Improving Identification Accuracy and Feature Analysis in Individual Identification Using Center of Gravity Sway

We have improved personal identification accuracy using dynamic center of pressure (COP) data during stepping on and stepping off the balance board. Wii Balance Board data was resampled at 50Hz, log-transformed, smoothed (Savitzky-Golay), and classified into five segments (S1-S5) based on X-axis acceleration. To reduce variability, only stabilization (S2), standing (S3), and pre-fall (S4) were analyzed. In 10-subject experiments using 36 features (including mean, standard deviation, and skewness of position and velocity), SVM (>99%) and random forest (>98%) outperformed XGBoost (∼95%). SHAP analysis confirmed lateral dynamic characteristics in S2 and S4 contribute to classification, demonstrating high accuracy and interpretability.