An approach using multiple machine learning algorithms based on sensor modeling for gesture matching and recognition
Peng Li · Advances in Engineering Technology Research · 2025
Gesture recognition is crucial for applications such as character modeling and humanoid robots, yet gesture recognition and generation remain underexplored, with most studies relying on camera-based tracking, which is limited by single-finger accuracy and computational requirements. This study proposes a hybrid framework that combines a haptic controller with machine learning (ML) to capture high-resolution finger motion data. The haptic controller enhances accuracy by providing additional evaluation of finger position and pressure metrics. We evaluated three ML algorithms - Random Forest (RF), Xgboost, and Support Vector Regression (SVR) to model 45 Euler angle-based joint rotations from 20 input parameters for each hand. Our results show that XGBoost outperforms RF and SVR in all sample sizes (500-3000 data points), achieving the lowest angle error (4431.4°) and distance error (19.8 cm) at 3000 samples. This study provides an innovative method for gesture recognition and provides valuable empirical experience for the application and development of related fields.