ML-Based Application for Enhanced Communication with Specially Abled Children

This research paper presents a hand gesture recognition mobile application designed to assist specially abled children. The application aims to help children with limited mobility to interact with mobile devices using hand gestures as an alternative to touch or keyboard input. The application uses machine learning algorithms to recognize different hand gestures and translate them into specific actions or commands. The research study involved the development and testing of the application with a group of children with various physical limitations. We aim to assist children facing difficulties in their daily lives due to conditions like cerebral palsy and Down syndrome. The results show that the application has the potential to improve the interaction experience for children with physical limitations and enhance their ability to communicate with the world around them. The research paper discusses the application's design, development process, and testing methodology, and concludes with recommendations for future improvements and possible extensions to the application's functionality.