System for Supporting Children with Autism Spectrum Disorder Based on Deep Learning Techniques

Deep Learning Techniques aim to develop a web application that displays and also alert the data from electromyography (EMG) for remote analysis and control. EMG is a method for capturing the electrical activity of muscles that has many uses in the medical sector, sports science and other areas. However the process of collecting, storing and analyzing EMG data can be complex and time-consuming particularly when it involves multiple patients or remote locations. To address these challenges, our project leverages biometrics software to convert graphical data from EMG to digital format which can then be stored in the cloud using Deep Learning Techniques and accessed remotely. Deep Learning Techniques provides a user-friendly interface for accessing and analyzing this data enabling healthcare professionals and researchers to make informed decisions based on real-time data. The project involved several key steps including designing and implementing the software architecture which collects EMG data from test subjects, analyzing the data using biometrics software and developing the web application for remote access and analysis by using deep learning techniques.