The PainMonit Database: An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. The PainMonit Database was created for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. On the one hand, pain was triggered by artificially applied heat stimuli in an experimental study protocol. On the other hand, physiological responses to pain during a physiotherapy session were also recorded.Detailed information on the dataset can be found in the related publication at the following link.

成功开展机器学习研究，离不开海量数据的支撑。然而，诸多应用场景均面临数据供给不足的困境——数据采集往往兼具挑战性与耗时性。自动疼痛识别领域亦存在此类问题：该领域的算法旨在学习疼痛程度与行为或生理反应之间的关联。尽管机器学习模型在优化当前疼痛监测金标准（自我报告）方面已展现出应用潜力，但可供研究人员免费使用的公开数据集却寥寥无几。疼痛监测数据库（PainMonit Database）专为基于生理数据的自动疼痛检测任务构建。该数据集包含两个部分：由于疼痛的感知会因潜在诱因不同而存在差异，其一来自实验研究范式中人工施加热刺激诱发的疼痛数据；其二则采集了物理治疗过程中受试者对疼痛产生的生理反应数据。有关该数据集的详细信息，可通过以下链接查阅相关发表文献获取。