scholarly journals Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3319
Author(s):  
Uzma Abid Siddiqui ◽  
Farman Ullah ◽  
Asif Iqbal ◽  
Ajmal Khan ◽  
Rehmat Ullah ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Gesture Recognition ◽  
Cross Validation ◽  
Wearable Sensors ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
Autistic People

Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child’s mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors’ data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation.

scholarly journals Accounting for motion in fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

2022 ◽  
Author(s):  
Mary Beth Nebel ◽  
Daniel Lidstone ◽  
Liwei Wang ◽  
David Benkeser ◽  
Stewart H Mostofsky ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Intellectual Ability ◽  
Typically Developing ◽  
Autistic Children ◽  
Typically Developing Children ◽  
The Impact

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 29.1% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 80.8% and 59.8% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

scholarly journals The Possible Role of the Microbiota-Gut-Brain-Axis in Autism Spectrum Disorder

2019 ◽  
Vol 20 (9) ◽  
pp. 2115 ◽  
Author(s):  
Piranavie Srikantha ◽  
M. Hasan Mohajeri
Keyword(s):  
Autism Spectrum Disorder ◽  
Gut Microbiota ◽  
Structural Changes ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Gut Health ◽  
Autistic Children ◽  
Bacterial Metabolites ◽  
Autistic People ◽  
Gastrointestinal Problems

New research points to a possible link between autism spectrum disorder (ASD) and the gut microbiota as many autistic children have co-occurring gastrointestinal problems. This review focuses on specific alterations of gut microbiota mostly observed in autistic patients. Particularly, the mechanisms through which such alterations may trigger the production of the bacterial metabolites, or leaky gut in autistic people are described. Various altered metabolite levels were observed in the blood and urine of autistic children, many of which were of bacterial origin such as short chain fatty acids (SCFAs), indoles and lipopolysaccharides (LPS). A less integrative gut-blood-barrier is abundant in autistic individuals. This explains the leakage of bacterial metabolites into the patients, triggering new body responses or an altered metabolism. Some other co-occurring symptoms such as mitochondrial dysfunction, oxidative stress in cells, altered tight junctions in the blood-brain barrier and structural changes in the cortex, hippocampus, amygdala and cerebellum were also detected. Moreover, this paper suggests that ASD is associated with an unbalanced gut microbiota (dysbiosis). Although the cause-effect relationship between ASD and gut microbiota is not yet well established, the consumption of specific probiotics may represent a side-effect free tool to re-establish gut homeostasis and promote gut health. The diagnostic and therapeutic value of bacterial-derived compounds as new possible biomarkers, associated with perturbation in the phenylalanine metabolism, as well as potential therapeutic strategies will be discussed.

scholarly journals Parent-mediated Behavioral Intervention for Treatment Behavioral Excesses in Children With Autism Spectrum Disorder

2020 ◽  
Vol 23 (4) ◽  
pp. 422-437
Author(s):  
Esmaeil Shiri ◽  
◽  
Hamidreza Pouratemad ◽  
Jalil Fathabadi ◽  
Mohammad Narimani ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Behavioral Intervention ◽  
Behavioral Interventions ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
Positive Effects ◽  
Children With Asd

Background and Aim: One of the problems of children with Autism Spectrum Disorder (ASD) is behavioral excesses resistance to many educational and rehabilitation programs. Parent-mediated behavioral interventions seem to be effective in overcoming these problems. However, these methods are not well-introduced. The purpose of this study is to systematically review these studies and their primary and secondary outcomes, and finally analyze the components. Methods & Materials: This study was a systematic review. The search included SID, Magiran, Medline, PubMed, Springer, Science Direct, Online Library, and PsycINFO. We reviewed The articles published between 2000 and 2017 about parent-mediated behavioral interventions on behavioral excesses in autistic children. Ethical Considerations: This study with ethical code IR.SBU.ICBS.97/1013 was approved by the Biological Research Department of Shahid Beheshti University. Results: The findings of 9 studies indicated positive effects of parent-mediated behavioral intervention on behavioral excesses, including repetitive behaviors, irritability (including tantrums, aggression, and self-injurious behaviors), echolalia, and destructive behaviors (preliminary results). Also, these interventions improved the adaptive behaviors in autistic children, and parental functions such as self-efficacy, parental style, psychological problems (secondary results). Components of the therapeutic program included the type of consequence-based interventions (such as response interruption and redirection), antecedent-based interventions A (visual cue and daily schedules), antecedent-based interventions B (such as enriching environment with play). Three articles had medium certainty of the evidence, and 6 had high certainty of evidence. Conclusion: The results of this study indicated the positive effect of parent-mediated behavioral intervention on behavioral excesses in children with ASD. Future studies should emphasize the comprehensiveness of all the effective components in the parent-mediated behavioral intervention and the feasibility of the intervention in various contexts. It is suggested that parent-mediated interventions be implemented on behavioral excesses in children with ASD in Iran.

RELATIONSHIP OF ANXIETY AND REPETITIVE BEHAVIORS AMONG AUTISTIC CHILDREN

2020 ◽  
pp. 65-70
Author(s):  
OJS Admin
Keyword(s):  
Autism Spectrum Disorder ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
High Anxiety ◽  
Core Feature ◽  
Relationship Of

Children with Autism Spectrum Disorder are vulnerable to anxiety. Repetitive behaviors are a core feature of Autism Spectrum Disorder (ASD) and have been associated anxiety. This study examined repetitive behaviors and anxiety in two groups of children with autism spectrum disorder, those with high anxiety and those with lower levels of anxiety.

