A DEEP NEURAL NETWORK FOR BODY PART-BASED CEREBRAL PALSY PREDICTION IN INFANTS TO DETECT ABNORMAL MOVEMENTS

Cerebral palsy is a neurological disorder that can be diagnosed as early as infancy. Early diagnosis and intervention can significantly improve outcomes for infants with cerebral palsy. In this study, we propose a method for predicting cerebral palsy in infants using pose-based feature sets and velocity information, and compare its performance with a Convolutional Neural Network (CNN) approach.A reliable dataset containing pose-based feature sets and velocity information related to the limbs of infants is collected and pre-processed by handling missing values, normalizing the feature sets and velocity information, and splitting the data into training and testing sets. Relevant features, such as joint orientations, displacements, and velocities in the X and Y directions, are extracted from the pose-based feature sets and velocity information.An MLP algorithm is trained using the preprocessed and normalized feature sets as inputs and the corresponding cerebral palsy diagnosis as the target output. Supervised learning is used to optimize the models parameters and weights. The performance of the trained MLP model is evaluated using various evaluation metrics, and the results show high accuracy in predicting cerebral palsy in infants.Next, a CNN approach is implemented for comparison. The pose-based feature sets and velocity information are used to train the CNN model, which consists of convolutional layers for feature extraction and pooling layers for spatial downsampling. The output from the CNN model is then fed into fully connected layers for classification. The performance of the CNN model is also evaluated using the same evaluation metrics as the MLP model.The results of the comparative analysis show that the MLP model achieves high accuracy in predicting cerebral palsy in infants, outperforming the CNN model. The MLP models accuracy compared to the CNN model. The interpretations of the MLP models predictions are validated using additional validation  datasets or by comparing with existing diagnostic methods for cerebral palsy.