Convolutional Neural Network Based Health Monitoring System with Thermal Scanning Temperature Detection

This research took place at the Lyceum of the Philippines Cavite Campus and aimed to create a Health Monitoring System for the LPU-Cavite community. The system utilized Face Recognition based on the Convolutional Neural Network (CNN) Algorithm along with Thermal Scanning Temperature Detection. By employing a camera for facial recognition, it determined whether the individual was a student or an employee. Additionally, a thermal scanner prototype equipped with an MLX90614 IR temperature sensor was used to detect a person’s temperature. The screening data could be accessed by the admin and clinic staff through a website. The research employed a Generic Software Development Life Cycle (SDLC) as its methodology, encompassing various stages including Planning, Analysis, Design, Development, Testing, Integration, and Maintenance. The evaluation of the developed system involved 27 participants who completed survey questionnaires based on ISO/IEC 25010 criteria. The system received a positive assessment, with respondents strongly agreeing with its overall performance. The system overall evaluation is 3.9447 wherein the equivalent remark is “Strongly Agree”. This result demonstrates that the system effectively achieves its intended function and objectives. It is recommended to incorporate a graphics processing unit to enhance the system’s speed and performance. Additionally, prioritizing user-friendliness in the software design will lead to an improved overall user experience.