Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism

Francesco Bonacini; Mufti Mahmud; David J. Brown

doi:10.1007/978-981-99-1642-9_50

Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism

Francesco Bonacini
, Mufti Mahmud^*
, David J. Brown

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

With the increasing prevalence of autism, it is imperative to develop new strategies and tools to help caregivers, parents and teaching staff support the needs of students with autism. In particular, children may experience highly stressful events, sometimes termed ‘meltdown’ or ‘emotional dysregulation’ events, which are preceded by a ‘rumble’ stage that could be detected and acted upon in a timely manner. Among the many possible solutions, the use of technology and, in particular, Artificial Intelligence is promising, thanks to the recent advancements in research. Our study focuses on the development of an action recognition model to detect and distinguish the six most common actions that children with autism exhibit during the rumble stage when approaching a meltdown. In doing so, we think caregivers, parents and teaching staff would be able to use the inferences generated by the model and intervene with evidence-based well-being practices to address such issues before escalation and decrease the frequency and intensity of such events.

Original language	English
Title of host publication	Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings
Editors	Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	584-596
Number of pages	13
ISBN (Print)	9789819916412
DOIs	https://doi.org/10.1007/978-981-99-1642-9_50
State	Published - 2023
Externally published	Yes
Event	29th International Conference on Neural Information Processing, ICONIP 2022 - Virtual, Online Duration: 22 Nov 2022 → 26 Nov 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1792 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	29th International Conference on Neural Information Processing, ICONIP 2022
City	Virtual, Online
Period	22/11/22 → 26/11/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

AI
Autism
CNN-LSTM
Deep Learning
Meltdown
Rumble Stage

ASJC Scopus subject areas

General Computer Science
General Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-99-1642-9_50

Cite this

Bonacini, F., Mahmud, M., & Brown, D. J. (2023). Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism. In M. Tanveer, S. Agarwal, S. Ozawa, A. Ekbal, & A. Jatowt (Eds.), Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings (pp. 584-596). (Communications in Computer and Information Science; Vol. 1792 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-1642-9_50

Bonacini, Francesco ; Mahmud, Mufti ; Brown, David J. / Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism. Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. editor / Mohammad Tanveer ; Sonali Agarwal ; Seiichi Ozawa ; Asif Ekbal ; Adam Jatowt. Springer Science and Business Media Deutschland GmbH, 2023. pp. 584-596 (Communications in Computer and Information Science).

@inproceedings{21d2d9c5da854ed99a1e79df45fce597,

title = "Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism",

abstract = "With the increasing prevalence of autism, it is imperative to develop new strategies and tools to help caregivers, parents and teaching staff support the needs of students with autism. In particular, children may experience highly stressful events, sometimes termed {\textquoteleft}meltdown{\textquoteright} or {\textquoteleft}emotional dysregulation{\textquoteright} events, which are preceded by a {\textquoteleft}rumble{\textquoteright} stage that could be detected and acted upon in a timely manner. Among the many possible solutions, the use of technology and, in particular, Artificial Intelligence is promising, thanks to the recent advancements in research. Our study focuses on the development of an action recognition model to detect and distinguish the six most common actions that children with autism exhibit during the rumble stage when approaching a meltdown. In doing so, we think caregivers, parents and teaching staff would be able to use the inferences generated by the model and intervene with evidence-based well-being practices to address such issues before escalation and decrease the frequency and intensity of such events.",

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Bonacini, F, Mahmud, M & Brown, DJ 2023, Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism. in M Tanveer, S Agarwal, S Ozawa, A Ekbal & A Jatowt (eds), Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. Communications in Computer and Information Science, vol. 1792 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 584-596, 29th International Conference on Neural Information Processing, ICONIP 2022, Virtual, Online, 22/11/22. https://doi.org/10.1007/978-981-99-1642-9_50

Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism. / Bonacini, Francesco; Mahmud, Mufti; Brown, David J.
Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. ed. / Mohammad Tanveer; Sonali Agarwal; Seiichi Ozawa; Asif Ekbal; Adam Jatowt. Springer Science and Business Media Deutschland GmbH, 2023. p. 584-596 (Communications in Computer and Information Science; Vol. 1792 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Bonacini F, Mahmud M, Brown DJ. Towards the Development of a Machine Learning-Based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism. In Tanveer M, Agarwal S, Ozawa S, Ekbal A, Jatowt A, editors, Neural Information Processing - 29th International Conference, ICONIP 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 584-596. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-1642-9_50