Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person’s abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately
${76}.{6}\%$
in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a
${5}\%$
improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen’s Kappa (
$\kappa _{\textit {lw}} = {0}.{733}$
). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at
${0}.{95}$
, we attain an impressive estimation accuracy of
${88}\%$
. Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.