Kinematic Data Augmentation Using a Spatiotemporal Dual-Discriminator GAN (SDGAN) for Joint Angle Prediction of Infant Crawling

Accurately generating joint motion trajectories for infant crawling is crucial for developing effective control strategies for exoskeletons. However, the limited availability of infant crawling data, owing to the high costs of data collection and privacy concerns, presents a challenge to the performance of such models and controllers. This study introduces a novel spatiotemporal dual-discriminator generative adversarial network (SDGAN) to generate synthetic kinematic data for infant crawling. The generator produces 3D joint coordinate sequences (101 time steps × 36 dimensions for 12 joints) by learning both the spatial joint relationships (via a spatial discriminator) and the temporal dynamics (via a temporal discriminator). To evaluate the model's effectiveness, the SDGAN was compared with existing benchmark models—the TimeGAN, DAT-GAN, and MTS-GAN. Additionally, we assessed the impact of varying synthetic-to-real data mixing ratios (0:1, 1:2, 1:1, 3:2, 2:1, 5:2, and 3:1) on the accuracy of joint angle predictions using a long short-term memory (LSTM) network. The results suggest that the SDGAN is a promising approach for addressing the challenge of limited infant crawling data and improving joint angle prediction accuracy in rehabilitation applications.