Exécution de notre outil de synthèse sur un nouvel article

Résumé : 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.