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.
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.
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|Item Type:||Conference Paper|
|Keywords:||Biomedical monitoring, Social signal processing, Action units, Pain, Hidden conditional random field|
|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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