ArticleFrontiers in pain research (Lausanne, Switzerland)2023
Multimodal physiological sensing for the assessment of acute pain.
Article in Frontiers in pain research (Lausanne, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 48 citations in OpenAlex.
- Automated Vision-Based Sensing of Paediatric Pain Using 2D-Facial Landmark Trajectories Derived from 3D Geometric Normalisation and a Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) Network.Sensors (Basel, Switzerland) · 2026Article
- Real-Time Pain Assessment from Electrodermal Activity Using Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Connectivity-Based Pain Recognition from fNIRS: Parsimonious Subject-Independent Classification.Sensors (Basel, Switzerland) · 2026Article
- Bioengineering Innovations for Personalized Care in Low Back Pain: From Sensors to Smart Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Deep Learning and Noninvasive Sensors for Detecting Physiological Dysregulation: A Scoping Review.Journal of medical systems · 2026Review
- Pain assessment using physiological responses/markers in different types of pain: a scoping review.NPJ digital medicine · 2026Article
- The Lived Experience of Pediatric Pain Management: Insights From Families and Providers.Journal of patient experience · 2026Article
- Zero-shot multimodal pain estimation via synthetic pain simulation and domain-invariant learning.Frontiers in artificial intelligence · 2026Article
- Article
- Wearable IoT-Enabled Galvanic Skin Response Device for Objective Pain and Stress Monitoring: Hardware Design and Prototype Development.Sensors (Basel, Switzerland) · 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
- Objective Measurement of Musculoskeletal Pain: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- The Investigation of the Relationship Between Individual Pain Perception, Brain Electrical Activity, and Facial Expression Based on Combined EEG and Facial EMG Analysis.Journal of pain research · 2025Article
- Dual-stream transformer approach for pain assessment using visual-physiological data modeling.PeerJ. Computer science · 2025Article
- Adaptive thresholding increases sensitivity to detect changes in the rate of skin conductance responses to psychologically arousing stimuli in both laboratory and ambulatory settings.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2024Article
- Multimodal automatic assessment of acute pain through facial videos and heart rate signals utilizing transformer-based architectures.Frontiers in pain research (Lausanne, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients' self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions,
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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.