Rhaman, Md. KhalilurIslam, AbrarAcanto, Orbin AhmedDrishty, Mehzabin IslamZaman, SamontyAhmed, Jaber2024-07-032024-07-0320222022-01ID 17101217ID 17201023ID 18101315ID 19201102ID 19201131http://hdl.handle.net/10361/23650Cataloged from PDF version of thesis.Includes bibliographical references (pages 50-53).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Patients are considered to be one of the most vulnerable persons. When it comes to critical patients their movements and behaviors need to be monitored constantly as simple negligences could result in severe consequences. It is almost impossible to monitor a patient 24/7 without making any slight error. Therefore, this paper will establish a simple but effective solution to this issue by creating a heuristic approach system that can detect a patient's facial expressions and postural movement to calculate the immediate conditions of patients with the assistance of deep learning algorithms. This is a hybrid approach as we have combined Convolutional Neural Network & BlazePose GHUM 3D to create a robust model which in our system can be used for image analysis in order to get precise monitoring results for critical situations by following specific sequences that would not have been possible without the hybrid model.53 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.CNNBlazePose GHUM 3DFacial expressionPosture sequence detectionCritical situation detectionNeural networks (Computer science)Computer algorithmsThesis