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.
Highlights
IN recent years there has been a drive toward more accurate sensing and robust interpretation of activity within exercise and physical rehabilitation systems [1], [2], [3]
Through a discussion of literature in chronic pain (CP) behaviour, we aim to provide an understanding of how CP and CP related emotions are expressed and the role they play in the exacerbation of the condition
We review efforts to automatically recognize pain expression and emotions relevant to chronic lower back pain (CLBP) using the modalities contained in EmoPain
Summary
IN recent years there has been a drive toward more accurate sensing and robust interpretation of activity within exercise and physical rehabilitation systems [1], [2], [3] In part, this has been done to alleviate the high demands placed upon limited numbers of healthcare staff as well as to make rehabilitation more enjoyable (e.g., through the use of games). This has been done to alleviate the high demands placed upon limited numbers of healthcare staff as well as to make rehabilitation more enjoyable (e.g., through the use of games) This has led research and industry to develop systems deployable in non-clinical settings such as the home or workplace, many with the objective of providing corrective biomechanical feedback [2]. It directly affects the efficacy of long term management strategies where a user can become anxious, discouraged and demotivated [4]
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