification HCI gesture interfaces three-state finger flexion classification VR interaction

Hand-based interaction in virtual reality (VR) is intuitive, but vision-based hand tracking often struggles to accurately capture user intent due to issues like occlusion, lighting variations, and tracking noise. To improve the stability of existing binary (straight/bent) classification and reduce the cognitive load of multi-level schemes, this study introduces a rule-based gesture recognition framework that categorizes finger flexion into three states: straight, intermediate, and bent. A multi-view webcam setup, combined with exponential moving-average filtering, was implemented to enhance robustness against occlusion and jitter. User evaluations across three VR scenarios showed high recognition accuracy and controllability, regardless of variations in hand size or morphology. However, gestures involving unfamiliar hand shapes highlighted areas for usability improvement. These results suggest that a practical, extensible, and reliable VR gesture input system can be developed without relying on complex machine-learning models, indicating potential for broader application and future enhancements.