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

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 performance, reaching an accuracy of 76.43%. These results outperform traditional approaches and offer a valuable tool for implementation in clinical settings. Moreover, significant differences in postural stability were observed among patients under different visual conditions, underscoring the importance of tailoring therapeutic interventions to each patient's specific needs.