Developing Toileting Detection System for Children with Cerebral Palsy (CP) Based on Body Gestures Using Deep Learning

Children with disabilities often have difficulty expressing their desire to go to the toilet. This can lead to health problems and other problems such as defecating out of place. In the research conducted, a toileting prediction system for children with disabilities was designed based on body gestures using deep learning. This research uses a camera to capture videos of children with disabilities. The camera will be installed in front of the subject during school activities. The data taken from the subject is data in the form of images of landmark points that form a gesture and the results of feature extraction. Each will be processed with different machine learning. The CNN model used is the VGG 16 Model which shows the best performance with 96.3% validation accuracy and 92.3% training accuracy. While the logistic regression model used is a model with a dataset division of 281 training data and 121 testing data with an accuracy of 0.86 and a loss of 0.341. Both machine learning models are able to classify different gestures with different classes. However, there are differences in classification results between CNN and logistic regression on the same data because of the different ways of processing data. CNN recognizes visual patterns in images, whereas logistic regression uses a linear combination of features that may not be as complex as the non-linear approach used by CNN. The toileting detection system based on body gestures with deep learning still has some room for development. One area that can be developed is to find other parameters that can describe signs of wanting to go to the toilet. In addition, it is also to increase the accuracy of the system. Future development plans are to learn more about the subject's habits and disabilities, add more subjects with disabilities, and develop the system into real time