ArticleSensors (Basel, Switzerland)2019
Exploring Deep Physiological Models for Nociceptive Pain Recognition.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Cross-spectral fusion of thermal and RGB imaging for objective pain estimation.PLOS digital health · 2026Article
- Pain assessment using physiological responses/markers in different types of pain: a scoping review.NPJ digital medicine · 2026Article
- 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
- A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework.Sensors (Basel, Switzerland) · 2025Article
- Dual-stream transformer approach for pain assessment using visual-physiological data modeling.PeerJ. Computer science · 2025Article
- Pseudo-labeling based adaptations of pain domain classifiers.Frontiers in pain research (Lausanne, Switzerland) · 2025Article
- Comparing multimodal physiological responses to social and physical pain in healthy participants.Frontiers in public health · 2024Article
- Article
- Multimodal automatic assessment of acute pain through facial videos and heart rate signals utilizing transformer-based architectures.Frontiers in pain research (Lausanne, Switzerland) · 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
- A systematic review of neurophysiological sensing for the assessment of acute pain.NPJ digital medicine · 2023Review
- Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition.Sensors (Basel, Switzerland) · 2023Article
- Transformer encoder with multiscale deep learning for pain classification using physiological signals.Frontiers in physiology · 2023Article
- Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods.Frontiers in network physiology · 2023Review
- Multimodal physiological sensing for the assessment of acute pain.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
- Personalized Deep Bi-LSTM RNN Based Model for Pain Intensity Classification Using EDA Signal.Sensors (Basel, Switzerland) · 2022Article
- Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Standard feature engineering involves manually designing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features for the subsequent optimisation of an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterised by the integration of feature engineering, feature selection and inference model optimisation into a single learning process. In the following work, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. In contrast to previous works, which rely on carefully designed sets of hand-crafted features, the current work aims at building competitive pain intensity inference models through autonomous feature learning, based on deep neural networks. The assessment of the designed deep learning architectures is based on the
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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.