Key result
Machine learning models trained on wearable physiological data achieve ~92% accuracy in detecting experimental pain.
Why the study?
Machine learning research for automated pain recognition is limited because only a handful of physiological datasets are freely accessible to researchers.
Observational (n=104)
Yes
The PainMonit Dataset provides a publicly available, multimodal physiological dataset for automated pain recognition in both experimental and clinical settings.
Advances automated pain recognition research; leaves open clinical translation pending prospective validation.
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.
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Gouverneur et al. (2024) conducted an observational in Pain (experimental and clinical) (n=104). Physiological sensor monitoring during pain induction and therapy vs. Baseline/No pain periods was evaluated on Classification accuracy for automated pain recognition. Machine learning models trained on the multimodal physiological PainMonit Dataset achieved up to 91.93% accuracy in detecting experimental heat pain and 81.00% accuracy in detecting clinical pain.
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