ArticleSensors (Basel, Switzerland)2020
Two-Stream Attention Network for Pain Recognition from Video Sequences.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Zero-shot multimodal pain estimation via synthetic pain simulation and domain-invariant learning.Frontiers in artificial intelligence · 2026Article
- Dual-stream transformer approach for pain assessment using visual-physiological data modeling.PeerJ. Computer science · 2025Article
- Multimodal automatic assessment of acute pain through facial videos and heart rate signals utilizing transformer-based architectures.Frontiers in pain research (Lausanne, Switzerland) · 2024Article
- Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores.Computational intelligence and neuroscience · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First Results.International journal of environmental research and public health · 2021Article
- Emotion and Stress Recognition Related Sensors and Machine Learning Technologies.Sensors (Basel, Switzerland) · 2021Article
- Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective.Frontiers in physiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Several approaches have been proposed for the analysis of pain-related facial expressions. These approaches range from common classification architectures based on a set of carefully designed handcrafted features, to deep neural networks characterised by an autonomous extraction of relevant facial descriptors and simultaneous optimisation of a classification architecture. In the current work, an end-to-end approach based on attention networks for the analysis and recognition of pain-related facial expressions is proposed. The method combines both spatial and temporal aspects of facial expressions through a weighted aggregation of attention-based neural networks' outputs, based on sequences of Motion History Images (MHIs) and Optical Flow Images (OFIs). Each input stream is fed into a specific attention network consisting of a Convolutional Neural Network (CNN) coupled to a Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN). An attention mechanism generates a single weighted representation of each input stream (MHI sequence and OFI sequence), which is subsequently used to perform specific classification tasks. Simultaneously, a weighted aggregation of the classification scores specific to each input stream is performed to generate a final classification output. The assessment conducted on both 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.