Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

918
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
918
Analgesia and Pain Management01:25

Analgesia and Pain Management

786
Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
786

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multi-Head Attention-Based Framework with Residual Network for Human Action Recognition.

Sensors (Basel, Switzerland)·2025
Same author

Mobile Robot Navigation with Enhanced 2D Mapping and Multi-Sensor Fusion.

Sensors (Basel, Switzerland)·2025
Same author

Experimental economics for machine learning-a methodological contribution on lie detection.

PloS one·2024
Same author

Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database.

Life (Basel, Switzerland)·2023
Same author

Assessing the Value of Multimodal Interfaces: A Study on Human-Machine Interaction in Weld Inspection Workstations.

Sensors (Basel, Switzerland)·2023
Same author

Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Sep 5, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

10.9K

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and

Ehsan Othman1, Philipp Werner1, Frerk Saxen1

  • 1Department of Neuro-Information Technology, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany.

Sensors (Basel, Switzerland)
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system for continuous pain monitoring using facial expressions, audio, and physiological signals. Fused modalities significantly improved pain assessment accuracy over single methods, with regression outperforming classification on imbalanced datasets.

Keywords:
continuous pain intensity monitoringelectrocardiogramelectrodermal activityelectromyogramfacial expressionsfused modalitieslong short-term memory networkrandom forestsample weighting

More Related Videos

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
09:38

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

12.8K
Author Spotlight: Quantifying Pain Experience &#8211; An Illustrative Approach Using the Pain Body Diagram
09:00

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram

Published on: July 7, 2023

3.7K

Related Experiment Videos

Last Updated: Sep 5, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

10.9K
Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
09:38

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

12.8K
Author Spotlight: Quantifying Pain Experience &#8211; An Illustrative Approach Using the Pain Body Diagram
09:00

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram

Published on: July 7, 2023

3.7K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Effective pain management is crucial for patient quality of life, but current clinical methods are subjective and lack continuous monitoring capabilities.
  • Automatic pain assessment using behavioral cues and physiological signals offers a more objective and robust approach to pain monitoring.
  • Existing automatic pain assessment systems face challenges with data imbalance and require further validation.

Purpose of the Study:

  • To develop and evaluate a reliable automatic system for continuous pain intensity monitoring.
  • To analyze the effectiveness of various modalities (facial expressions, audio, ECG, EMG, EDA) individually and in fusion for pain assessment.
  • To compare classification and regression approaches, and assess the impact of sample weighting on imbalanced datasets.

Main Methods:

  • Utilized the X-ITE Pain Dataset comprising facial expressions, audio, electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) signals.
  • Implemented Random Forest (RF), Long Short-Term Memory (LSTM), and a sample-weighted LSTM (LSTM-SW) for uni-modality and multi-modality experiments.
  • Conducted experiments using 11 imbalanced datasets, applying regression and classification tasks, with and without sample weighting for improved performance.

Main Results:

  • Regression-based pain assessment demonstrated superior performance compared to classification on imbalanced datasets.
  • Electrodermal activity (EDA) emerged as the most effective single modality for pain intensity monitoring.
  • Fusion of multiple modalities (Bi-modality and Multi-modality) significantly enhanced assessment performance over uni-modality in 10 out of 11 datasets.

Conclusions:

  • The proposed automatic system effectively monitors pain intensity by integrating diverse data sources.
  • Multi-modal fusion strategies significantly improve the accuracy and robustness of automatic pain assessment systems.
  • The findings highlight the potential of objective, continuous pain monitoring for better patient care and management.