Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland.
Sci Data. 2024 Sep 27;11(1):1051. doi: 10.1038/s41597-024-03878-w.
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. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.
大量数据的获取对于成功的机器学习研究至关重要。然而，对于许多应用来说，数据采集往往具有挑战性且耗时，因此数据不足。自动化疼痛识别也是如此，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改善当前疼痛监测的金标准（自我报告）方面显示出了前景，但只有少数数据集可供研究人员自由访问。本文提出了使用生理数据进行自动化疼痛检测的 PainMonit 数据集。该数据集由两部分组成，因为疼痛的感知可能因潜在原因而异。（1）在一项实验研究中，通过热刺激引发疼痛，在此期间，从 55 名健康受试者记录了 9 种生理传感器模式（BVP、2×EDA、皮肤温度、ECG、EMG、IBI、HR、呼吸）。（2）从 49 名参与者记录了 8 种模式（2×BVP、2×EDA、EMG、皮肤温度、呼吸、握力），以评估他们在物理治疗期间的疼痛。
