ArticleSensors (Basel, Switzerland)2021
Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance.Frontiers in digital health · 2025Pooled it
- A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion.Scientific reports · 2025Article
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Review
- A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework.Sensors (Basel, Switzerland) · 2025Article
- A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing.PloS one · 2025Article
- Pseudo-labeling based adaptations of pain domain classifiers.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.Scientific data · 2024Article
- Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study.Sensors (Basel, Switzerland) · 2023Article
- Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition.Sensors (Basel, Switzerland) · 2023Article
- Using the Electrocardiogram for Pain Classification under Emotional Contexts.Sensors (Basel, Switzerland) · 2023Article
- Transformer encoder with multiscale deep learning for pain classification using physiological signals.Frontiers in physiology · 2023Article
- Analysis of Physiological Responses during Pain Induction.Sensors (Basel, Switzerland) · 2022Article
- A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.