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ASC-emotion: A privacy-aware dataset for analysing emotional dysregulation and engagement in children with Autism<sup/>.

Overview

Authors: Zakia Batool Turabee1, David J. Brown1, Mufti Mahmud2,3,4, Andreas Oikonomou1, Muhammad Arifur Rahman1, Andrew Burton1, Nicholas Shopland1, Dawn Clarke5, Fiona Gray5
ORCID iDs: Zakia Batool Turabee, David J. Brown, Mufti Mahmud, Andreas Oikonomou, Muhammad Arifur Rahman, Andrew Burton, Nicholas Shopland
  1. Department of Computer Science, Nottingham Trent University, Nottingham, NG11 8NS, United Kingdom
  2. Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
  3. SDAIA-KFUPM Joint Research Center for AI, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
  4. Interdisciplinary Research Center for Biosystems and Machines, King Fahd University of Petroleum &amp; Minerals, Dhahran, 31261, Saudi Arabia
  5. Autism Team, SEND Support Services, Education Division, Nottingham City Council, Nottingham, NG8 3BP, United Kingdom
Institutions: Nottingham Trent University (United Kingdom); King Fahd University of Petroleum and Minerals (Saudi Arabia); Nottingham City Council (United Kingdom)
Journal: MethodsX, volume 17, article 104073
Dates: received 15 May 2026; accepted 25 July 2026; published online 27 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.mex.2026.104073 · PMID 42569523 · PMCID PMC13450243 · OpenAlex W7171443786
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: human (organism), autism (population)
Methods: Machine learning
Keywords: Autism spectrum condition (ASC), Challenging behaviours, Facial expressions, Rumble moments, Emotional dysregulation, Engagement and arousal tracking, Camera and wearables
Journal subjects: Computer Science
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Nottingham City Council; Horizon 2020 Framework Programme
Citations: not cited yet (Europe PMC); 19 references in the paper

Abstract

Predicting emotional dysregulation events in children with Autism is essential for timely mitigation of triggering events and prevention of further escalation of the situation. However, there is a scarcity of accessible and standarised datasets for use in AI-based research associated with challenging behaviours in children with ASC. To address this gap, we have curated a novel privacy-preserved dataset as part of an Erasmus+ funded project (AI-TOP-2020–1-UK01-KA201–079,167). The dataset was validated using three machine learning architectures which gave an accuracy of 96% in detecting the affective states related to learning in children with Autism. The key contributions of this study are:

1. Development of an Autism meltdown dataset exemplifies methodological rigor, advances an urgent clinical challenge through early detection and intervention, and enables broad impact by promoting reproducibility, benchmarking and translational health outcomes.

2. Implementation of privacy preserving measures addresses ethical concerns regarding the use of video data with this vulnerable population as part of the machine learning pipeline.

3. High levels of accuracy are demonstrated via empirical validation of the dataset through three machine learning models (BiLSTM, Graphical Neural Network – EdgeConv, PointCNN+LSTM) for detecting affective states related to learning and physiological arousal in children with Autism.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Tracing map

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Data

Datasets cited

Data availability

The data is publicaly available and doi is provided in the manuscript.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Zakia Batool Turabee (0000-0003-2613-7438); Andreas Oikonomou (0000-0002-5069-3971); Muhammad Arifur Rahman (0000-0002-6774-0041); Andrew Burton (0000-0002-9073-8310); Nicholas Shopland (0000-0003-2082-9070); removed Zakia Batool Turabee; Andreas Oikonomou; Muhammad Arifur Rahman; Andrew Burton; Nicholas Shopland
  • Funding: added Nottingham City Council; European Commission

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 9 authors, 7 keywords, 6 references.

Cite

This paper

Turabee, Z. B., Brown, D. J., Mahmud, M., Oikonomou, A., Rahman, M. A., Burton, A., Shopland, N., Clarke, D., & Gray, F. (2026). ASC-emotion: A privacy-aware dataset for analysing emotional dysregulation and engagement in children with Autism<sup/>. MethodsX, 17, 104073. https://doi.org/10.1016/j.mex.2026.104073

BibTeX

@article{turabee2026asc,
author = {Turabee, Zakia Batool and Brown, David J. and Mahmud, Mufti and Oikonomou, Andreas and Rahman, Muhammad Arifur and Burton, Andrew and Shopland, Nicholas and Clarke, Dawn and Gray, Fiona},
title = {{ASC-emotion: A privacy-aware dataset for analysing emotional dysregulation and engagement in children with Autism<sup/>}},
journal = {MethodsX},
year = {2026},
month = jul,
volume = {17},
pages = {104073},
publisher = {Elsevier},
issn = {2215-0161},
doi = {10.1016/j.mex.2026.104073},
url = {https://doi.org/10.1016/j.mex.2026.104073},
pmid = {42569523},
pmcid = {PMC13450243}
}

RIS

TY - JOUR
AU - Turabee, Zakia Batool
AU - Brown, David J.
AU - Mahmud, Mufti
AU - Oikonomou, Andreas
AU - Rahman, Muhammad Arifur
AU - Burton, Andrew
AU - Shopland, Nicholas
AU - Clarke, Dawn
AU - Gray, Fiona
TI - ASC-emotion: A privacy-aware dataset for analysing emotional dysregulation and engagement in children with Autism<sup/>
T2 - MethodsX
J2 - MethodsX
PY - 2026
DA - 2026/07/27
VL - 17
SP - 104073
SN - 2215-0161
PB - Elsevier
DO - 10.1016/j.mex.2026.104073
UR - https://doi.org/10.1016/j.mex.2026.104073
LA - en
ER -

CSL-JSON

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