Estimating Gait Kinematics from Muscle Activity Using Deep Learning in Typically Developing Children

Abstract Instrumented gait assessment in pediatric populations is often constrained by the complexity and lack of portability of traditional motion capture systems. In this article, we propose a deep learning approach utilizing a one-dimensional (1D) U-Net architecture to accurately estimate ankle and knee joint angles in the sagittal plane from surface electromyography (sEMG) signals. We analyzed data from the tibialis anterior and medial gastrocnemius of 25 typically developing children (ages 4–16) to evaluate the model’s performance and the influence of age-related gait maturation. The proposed 1D U-Net achieved high predictive accuracy for the ankle joint (RMSE: 3.6°) and the knee joint (RMSE: 4.1∘). Experimental results demonstrated that incorporating the toe-off event as a temporal marker significantly enhanced prediction stability during transitional gait phases. Furthermore, Statistical Parametric Mapping (SPM) was employed to identify systematic errors, which were primarily localized during initial contact and pre-swing but remained below clinically relevant thresholds. The findings reveal that prediction accuracy increases with age, reflecting more stable neuromotor patterns. This study demonstrates that a 1D U-Net can reliably decode lower-limb kinematics from sEMG alone, enabling the development of simplified, non-invasive, and portable pediatric gait assessment tools that can be integrated into the control strategies of assistive devices.