Automated and scalable Computerized Assessment of Motor Imitation (CAMI) in children with Autism Spectrum Disorder using a single 2D camera: A pilot study

Lidstone, Daniel E.; Rochowiak, Rebecca; Pacheco, Carolina; Tunçgenç, Bahar; Vidal, René; Mostofsky, Stewart H.

doi:10.1016/j.rasd.2021.101840

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…A standard battery of kinematic parameters for measuring motor differences in autism is conceivable and has functional utility in the development of algorithmic assessment of movement (Millar et al., 2019; Wedvan et al., 2019; Lidstone et al., 2021). Such a battery, incorporating parameters with neurobiological and developmental significance, could be tailored to suit specific aims with parameter selection guided by data type and context.…”

Section: Discussionmentioning

confidence: 99%

Developmental differences in the prospective organisation of goal‐directed movement between children with autism and typically developing children: A smart tablet serious game study

Chua

Anzulewicz

et al. 2021

Developmental Science

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Movement is prospective. It structures self-generated engagement with objects and social partners and is fundamental to children's learning and development. In autistic children, previous reports of differences in movement kinematics compared to neurotypical peers suggest that its prospective organisation might be disrupted.Here, we employed a smart tablet serious game paradigm to assess differences in the feedforward and feedback mechanisms of prospective action organisation, between autistic and neurotypical preschool children. We analysed 3926 goal-directed finger movements made during smart-tablet ecological gameplay, from 28 children with Childhood Autism (ICD-10; ASD) and 43 neurotypical children (TD), aged 3-6 years old. Using linear and generalised linear mixed-effect models, we found the ASD group executed movements with longer movement time (MT) and time to peak velocity (TTPV), lower peak velocity (PV), with PV less likely to occur in the first movement unit (MU) and with a greater number of movement units after peak velocity (MU-APV).Interestingly, compared to the TD group, the ASD group showed smaller increases in PV, TTPV and MT with an increase in age (ASD × age interaction), together with a smaller reduction in MU-APV and an increase in MU-APV at shorter target distances (ASD × Dist interaction). Our results are the first to highlight different developmental trends in anticipatory feedforward and compensatory feedback mechanisms of control, contributing to differences in movement kinematics observed betweenThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

Developmental differences in the prospective organisation of goal‐directed movement between children with autism and typically developing children: A smart tablet serious game study

Chua

Anzulewicz

et al. 2021

Developmental Science

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“…Hashemi et al ( 78 ) tracked the head motion of children with autism using bounding boxes over the child’s eyes, ears, and nose. Lidstone et al ( 79 ) used 3D depth cameras from Microsoft’s motion-sensing input device Kinect to distinguish children with autism from neurotypical controls, reaching an AUROC of 0.94 on a sample of 23 children with autism and 17 neurotypical controls. Kojovic et al ( 80 ) achieved an F 1 score of 0.89 in a binary autism diagnosis task on 68 children with autism and 68 neurotypical controls by using a convolutional neural network (CNN) to extract visual features from pose images and feeding these into a long short-term memory network.…”

Section: Classification Of Autism and Related Behaviorsmentioning

confidence: 99%

A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism

Washington

Wall

2023

Annu. Rev. Biomed. Data Sci.

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Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case–control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…A wealth of children's motion parameters can be observed in ASD study using a non-invasive method 24,25,32 . OpenPose is a common and robust tool used in pose estimation of children in ordinary 2D videos compared to many existing approaches [33][34][35][36][37] , e.g., Kinect. In this study, we developed a non-invasive computer vision and machine learning-based framework for analyzing ADOS assessment videos.…”

Section: Identifying Activity Level Related Movement Features Of Chil...mentioning

confidence: 99%

Identifying activity level related movement features of children with ASD based on ADOS videos

Jin

Zhu

Cao

et al. 2023

Sci Rep

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects about 2% of children. Due to the shortage of clinicians, there is an urgent demand for a convenient and effective tool based on regular videos to assess the symptom. Computer-aided technologies have become widely used in clinical diagnosis, simplifying the diagnosis process while saving time and standardizing the procedure. In this study, we proposed a computer vision-based motion trajectory detection approach assisted with machine learning techniques, facilitating an objective and effective way to extract participants’ movement features (MFs) to identify and evaluate children’s activity levels that correspond to clinicians’ professional ratings. The designed technique includes two key parts: (1) Extracting MFs of participants’ different body key points in various activities segmented from autism diagnostic observation schedule (ADOS) videos, and (2) Identifying the most relevant MFs through established correlations with existing data sets of participants’ activity level scores evaluated by clinicians. The research investigated two types of MFs, i.e., pixel distance (PD) and instantaneous pixel velocity (IPV), three participants’ body key points, i.e., neck, right wrist, and middle hip, and five activities, including Table-play, Birthday-party, Joint-attention, Balloon-play, and Bubble-play segmented from ADOS videos. Among different combinations, the high correlations with the activity level scores evaluated by the clinicians (greater than 0.6 with p < 0.001) were found in Table-play activity for both the PD-based MFs of all three studied key points and the IPV-based MFs of the right wrist key point. These MFs were identified as the most relevant ones that could be utilized as an auxiliary means for automating the evaluation of activity levels in the ASD assessment.

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Automated and scalable Computerized Assessment of Motor Imitation (CAMI) in children with Autism Spectrum Disorder using a single 2D camera: A pilot study

Cited by 9 publications

References 22 publications

Developmental differences in the prospective organisation of goal‐directed movement between children with autism and typically developing children: A smart tablet serious game study

Developmental differences in the prospective organisation of goal‐directed movement between children with autism and typically developing children: A smart tablet serious game study

A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism

Identifying activity level related movement features of children with ASD based on ADOS videos

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