WaveGaitNet: A Wavelet-Based Framework for Gait Kinematics Prediction From Compressed Motion Features

Abstract

Human walking and joint-angle trajectories are complex and highly individual-specific. The study presents a compact wavelet-domain approach that predicts joint movement from body measurements and reconstructs full trajectories using an inverse wavelet transform. Lower-limb joint signals, including those from the hip, knee, and ankle, are sampled at 1,000 frames per stride and processed into a 15-coefficient format using a discrete wavelet transform (DWT). A feedforward neural network connects body measurements to these coefficients. The predicted coefficients are converted back using inverse DWT (IDWT) to generate 1,000 joint-angle trajectories per frame. The proposed framework was evaluated on a publicly available motion capture dataset comprising 138 non-disabled adults across the lifespan. Then, the mean squared error (MSE) and Pearson correlation coefficient were evaluated between the original and reconstructed signals. The proposed WaveGaitNet framework achieves $R^{2}$ values of 0.97, 0.96, and 0.88 for hip, knee, and ankle joints, respectively, with corresponding RMSE values of 3.01°, 4.85°, and 2.67°. This demonstrates improved reliability in reconstructing individual joint-angle trajectories using only basic body measurements. Note to Practitioners—This study introduces a wavelet-based framework for predicting personalised lower-limb joint paths using basic body measurements. The method is especially useful for developers of gait-driven applications, such as rehabilitation robots, lower-limb exoskeletons, and assistive walking devices, where collecting motion capture data can be difficult. By compressing joint-angle signals into a compact wavelet representation and reconstructing full paths via an inverse wavelet transform, the proposed method reduces data size while preserving key gait features. While it shows better reconstruction accuracy for hip and knee joints, caution is needed when applying the method to joints with rapid motion, such as the ankle, especially in new walking conditions.