Development of Neural Structure and Function in Autism Spectrum Disorder: Potential Implications for Learning Language

2020 ◽  
Vol 29 (4) ◽  
pp. 1783-1797
Author(s):  
Kelly L. Coburn ◽  
Diane L. Williams
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Development ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Language Intervention ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
Neural Structure ◽  
Complex Information ◽  
And Function

Purpose Neurodevelopmental processes that begin during gestation and continue throughout childhood typically support language development. Understanding these processes can help us to understand the disruptions to language that occur in neurodevelopmental conditions, such as autism spectrum disorder (ASD). Method For this tutorial, we conducted a focused literature review on typical postnatal brain development and structural and functional magnetic resonance imaging, diffusion tensor imaging, magnetoencephalography, and electroencephalography studies of the neurodevelopmental differences that occur in ASD. We then integrated this knowledge with the literature on evidence-based speech-language intervention practices for autistic children. Results In ASD, structural differences include altered patterns of cortical growth and myelination. Functional differences occur at all brain levels, from lateralization of cortical functions to the rhythmic activations of single neurons. Neuronal oscillations, in particular, could help explain disrupted language development by elucidating the timing differences that contribute to altered functional connectivity, complex information processing, and speech parsing. Findings related to implicit statistical learning, explicit task learning, multisensory integration, and reinforcement in ASD are also discussed. Conclusions Consideration of the neural differences in autistic children provides additional scientific support for current recommended language intervention practices. Recommendations consistent with these neurological findings include the use of short, simple utterances; repetition of syntactic structures using varied vocabulary; pause time; visual supports; and individualized sensory modifications.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

Emotion Dysregulation in Autism Spectrum Disorder

2018 ◽  
pp. 282-298
Author(s):  
Emily Neuhaus
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Educational Outcomes ◽  
Emotion Dysregulation ◽  
Research Priorities ◽  
Theoretical Models ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder

Autism spectrum disorder (ASD) is defined by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. Although current diagnostic conceptualizations of ASD do not include emotional difficulties as core deficits, the disorder is associated with emotion dysregulation across the lifespan, with considerable implications for long-term psychological, social, and educational outcomes. The overarching goal of this chapter is to integrate existing knowledge of emotion dysregulation in ASD and identify areas for further investigation. The chapter reviews the prevalence and expressions of emotion dysregulation in ASD, discusses emerging theoretical models that frame emotion dysregulation as an inherent (rather than associated) feature of ASD, presents neurobiological findings and mechanisms related to emotion dysregulation in ASD, and identifies continuing controversies and resulting research priorities.

scholarly journals The Impact of COVID-19 on the Adaptive Functioning, Behavioral Problems, and Repetitive Behaviors of Italian Children with Autism Spectrum Disorder: An Observational Study

Children ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 96
Author(s):  
Martina Siracusano ◽  
Eugenia Segatori ◽  
Assia Riccioni ◽  
Leonardo Emberti Gialloreti ◽  
Paolo Curatolo ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Behavioral Problems ◽  
Adaptive Functioning ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Assessment System ◽  
Spectrum Disorder ◽  
Adaptive Skills ◽  
The Impact

Children with autism spectrum disorder (ASD) and their families have represented a fragile population on which the extreme circumstances of the COVID-19 outbreak may have doubly impaired. Interruption of therapeutical interventions delivered in-person and routine disruption constituted some of the main challenges they had to face. This study investigated the impact of the COVID-19 lockdown on adaptive functioning, behavioral problems, and repetitive behaviors of children with ASD. In a sample of 85 Italian ASD children (mean age 7 years old; 68 males, 17 females), through a comparison with a baseline evaluation performed during the months preceding COVID-19, we evaluated whether after the compulsory home confinement any improvement or worsening was reported by parents of ASD individuals using standardized instruments (Adaptive Behavior Assessment System (Second Edition), Achenbach Child Behavior Checklist, Repetitive Behavior Scale-Revised). No significant worsening in the adaptive functioning, problematic, and repetitive behaviors emerged after the compulsory home confinement. Within the schooler children, clinical stability was found in reference to both adaptive skills and behavioral aspects, whereas within preschoolers, a significant improvement in adaptive skills emerged and was related to the subsistence of web-delivered intervention, parental work continuance, and online support during the lockdown.

scholarly journals Local Protein Translation and RNA Processing of Synaptic Proteins in Autism Spectrum Disorder

2021 ◽  
Vol 22 (6) ◽  
pp. 2811
Author(s):  
Yuyoung Joo ◽  
David R. Benavides
Keyword(s):  
Autism Spectrum Disorder ◽  
Fragile X ◽  
Single Gene ◽  
Protein Translation ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Synaptic Proteins ◽  
Local Translation ◽  
Translation Control

Autism spectrum disorder (ASD) is a heritable neurodevelopmental condition associated with impairments in social interaction, communication and repetitive behaviors. While the underlying disease mechanisms remain to be fully elucidated, dysfunction of neuronal plasticity and local translation control have emerged as key points of interest. Translation of mRNAs for critical synaptic proteins are negatively regulated by Fragile X mental retardation protein (FMRP), which is lost in the most common single-gene disorder associated with ASD. Numerous studies have shown that mRNA transport, RNA metabolism, and translation of synaptic proteins are important for neuronal health, synaptic plasticity, and learning and memory. Accordingly, dysfunction of these mechanisms may contribute to the abnormal brain function observed in individuals with autism spectrum disorder (ASD). In this review, we summarize recent studies about local translation and mRNA processing of synaptic proteins and discuss how perturbations of these processes may be related to the pathophysiology of ASD.

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