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Automated Newborn Pain Assessment Framework Using Computer Vision Techniques

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Enrico BaccagliniAuthors Info & Claims

ICBRA '17: Proceedings of the 4th International Conference on Bioinformatics Research and Applications

Pages 31 - 36

https://doi.org/10.1145/3175587.3175590

Published: 08 December 2017 Publication History

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Abstract

Pain evaluation in newborns is becoming a mandatory task in clinical practice. Currently, despite its complexity, pain assessment is entirely delegated to subjective estimates. The aim of this study is to propose an automated -- thus objective -- approach for neonatal pain evaluation focusing on facial expressions. From patients' face detection, a set of relevant parameters were extracted; both pixel-based image processing and analysis of facial landmarks led to final pain scores which were computed with respect to 3 widely adopted pain scales. The algorithm has been validated in a trial based on 15 videos acquired at the Neonatal Unit of AO Ordine Mauriziano Hospital, Turin, during heel stick procedure for blood sampling. The proposed algorithm scores have been compared to those subjectively assigned by health care professionals. The results confirm that manual pain assessment is a challenging task that often results in an elevated variance across scores between different operators, making automated evaluation highly desirable. The proposed algorithm is a first step in this direction, and despite difficulties in handling rapid facial changes, it is preparatory to the definition of an experimental protocol which merges video analysis, including the most relevant facial metrics, with audio processing for improved reliability.

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Cited By

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Gaikwad NUpadhyay DManero JSampalli S(2024)IoT-Enabled Pediatric Pain Care Leveraging Majority Vote-Based Transfer Learning Models2024 7th Conference on Cloud and Internet of Things (CIoT)10.1109/CIoT63799.2024.10757022(1-8)Online publication date: 29-Oct-2024
https://doi.org/10.1109/CIoT63799.2024.10757022
Mokhtari BMerizig AZerdoumi H(2024)Enhanced emotion recognition in an IoMT platform: leveraging data augmentation and the random forest algorithm for ECG-based E-healthInternational Journal of Information Technology10.1007/s41870-024-01951-6Online publication date: 9-Jun-2024
https://doi.org/10.1007/s41870-024-01951-6
Bergamasco LLattanzi MGavelli MPastrone COlmo GBorsotti LParodi E(2024)Pain Assessment in Neonatal Clinical Practice via Facial Expression Analysis and Deep LearningBioinformatics and Biomedical Engineering10.1007/978-3-031-64636-2_19(249-263)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-64636-2_19
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Index Terms

Automated Newborn Pain Assessment Framework Using Computer Vision Techniques
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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Published In

ICBRA '17: Proceedings of the 4th International Conference on Bioinformatics Research and Applications

December 2017

91 pages

ISBN:9781450353823

DOI:10.1145/3175587

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 December 2017

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ICBRA 2017

ICBRA 2017: International Conference on Bioinformatics Research and Applications 2017

December 8 - 10, 2017

Barcelona, Spain

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Cited By

View all

Gaikwad NUpadhyay DManero JSampalli S(2024)IoT-Enabled Pediatric Pain Care Leveraging Majority Vote-Based Transfer Learning Models2024 7th Conference on Cloud and Internet of Things (CIoT)10.1109/CIoT63799.2024.10757022(1-8)Online publication date: 29-Oct-2024
https://doi.org/10.1109/CIoT63799.2024.10757022
Mokhtari BMerizig AZerdoumi H(2024)Enhanced emotion recognition in an IoMT platform: leveraging data augmentation and the random forest algorithm for ECG-based E-healthInternational Journal of Information Technology10.1007/s41870-024-01951-6Online publication date: 9-Jun-2024
https://doi.org/10.1007/s41870-024-01951-6
Bergamasco LLattanzi MGavelli MPastrone COlmo GBorsotti LParodi E(2024)Pain Assessment in Neonatal Clinical Practice via Facial Expression Analysis and Deep LearningBioinformatics and Biomedical Engineering10.1007/978-3-031-64636-2_19(249-263)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-64636-2_19
Bergamasco LGavelli MFadda CParodi EBondone CCastagno E(2023)Measurement of Acute Pain in the Pediatric Emergency Department Through Automatic Detection of Behavioral Parameters: A Pilot StudyBioinformatics and Biomedical Engineering10.1007/978-3-031-34953-9_37(469-481)Online publication date: 29-Jun-2023
https://doi.org/10.1007/978-3-031-34953-9_37

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Tangible Interactions for Pain Assessment in Palliative Care

Pose-invariant and occlusion-robust neonatal facial pain assessment

Human-Computer Interaction Using Emotion Recognition from Facial Expression