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.

中文翻译：

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慢性疼痛相关表达的自动检测：要求、挑战和多模式 EmoPain 数据集

与疼痛相关的情绪是慢性疼痛有效自我康复的主要障碍。能够检测这些情绪的自动辅导系统是一个潜在的解决方案。本文通过做出三个贡献为此类系统的开发奠定了基础。首先，通过文献综述，概述了慢性疼痛中疼痛的表达方式以及在身体康复中检测疼痛的动机。其次，提供了一个完全标记的多模态数据集（名为“EmoPain”），其中包含高分辨率多视图面部视频、头戴式和室内音频信号、全身 3D 动作捕捉和来自背部肌肉的肌电信号。患有慢性疼痛的人进行体育锻炼时，会产生与疼痛相关的自然不受约束的面部表情和身体运动行为。有指导和无指导的练习都被认为反映了物理治疗师指导治疗和家庭自我指导治疗的传统场景。分配了两组标签：由八位评估者注释的面部表情的疼痛程度和由四位专家划分的六种与疼痛相关的身体行为的发生情况。第三，通过基于数据的探索性实验，描述了自动识别此类表达和行为的因素和挑战，论文最后讨论了这些发现的潜在途径，并强调了所讨论的两种练习场景的差异。