Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework

A sentiment analysis system has been proposed in this paper for pain detection using cutting edge techniques in a smart healthcare framework. This proposed system may be eligible for detecting pain sentiments by analyzing facial expressions on the human face. The implementation of the proposed system has been divided into four components. The first component is about detecting the face region from the input image using a tree-structured part model. Statistical and deep learning-based feature analysis has been performed in the second component to extract more valuable and distinctive patterns from the extracted facial region. In the third component, the prediction models based on statistical and deep feature analysis derive scores for the pain intensities (no-pain, low-pain, and high-pain) on the facial region. The scores due to the statistical and deep feature analysis are fused to enhance the performance of the proposed method in the fourth component. We have employed two benchmark facial pain expression databases during experimentation, such as UNBC-McMaster shoulder pain and 2D Face-set database with Pain-expression. The performance concerning these databases has been compared with some existing state-of-the-art methods. These comparisons show the superiority of the proposed system.

Automated summary

In this paper, a sentiment analysis system for pain detection is proposed using cutting edge techniques in a smart healthcare framework. The system analyzes facial expressions to detect pain sentiments and is divided into four components: face region detection, feature analysis, pain intensity prediction, and performance enhancement. Experimental results comparing the system with existing methods using two benchmark facial pain expression databases demonstrate the superiority of the proposed system.