Social signal processing for pain monitoring using a hidden conditional random field

Ghasemi, Afsane, Wei, Xinyu, Lucey, Patrick, Sridharan, Sridha, & Fookes, Clinton B. (2014) Social signal processing for pain monitoring using a hidden conditional random field. In Proceedings of the 2014 IEEE Workshop on Statistical Signal Processing (SSP), IEEE, Gold Coast, QLD, pp. 61-64.

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Abstract

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.

Impact and interest:

2 citations in Scopus
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ID Code: 58795
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Biomedical monitoring, Social signal processing, Action units, Pain, Hidden conditional random field
DOI: 10.1109/SSP.2014.6884575
ISBN: 9781479949755
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Deposited On: 12 Nov 2014 01:17
Last Modified: 13 Nov 2014 02:24

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