Applying neuro-fuzzy modeling to evaluate and enhance badminton footwork training

To design a coach-interpretable movement quality score for badminton footwork training using an adaptive neuro-fuzzy inference system. We derived compact biomechanical descriptors of timing, kinematics, dynamics, and stability from benchmark motion recordings and trained a first order Takagi Sugeno ANFIS with Gaussian membership functions and hybrid learning using a 70 15 15 train validation test split with early stopping. The model achieved RMSE 0.074, MAE 0.058, and R2 0.91 on the test set and outperformed linear regression, multilayer perceptron, and support vector regression while preserving transparent fuzzy rules. ANFIS provides accurate and explainable quality estimation. Future work should validate on in-court badminton datasets, expand features for foot contact and center of mass, and deploy lightweight wearable inference for practical real-time feedback.