Abnormal Gait Classification in Children With Cerebral Palsy Using ConvLSTM Hybrid Model and GAN

Abnormal gait patterns are a common feature of Cerebral Palsy, a neurodevelopmental disease for which early identification is essential for treatment. In the proposed research, a novel methodology is provided for classifying abnormal gait patterns in children with Cerebral Palsy, using gait analysis as a diagnostic tool. To improve gait classification accuracy and efficiency, a hybrid model of Convolutional Long Short-Term Memory (ConvLSTM) model and Generative Adversarial Network (GAN) is used in the suggested technique. The proposed study concentrated on temporal signal data, using hypothetical planes with minimal regard for anatomical indicators. The reduction technique enables a more efficient and successful gait analysis. Heatmap images were created from the selected temporal data. GAN generated images were added to the dataset in order to overcome the problems caused by class imbalance and guarantee a thorough depiction of abnormal gait patterns. In the proposed work, a ConvLSTM-based model with a batch size of 32, training as well as validation datasets were evaluated over a period of 50 epochs. The effectiveness of the suggested model was compared to other models such as Gated Recurrent Unit, Convolutional Neural Network, and Long Short-Term Memory model that were trained using the same input data. Our suggested ConvLSTM model produced an impressive accuracy of 91.8% and a loss of 0.42. The Convolutional Long Short Term Memory model performed better than the other models when compared based on a number of criteria, including accuracy, precision, recall, and F1-score. The performance measures demonstrate how well our method works to classify the abnormal gait in kids with Cerebral Palsy.