ANOVA and linear regression feature selection for GRU-based foot position prediction in powered prostheses

This study evaluates feature selection using ANOVA and Linear Regression to optimize GRU-based models for predicting foot position in powered prostheses across varied terrains. Kinematic data from ten healthy participants during walking, stair ascend/descend, and standing were processed in MATLAB. Selected features, compared with Recursive Feature Elimination, trained GRU networks on mixed datasets and were tested on independent subjects. Results showed ANOVA and regression efficiently selected features with reduced computation and comparable performance. The GRU achieved RMSE as low as 0.066 radians, demonstrating robust generalization. While promising, clinical validation on amputee subjects remains necessary.