ABSTRACT
At present, in every corner of the world, including developing and developed, countries got affected by infectious diseases such as the COVID-19 virus. Our objective was to create a real-time pain detection for everyone that can use it by themselves before going to the hospital. In this research, we used a dataset from the University of Northern British Columbia (UNBC) and the Japanese Female Facial Expression (JAFFE) as a training set. Furthermore, we used unseen data from webcam or video as a testing set. In our system, pain is divided into three categories: mild, moderate-to-severe-to-painful, and severe. The system’s efficiency was assessed by contrasting its results with those of a highly qualified physician. Classification accuracy rates were 96.71, 92.16, and 98.40% for the not hurting, getting uncomfortable, and painful categories. To summarize, our research has created a simple, cost-effective, and readily understood alternate method for the general public and healthcare professionals to screen for pain before admission. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.