Affiliations: [a] Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India | [b] Department of Computer Science and Engineering, Aliah University, Kolkata, India | [c] Department of Information Technology, Jadavpur University, Kolkata, India | [d] Department of Computer Science and Engineering, National Institute of Technology, Srinagar, J & K, India
Correspondence:
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Corresponding author: Saiyed Umer, Department of Computer Science and Engineering, Aliah University, Kolkata, India. E-mail: [email protected].
Abstract: BACKGROUND: Patient sentiment analysis aids in identifying issue areas, timely remediation, and improved patient care by the healthcare professional. The relationship between pain management and patient sentiment analysis is crucial to providing patients with high-quality medical care. Therefore, a self-reported pain level assessment is required for the smart healthcare framework to determine the best course of treatment. OBJECTIVE: An efficient method for a pain sentiment recognition system has been proposed based on the analysis of human facial emotion patterns of patients in the smart healthcare framework. METHODS: The proposed system has been implemented in four phases: (i) in the first phase, the facial regions of the observation patient have been detected using the computer vision-based face detection technique; (ii) in the second phase, the extracted facial regions are analyzed using deep learning based feature representation techniques to extract discriminant and crucial facial features to analyze the level of pain emotion of patient; (iii) the level of pain emotions belongs from macro to micro facial expressions, so, some advanced feature tunning and representation techniques are built along with deep learning based features such as to distinguish low to high pain emotions among the patients in the third phase of the implementation, (iv) finally, the performance of the proposed system is enhanced using the score fusion techniques applied on the obtained deep pain recognition models for the smart healthcare framework. RESULTS: The performance of the proposed system has been tested using two standard facial pain benchmark databases, the UNBC-McMaster shoulder pain expression archive dataset and the BioVid Heat Pain Dataset, and the results are compared with some existing state-of-the-art methods employed in this research area. CONCLUSIONS: From extensive experiments and comparative studies, it has been concluded that the proposed pain sentiment recognition system performs remarkably well compared to the other pain recognition systems for the smart healthcare framework.
