Automated Anomalous Child Repetitive Head Movement Identification through Transformer Networks

Nushara Wedasingha, Pradeepa Samarasinghe, Lasantha Senevirathna, Michela Papandrea, Alessandro Puiatti, Debbie Rankin

Research output: Contribution to journalArticlepeer-review

Abstract

The increasing prevalence of behavioral disorders in children is of growing concern within the medical community. Recognising the significance of early identification and intervention for atypical behaviors, there is a consensus on their pivotal role in improving outcomes. Due to inadequate facilities and a shortage of medical professionals with specialized expertise, traditional diagnostic methods have been unable to effectively address the rising incidence of behavioral disorders. Hence, there is a need to develop automated approaches for the diagnosis of behavioral disorders in children, to overcome the challenges with traditional methods. The purpose of this study is to develop an automated model capable of analyzing videos to differentiate between typical and atypical repetitive head movements in. To address problems resulting from the limited availability of child datasets, various learning methods are employed to mitigate these issues. In this work, we present a fusion of transformer networks, and Non-deterministic Finite Automata (NFA) techniques, which classify repetitive head movements of a child as typical or atypical based on an analysis of gender, age, and type of repetitive head movement, along with count, duration, and frequency of each repetitive head movement. Experimentation was carried out with different transfer learning methods to enhance the performance of the model. The experimental results on five datasets: NIR face dataset, Bosphorus 3D face dataset, ASD dataset, SSBD dataset, and the Head Movements in the Wild dataset, indicate that our proposed model has outperformed many state-of-the-art frameworks when distinguishing typical and atypical repetitive head movements in children.

Original languageEnglish
Pages (from-to)1427-1445
Number of pages19
JournalPhysical and Engineering Sciences in Medicine
Volume46
Issue number4
Early online date9 Oct 2023
DOIs
Publication statusPublished online - 9 Oct 2023

Bibliographical note

Funding Information:
This research was supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education of Sri Lanka funded by the World Bank. Url: https://ahead.lk/result-area-3/ .

Funding Information:
This research was supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education of Sri Lanka funded by the World Bank ( https://ahead.lk/result-area-3/ ).

Publisher Copyright:
© 2023, Australasian College of Physical Scientists and Engineers in Medicine.

Keywords

  • transformer
  • Head pose estimation
  • Periodicities
  • non-deterministic finite automata
  • transfer learning
  • repetitive behaviour analysis
  • movement analysis
  • atypical head movements
  • head gesture classification
  • Transformer
  • Transfer learning
  • Periodicity
  • Movement analysis
  • Repetitive behavior analysis
  • Atypical head movements
  • Non-deterministic finite automata (NFA)
  • Head gesture classification

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