ArticlePloS one2021
Exploration of physiological sensors, features, and machine learning models for pain intensity estimation.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 82 citations in OpenAlex.
- Real-Time Pain Assessment from Electrodermal Activity Using Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- An exploratory study of headache pain intensity using facial expressions and APEX frames.NPJ digital medicine · 2026Article
- Pain assessment using physiological responses/markers in different types of pain: a scoping review.NPJ digital medicine · 2026Article
- InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain Patients.Sensors (Basel, Switzerland) · 2025Article
- Influence of Pre-Existing Pain on the Body's Response to External Pain Stimuli: Experimental Study.JMIR biomedical engineering · 2025Article
- A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion.Scientific reports · 2025Article
- Classifying social and physical pain from multimodal physiological signals using machine learning.Scientific reports · 2025Article
- Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.Scientific reports · 2025Article
- Pseudo-labeling based adaptations of pain domain classifiers.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Dual-stream transformer approach for pain assessment using visual-physiological data modeling.PeerJ. Computer science · 2025Article
- Impact of Demographics and Psychological Factors on Three-Day Postoperative Pain Perception Following Hand Surgery.Journal of clinical medicine · 2024Article
- Exploring the Potential of a Smart Ring to Predict Postoperative Pain Outcomes in Orthopedic Surgery Patients.Sensors (Basel, Switzerland) · 2024Article
- Article
- Analysis of pain research literature through keyword Co-occurrence networks.PLOS digital health · 2023Article
- Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database.Life (Basel, Switzerland) · 2023Article
- Altered habenular connectivity in chronic low back pain: An fMRI and machine learning study.Human brain mapping · 2023Article
- A systematic review of neurophysiological sensing for the assessment of acute pain.NPJ digital medicine · 2023Review
- Classification of Level of Consciousness in a Neurological ICU Using Physiological Data.Neurocritical care · 2023Observational
- Transformer encoder with multiscale deep learning for pain classification using physiological signals.Frontiers in physiology · 2023Article
- Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation.Procedia computer science · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In current clinical settings, typically pain is measured by a patient's self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.