Paper Info
Title
The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset.
Abstract
Pain-related emotions are a major barrier to effective self rehabilitation in chronic pain. Automated coaching systems capable of detecting these emotions are a potential solution. This paper lays the foundation for the development of such systems by making three contributions. First, through literature reviews, an overview of how pain is expressed in chronic pain and the motivation for detecting it in physical rehabilitation is provided. Second, a fully labelled multimodal dataset (named ‘EmoPain’) containing high resolution multiple-view face videos, head mounted and room audio signals, full body 3D motion capture and electromyographic signals from back muscles is supplied. Natural unconstrained pain related facial expressions and body movement behaviours were elicited from people with chronic pain carrying out physical exercises. Both instructed and non-instructed exercises were considered to reflect traditional scenarios of physiotherapist directed therapy and home-based self-directed therapy. Two sets of labels were assigned: level of pain from facial expressions annotated by eight raters and the occurrence of six pain-related body behaviours segmented by four experts. Third, through exploratory experiments grounded in the data, the factors and challenges in the automated recognition of such expressions and behaviour are described, the paper concludes by discussing potential avenues in the context of these findings also highlighting differences for the two exercise scenarios addressed.
Year
DOI
Venue
2016
10.1109/TAFFC.2015.2462830
IEEE Trans. Affective Computing
Keywords
DocType
Volume
Chronic low back pain, emotion, pain behaviour, body movement, facial expression, surface electromyography, motion capture, automatic emotion recognition, multimodal database
Journal
7
Issue
ISSN
Citations 
4
1949-3045
16
PageRank 
References 
Authors
0.73
42
18
Name
Order
Citations
PageRank
Hane Aung1896.81
Sebastian Kaltwang21416.03
Bernardino Romera-Paredes373027.90
Brais Martínez41388.23
Aneesha Singh510011.76
Matteo Cella6160.73
Michel F. Valstar73116140.79
Hongying Meng883269.39
Andrew Kemp9160.73
Moshen Shafizadeh10160.73
Aaron C. Elkins111089.18
Natalie Kanakam12512.74
Amschel de Rothschild13160.73
Nick Tyler14161.07
Paul Watson15271.44
Amanda Williams1612611.24
Maja Pantic1710434487.02
Nadia Bianchi-Berthouze18123998.61