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.

获取海量数据是成功开展机器学习研究的必要前提。然而，由于数据采集往往兼具挑战性与耗时性，诸多应用场景均面临数据供给不足的困境。这一现状同样适用于自动化疼痛识别任务——此类任务旨在通过算法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数据集却寥寥无几。PainMonit数据库便是为利用生理数据开展自动化疼痛检测而专门构建的数据集。该数据集包含两个部分，这是因为疼痛的主观感知会因潜在诱因的不同而存在差异。其一，在一项实验研究范式中，通过人工施加热刺激来诱发疼痛；其二，研究人员同时记录了物理治疗过程中受试者面对疼痛时的生理反应。

搜集汇总

背景与挑战

背景概述

The PainMonit Database是一个包含实验性和临床性生理信号的数据集，专门用于自动疼痛识别研究。数据集通过热刺激实验和物理治疗过程中的生理反应记录，为机器学习模型提供训练数据，以改进现有的疼痛监测方法。