Estimating Ground Reaction Forces from Gait Kinematics in Cerebral Palsy: A Convolutional Neural Network Approach.

Purpose: While gait analysis is essential for assessing neuromotor disorders like cerebral palsy (CP), capturing accurate ground reaction force (GRF) measurements during natural walking presents challenges, particularly due to variations in gait patterns. Previous studies have explored GRF prediction using machine learning, but specific focus on patients with CP is lacking. This research aims to address this gap by predicting GRF using joint angles derived from marker data during gait in patients with CP, thereby suggesting a protocol for gait analysis without the need for force plates.

Methods: The study employed an extensive dataset comprising both typically developed (TD) subjects (n = 132) and patients with CP (n = 622), captured using motion capture systems and force plates. Kinematic data included lower limb angles in three planes of motion, while GRF data encompassed three axes. A one-dimensional convolutional neural network model was designed to extract features from kinematic time series, followed by densely connected layers for GRF prediction. Evaluation metrics included normalized root mean squared error (nRMSE) and Pearson correlation coefficient (PCC).

Results: GRFs of patients with CP were predicted with nRMSE values consistently below 20.13% and PCC scores surpassing 0.84. In the TD group, all GRFs were predicted with higher accuracy, showing nRMSE values lower than 12.65% and PCC scores exceeding 0.94.

Conclusion: The predictions considerably captured the patterns observed in the experimentally obtained GRFs. Despite limitations, including the absence of upper extremity kinematics data and the need for continuous model evolution, the study demonstrates the potential of machine learning in predicting GRFs in patients with CP, albeit with current prediction errors constraining immediate clinical applicability.