Automated detection approaches to autism spectrum disorder based on human activity analysis: A review

Article


Rahman, S., Ahmed, S. F., Shahid, O., Arrafi, M. A. and Ahad, M. A. R. 2022. Automated detection approaches to autism spectrum disorder based on human activity analysis: A review. Cognitive Computation. 14, pp. 1773-1800. https://doi.org/10.1007/s12559-021-09895-w
AuthorsRahman, S., Ahmed, S. F., Shahid, O., Arrafi, M. A. and Ahad, M. A. R.
Abstract

Autism Spectrum Disorder (ASD) is a neuro-developmental disorder that limits social and cognitive abilities. ASD has no cure so early diagnosis is important for reducing its impact. The current behavioral observation-based subjective-diagnosis systems (e.g., DSM-5 or ICD-10) frequently misdiagnose subjects. Therefore, researchers are attempting to develop automated diagnosis systems with minimal human intervention, quicker screening time, and better outreach. This paper is a PRISMA-based systematic review examining the potential of automated autism detection system with Human Activity Analysis (HAA) to look for distinctive ASD characteristics such as repetitive behavior, abnormal gait and visual saliency. The literature from 2011 onward is qualitatively and quantitatively analyzed to investigate whether HAA can identify the features of ASD, the level of its classification accuracy, the degree of human intervention, and screening time. Based on these findings, we discuss the approaches, challenges, resources, and future directions in this area. According to our quantitative assessment of the dataset Zunino et al. (IEEE: 3421–3426, 2018 [1]), Inception v3 and LSTM Zunino et al. (IEEE: 3421–3426, 2018 [1]) give the highest accuracy (89%) for repetitive behavior. For abnormal gait-based approach, the multilayer perceptron gives 98% accuracy based on 18 features from dataset Abdulrahman et al. (COMPUSOFT: An International Journal of Advanced Computer Technology 9(8):3791–3797, 2020 [2]). For gaze pattern, a saliency-metric feature-based learning Rahman et al. (Int Conf Pattern Recognit, 2020 [3]) gives 99% accuracy on dataset Duan et al. (Proceedings of the 10th ACM Multimedia Systems Conference: 255–260, 2019 [4]), while an algorithm involving statistical features and Decision Trees yields an accuracy of 76% on dataset Yaneva et al. (Proceedings of the Internet of Accessible Things. W4A ’18, Association for Computing Machinery, New York, NY, USA, 1–10, 2018 [5]). In terms of the state of the art, fully automated HAA systems for ASD diagnosis show promise but are still in developmental stages. However, this is an active research field, and HAA has good prospects for helping to diagnose ASD objectively in less time with better accuracy.

KeywordsAutism; Autism Spectrum Disorder; Detection, AI
JournalCognitive Computation
Journal citation14, pp. 1773-1800
ISSN 1866-9956
Year2022
PublisherSpringer
Digital Object Identifier (DOI)https://doi.org/10.1007/s12559-021-09895-w
Publication dates
Online27 Jul 2021
PrintSep 2022
Publication process dates
Accepted29 Mar 2021
Deposited04 Dec 2023
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