Deep learning model for detection of pain intensity from facial expression

Soar, Jeffrey and Bargshady, Ghazal and Zhou, Xujuan and Whittaker, Frank (2018) Deep learning model for detection of pain intensity from facial expression. In: 16th International Conference on Smart Homes and Health Telematics: Designing a Better Future: Urban Assisted Living (ICOST 2018), 10-12 July 2018, Singapore.

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Abstract

Many people who are suffering from a chronic pain face pe- riods of acute pain and resulting problems during their illness and ade- quate reporting of symptoms is necessary for treatment. Some patients have difficulties in adequately alerting caregivers to their pain or describ- ing the intensity which can impact on effective treatment. Pain and its intensity can be noticeable in ones face. Movements in facial muscles can depict ones current emotional state. Machine learning algorithms can detect pain intensity from facial expressions. The algorithm can ex- tract and classify facial expression of pain among patients. In this paper, we propose a new deep learning model for detection of pain intensity from facial expressions. This automatic pain detection system may help clinicians to detect pain and its intensity in patients and by doing this healthcare organizations may have access to more complete and more regular information of patients regarding their pain.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 24 Apr 2019 05:27
Last Modified: 24 Jun 2019 03:47
Uncontrolled Keywords: deep learning, pain, facial recognition
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/36313

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