Deep learning-based classification of hemiplegia and diplegia in cerebral palsy using postural control analysis.

ABSTRACT: Cerebral palsy (CP) is a neurological condition that affects mobility and motor control, presenting significant challenges for accurate diagnosis, particularly in cases of hemiplegia and diplegia. This study proposes a method of classification utilizing Recurrent Neural Networks (RNNs) to analyze time series force data obtained via an AMTI platform. The proposed research focuses on optimizing these models through advanced techniques such as automatic parameter optimization and data augmentation, improving the accuracy and reliability in classifying these conditions. The results demonstrate the effectiveness of the proposed models in capturing complex temporal dynamics, with the Bidirectional Gated Recurrent Unit (BiGRU) and Long Short-Term Memory (LSTM) model achieving the highest performa

SUBMITTER: Arias Valdivia JT 

PROVIDER: S-EPMC11909225 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature