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Related Experiment Video

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

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Published on: April 5, 2019

Two-Stream Attention Network for Pain Recognition from Video Sequences.

Patrick Thiam^1,2, Hans A Kestler¹, Friedhelm Schwenker²

¹Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany.

Sensors (Basel, Switzerland)

|February 9, 2020

Summary

This summary is machine-generated.

This study introduces an attention network model for recognizing pain-related facial expressions. The approach effectively combines spatial and temporal facial data, achieving state-of-the-art performance in pain expression analysis.

Keywords:

convolutional neural networks, long short-term memory recurrent neural networks, information fusion, pain recognition

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

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

Published on: April 14, 2016

Area of Science:

Biomedical Engineering
Computer Vision
Affective Computing

Background:

Pain expression analysis is crucial for patient care.
Existing methods rely on handcrafted features or deep learning models.
There is a need for robust and automated pain expression recognition systems.

Purpose of the Study:

To propose an end-to-end attention network for pain-related facial expression analysis.
To integrate spatial and temporal facial information for improved recognition accuracy.
To evaluate the proposed method against existing state-of-the-art approaches.

Main Methods:

Utilized an attention network combining Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks.
Processed sequences of Motion History Images (MHIs) and Optical Flow Images (OFIs) using separate attention streams.
Employed a weighted aggregation of attention-based outputs for final classification.
Combined spatial and temporal facial dynamics through weighted fusion of MHI and OFI stream classifications.

Main Results:

The proposed attention network achieved classification performance comparable to state-of-the-art methods.
The integrated spatial-temporal approach demonstrated effectiveness in pain expression recognition.
Evaluated on the BioVid Heat Pain Database (Part A) and SenseEmotion Database.

Conclusions:

The attention network approach offers a promising end-to-end solution for pain expression analysis.
Combining spatial and temporal facial features enhances the accuracy of pain recognition.
The method shows potential for real-world clinical applications in pain assessment.