ArticleCEUR workshop proceedings2018
Towards Automated Pain Detection in Children using Facial and Electrodermal Activity.
Article in CEUR workshop proceedings, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
- Identification of technology-based models and efficacy of digital-based pain facial expression assessment tools among children: a systematic review.BMC nursing · 2025Article
- Using AI to Detect Pain through Facial Expressions: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition.Sensors (Basel, Switzerland) · 2023Article
- Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity.American journal of physiology. Regulatory, integrative and comparative physiology · 2021Article
- Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor.Sensors (Basel, Switzerland) · 2021Article
- Using electrodermal activity to validate multilevel pain stimulation in healthy volunteers evoked by thermal grills.American journal of physiology. Regulatory, integrative and comparative physiology · 2020Article
- A child in pain: A psychologist's perspective on changing priorities in scientific understanding and clinical care.Paediatric & neonatal pain · 2020Review
- Pediatric Clinical Endpoint and Pharmacodynamic Biomarkers: Limitations and Opportunities.Paediatric drugs · 2020Review
- Automated Pain Detection in Facial Videos of Children using Human-Assisted Transfer Learning.CEUR workshop proceedings · 2018Article
- Towards Automated Pain Detection in Children using Facial and Electrodermal Activity.CEUR workshop proceedings · 2018Article
- Managing Pediatric Pain in Low-Resource Emergency Settings: Barriers, Advances, and Future Directions.Sage open pediatricsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity and electro- dermal activity (EDA) provide rich information about pain, and both have been used in automated pain detection. In this paper, we discuss preliminary steps towards fusing models trained on video and EDA features respectively. We compare fusion models using original video features and those using transferred video features which are less sensitive to environmental changes. We demonstrate the benefit of the fusion and the transferred video features with a special test case involving domain adaptation and improved performance relative to using EDA and video features alone.
Indexed as
Identifiers
30713486PMC6352962W2889026124What OpenQuestion holds
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.