QUT ePrints

Improving Pain Recognition Through Better Utilisation of Temporal Information

Lucey, Patrick J., Howlett, Jessica M., Cohn, Jeffrey , Lucey, Simon, Sridharan, Sridha, & Ambadar, Zara (2008) Improving Pain Recognition Through Better Utilisation of Temporal Information. In Goecke, Roland, Lucey, Patrick J., & Lucey, Simon (Eds.) International Conference on Auditory-Visual Speech Processing, 26-29 September 2008, Tangalooma, Australia.

Abstract

Automatically recognizing pain from video is a very useful application as it has the potential to alert carers to patients that are in discomfort who would otherwise not be able to communicate such emotion (i.e young children, patients in postoperative care etc.). In previous work [1], a “pain-no pain” system was developed which used an AAM-SVM approach to good effect. However, as with any task involving a large amount of video data, there are memory constraints that need to be adhered to and in the previous work this was compressing the temporal signal using K-means clustering in the training phase. In visual speech recognition, it is well known that the dynamics of the signal play a vital role in recognition. As pain recognition is very similar to the task of visual speech recognition (i.e. recognising visual facial actions), it is our belief that compressing the temporal signal reduces the likelihood of accurately recognising pain. In this paper, we show that by compressing the spatial signal instead of the temporal signal, we achieve better pain recognition. Our results show the importance of the temporal signal in recognizing pain, however, we do highlight some problems associated with doing this due to the randomness of a patient's facial actions.

Impact and interest:

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 15248
Item Type: Conference Paper
Additional Information: For more information, please refer to the conference's website (see hypertext link) or contact the author.
Additional URLs:
ISBN: 9780646495033
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2008 (please consult author)
Deposited On: 20 Oct 2008
Last Modified: 29 Feb 2012 23:46

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page