Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

Doi, Hiroyuki; Iijima, Naoya; Furui, Akira; Soh, Zu; Yonei, Rikuya; Shinohara, Kazuyuki; Iriguchi, Mayuko; Shimatani, Koji; Tsuji, Takao

doi:10.1038/s41598-022-21308-y

Cited by 10 publications

(14 citation statements)

References 65 publications

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“…So far, several studies have demonstrated its effectiveness in recognizing biological information [ 29 , 30 ], including an application to analyze the relationship between driving aptitude and physical and cognitive functions [ 31 ]. Moreover, since only the number of mixtures is a hyperparameter, the tuning cost is lower than that of general neural networks such as the multilayer perceptron [ 32 ]. However, simply evaluating driving aptitude using the LLGMN does not reveal the relationship between each participant’s clinical index and driving aptitude, rendering it challenging to realize the original intent of establishing an effective method for identifying subjects who must undergo an on-road driving test through hospital-based physical and cognitive function screening tests.…”

Section: Methodsmentioning

confidence: 99%

Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis

Tabuchi,

Furui,

Hama

et al. 2023

J NeuroEngineering Rehabil

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Background People who were previously hospitalised with stroke may have difficulty operating a motor vehicle, and their driving aptitude needs to be evaluated to prevent traffic accidents in today’s car-based society. Although the association between motor-cognitive functions and driving aptitude has been extensively studied, motor-cognitive functions required for driving have not been elucidated. Methods In this paper, we propose a machine-learning algorithm that introduces sparse regularization to automatically select driving aptitude-related indices from 65 input indices obtained from 10 tests of motor-cognitive function conducted on 55 participants with stroke. Indices related to driving aptitude and their required tests can be identified based on the output probability of the presence or absence of driving aptitude to provide evidence for identifying subjects who must undergo the on-road driving test. We also analyzed the importance of the indices of motor-cognitive function tests in evaluating driving aptitude to further clarify the relationship between motor-cognitive function and driving aptitude. Results The experimental results showed that the proposed method achieved predictive evaluation of the presence or absence of driving aptitude with high accuracy (area under curve 0.946) and identified a group of indices of motor-cognitive function tests that are strongly related to driving aptitude. Conclusions The proposed method is able to effectively and accurately unravel driving-related motor-cognitive functions from a panoply of test results, allowing for autonomous evaluation of driving aptitude in post-stroke individuals. This has the potential to reduce the number of screening tests required and the corresponding clinical workload, further improving personal and public safety and the quality of life of individuals with stroke.

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Section: Methodsmentioning

confidence: 99%

Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis

Tabuchi,

Furui,

Hama

et al. 2023

J NeuroEngineering Rehabil

Self Cite

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“…The features extracted from the video-recordings are tabulated in Table 1 . The exact definition and extraction procedure of each feature are described in more details in our previous studies 37 , 38 and are briefly summarized in Supplementary Materials. Because the video recording took place in a hospital room, there was ambient background noise, such as voices and operational sounds, throughout the recording session.…”

Section: Methodsmentioning

confidence: 99%

“…Teitelbaum et al proposed a framework for the early diagnosis of ASD by qualitative assessment of body movement at around five months old 32 . Other studies have also suggested the utility of an atypical pattern of body movement as potential biomarker for early screening of ASD risk 33 – 37 . However, these studies focused on infants after four months old, and it remains unclear whether an assessment of body movement pattern at the neonatal stage is useful in early screening of children at risk for ASD.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal patterns of spontaneous movement in neonates are significantly linked to risk of autism spectrum disorders at 18 months old

Doi,

Furui,

Ueda

et al. 2023

Sci Rep

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Infants make spontaneous movements from the prenatal period. Several studies indicate that an atypical pattern of body motion during infancy could be utilized as an early biomarker of autism spectrum disorders (ASD). However, to date, little is known about whether the body motion pattern in neonates is associated with ASD risk. The present study sought to clarify this point by examining, in a longitudinal design, the link between features of spontaneous movement at about two days after birth and ASD risk evaluated using the Modified Checklist for Autism in Toddlers by their caregivers at 18 months old. The body movement features were quantified by a recently developed markerless system of infant body motion analysis. Logistic regression analysis revealed that ASD risk at 18 months old is associated with the pattern of spontaneous movement at the neonatal stage. Further, logistic regression based on body movement features during sleep shows better performance in classifying high- and low-risk infants than during the awake state. These findings raise the possibility that early signs of ASD risk may emerge at a developmental stage far earlier than previously thought.

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“…Parents of infants who have a sibling with a diagnosed developmental impairment, such as autism spectrum disorder (ASD), fall into this category based on suggested familial risk [ 6 ]. As is the current clinical thinking relative to CP, detection of signs of ASD as early as possible [ 7 , 8 ] facilitates the introduction of interventions most likely to be effective in maximizing positive outcomes.…”

Section: Introductionmentioning

confidence: 99%

Evolution of a System to Monitor Infant Neuromotor Development in the Home: Lessons from COVID-19

et al. 2023

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In the nine months leading up to COVID-19, our biomedical engineering research group was in the very early stages of development and in-home testing of HUGS, the Hand Use and Grasp Sensor (HUGS) system. HUGS was conceived as a tool to allay parents’ anxiety by empowering them to monitor their infants’ neuromotor development at home. System focus was on the evolving patterns of hand grasp and general upper extremity movement, over time, in the naturalistic environment of the home, through analysis of data captured from force-sensor-embedded toys and 3D video as the baby played. By the end of March, 2020, as the COVID-19 pandemic accelerated and global lockdown ensued, home visits were no longer possible and HUGS system testing ground to an abrupt halt. In the spring of 2021, still under lockdown, we were able to resume recruitment and in-home testing with HUGS-2, a system whose key requirement was that it be contactless. Participating families managed the set up and use of HUGS-2, supported by a detailed library of video materials and virtual interaction with the HUGS team for training and troubleshooting over Zoom. Like the positive/negative poles of experience reported by new parents under the isolation mandated to combat the pandemic, HUGS research was both impeded and accelerated by having to rely solely on distance interactions to support parents, troubleshoot equipment, and securely transmit data. The objective of this current report is to chronicle the evolution of HUGS. We describe a system whose design and development straddle the pre- and post-pandemic worlds of family-centered health technology design. We identify and classify the clinical approaches to infant screening that predominated in the pre-COVID-19 milieu and describe how these procedural frameworks relate to the family-centered conceptualization of HUGS. We describe how working exclusively through the proxy of parents revealed the family’s priorities and goals for child interaction and surfaced HUGS design shortcomings that were not evident in researcher-managed, in-home testing prior to the pandemic.

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Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

Cited by 10 publications

References 65 publications

Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis

Motor-cognitive functions required for driving in post-stroke individuals identified via machine-learning analysis

Spatiotemporal patterns of spontaneous movement in neonates are significantly linked to risk of autism spectrum disorders at 18 months old

Evolution of a System to Monitor Infant Neuromotor Development in the Home: Lessons from COVID-19

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