Movement detection software to enhance autism assessment processes

Simeoli, Roberta; Arnucci, Miriana; Rega, Angelo; Marocco, Davide

doi:10.1145/3401956.3404246

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…Anzulewicz et al (2016) used an iPad gameplay and 262 features of movements, derived from touch screen and inertial sensors, and they showed that children with autism could be identified with up to 93% accuracy. Differences between children with ASD and typically developing children have emerged in terms of linearity, speed, and pattern of interaction (Simeoli et al, 2019b(Simeoli et al, , 2020. Also, a greater engagement in carrying out cognitive tasks using digital tools has emerged, especially for individuals with severe autism (Simeoli et al, 2019a).…”

Section: Related Workmentioning

confidence: 99%

Using Technology to Identify Children With Autism Through Motor Abnormalities

et al. 2021

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Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In recent years, there is a growing need to make assessment processes more motivating and capable to provide objective measures of the disorder. New evidence showed that motor abnormalities may underpin the disorder and provide a computational marker to enhance assessment and diagnostic processes. Thus, a measure of motor patterns could provide a means to assess young children with autism and a new starting point for rehabilitation treatments. In this study, we propose to use a software tool that through a smart tablet device and touch screen sensor technologies could be able to capture detailed information about children’s motor patterns. We compared movement trajectories of autistic children and typically developing children, with the aim to identify autism motor signatures analyzing their coordinates of movements. We used a smart tablet device to record coordinates of dragging movements carried out by 60 children (30 autistic children and 30 typically developing children) during a cognitive task. Machine learning analysis of children’s motor patterns identified autism with 93% accuracy, demonstrating that autism can be computationally identified. The analysis of the features that most affect the prediction reveals and describes the differences between the groups, confirming that motor abnormalities are a core feature of autism.

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Section: Related Workmentioning

confidence: 99%

Using Technology to Identify Children With Autism Through Motor Abnormalities

et al. 2021

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“…For this reason, the scientific community is currently deepening the etiopathogenesis of the disorder and refining the diagnostic and assessment methods for ASD ( Bertoglio and Hendren, 2009 ; Fombonne, 2009 ). Namely, researchers have begun to reconsider autistic symptomatology highlighting the potential of motion analysis ( Anzulewicz et al, 2016 ; Simeoli et al, 2020 , 2021 ). Although motor impairments have been widely observed in ASD, their importance as “defining symptoms” has always been underestimated.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we deepened the analysis of a previous dataset ( Simeoli et al, 2020 , 2021 ) by applying a variational autoencoder, i.e., a particular type of artificial neural network. Such VAE is used to explore different prediction methods and broaden the ASD motion pattern analysis through latent distribution analysis.…”

Section: Introductionmentioning

confidence: 99%

A deep learning latent variable model to identify children with autism through motor abnormalities

et al. 2023

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IntroductionAutism Spectrum Disorder (ASD) is a by-birth neurodevelopmental disorder difficult to diagnose owing to the lack of clinical objective and quantitative measures. Classical diagnostic processes are time-consuming and require many specialists’ collaborative efforts to be properly accomplished. Most recent research has been conducted on automated ASD detection using advanced technologies. The proposed model automates ASD detection and provides a new quantitative method to assess ASD.MethodsThe theoretical framework of our study assumes that motor abnormalities can be a potential hallmark of ASD, and Machine Learning may represent the method of choice to analyse them. In this study, a variational autoencoder, a particular type of Artificial Neural Network, is used to improve ASD detection by analysing the latent distribution description of motion features detected by a tablet-based psychometric scale.ResultsThe proposed ASD detection model revealed that the motion features of children with autism consistently differ from those of children with typical development.DiscussionOur results suggested that it could be possible to identify potential motion hallmarks typical for autism and support clinicians in their diagnostic process. Potentially, these measures could be used as additional indicators of disorder or suspected diagnosis.

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“…Figure–ground segmentation is a core process of perceptual organization, the mechanisms of which are still being studied extensively [ 43 , 44 , 45 ]. Additionally, recent studies indicate that the performance in figure–ground segmentation can also be applied to diagnostic tasks, such as for neurological impairments, e.g., screening for posterior cortical atrophy [ 46 ] or as a part of autism assessment procedures [ 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

Figure–Ground Segmentation and Biological Motion Perception in Peripheral Visual Field

et al. 2023

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Biological motion perception is a specific type of perceptual organization, during which a clear image of a moving human body is perceptually generated in virtue of certain core light dots representing the major joint movements. While the processes of biological motion perception have been studied extensively for almost a century, there is still a debate on whether biological motion task performance can be equally precise across all visual field or is central visual field specified for biological motion perception. The current study explores the processes of biological motion perception and figure–ground segmentation in the central and peripheral visual field, expanding the understanding of perceptual organization across different eccentricities. The method involved three different tasks of visual grouping: (1) a static visual grouping task, (2) a dynamic visual grouping task, and (3) a biological motion detection task. The stimuli in (1) and (2) were generated from 12–13 dots grouped by proximity and common fate, and, in (3), light dots representing human motion. All stimuli were embedded in static or dynamics visual noise and the threshold value for the number of noise dots in which the elements could still be grouped by proximity and/or common fate was determined. The results demonstrate that biological motion can be differentiated from the scrambled set of moving dots in a more intensive visual noise than static and dynamic visual grouping tasks. Furthermore, in all three visual tasks (static and dynamic grouping, and biological motion detection) the performance was significantly worse in the periphery than in the central visual field, and object magnification could not compensate for the reduced performance in any of the three grouping tasks. The preliminary results of nine participants indicate that (a) human motion perception involves specific perceptual processes, providing the high-accuracy perception of the human body and (b) the processes of figure–ground segmentation are governed by the bottom-up processes and the best performance can be achieved only when the object is demonstrated in the central visual field.

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Movement detection software to enhance autism assessment processes

Cited by 6 publications

References 15 publications

Using Technology to Identify Children With Autism Through Motor Abnormalities

Using Technology to Identify Children With Autism Through Motor Abnormalities

A deep learning latent variable model to identify children with autism through motor abnormalities

Figure–Ground Segmentation and Biological Motion Perception in Peripheral Visual Field

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