Decreased wrist rotation imitation abilities in children with autism spectrum disorder
IntroductionWhile meaningless gross motor imitation (GMI) is a common challenge for children diagnosed with autism spectrum disorder (ASD), this topic has not attracted much attention and few appropriate test paradigms have been developed.MethodsThe current study proposed a wrist rotation imitation...
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| Format: | Article |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1349879/full |
| _version_ | 1797201581093945344 |
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| author | Fulin Liu Kai Qiu Hongan Wang Yuhong Dong Dongchuan Yu Dongchuan Yu Dongchuan Yu |
| author_facet | Fulin Liu Kai Qiu Hongan Wang Yuhong Dong Dongchuan Yu Dongchuan Yu Dongchuan Yu |
| author_sort | Fulin Liu |
| collection | DOAJ |
| description | IntroductionWhile meaningless gross motor imitation (GMI) is a common challenge for children diagnosed with autism spectrum disorder (ASD), this topic has not attracted much attention and few appropriate test paradigms have been developed.MethodsThe current study proposed a wrist rotation imitation (WRI) task (a meaningless GMI assignment), and established a WRI ability evaluation system using low-cost wearable inertial sensors, which acquired the simultaneous data of acceleration and angular acceleration during the WRI task. Three metrics (i.e., total rotation time, rotation amplitude, and symmetry) were extracted from those data of acceleration and angular acceleration, and then were adopted to construct classifiers based on five machine learning (ML) algorithms, including k-nearest neighbors, linear discriminant analysis, naive Bayes, support vector machines, and random forests. To illustrate our technique, this study recruited 49 ASD children (aged 3.5-6.5 years) and 59 age-matched typically developing (TD) children.ResultsFindings showed that compared with TD children, those with ASD may exhibit shorter total rotation time, lower rotation amplitude, and weaker symmetry. This implies that children with ASD might exhibit decreased WRI abilities. The classifier with the naive Bayes algorithm outperformed than other four algorithms, and achieved a maximal classification accuracy of 88% and a maximal AUC value of 0.91. Two metrics (i.e., rotation amplitude and symmetry) had high correlations with the gross and fine motor skills [evaluated by Gesell Developmental Schedules-Third Edition and Psychoeducational Profile-3 (PEP-3)]. While, the three metrics had no significant correlation with the visual-motor imitation abilities (evaluated by the subdomain of PEP-3) and the ASD symptom severity [evaluated by the Childhood Autism Rating Scale (CARS)] .DiscussionThe strengths of this study are associated with the low-cost measurement system, correlation between the WRI metrics and clinical measures, decreased WRI abilities in ASD, and high classification accuracy. |
| first_indexed | 2024-04-24T07:49:49Z |
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| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-04-24T07:49:49Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-305d0983e36547a88f04a4c9181055ea2024-04-18T13:26:12ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402024-04-011510.3389/fpsyt.2024.13498791349879Decreased wrist rotation imitation abilities in children with autism spectrum disorderFulin Liu0Kai Qiu1Hongan Wang2Yuhong Dong3Dongchuan Yu4Dongchuan Yu5Dongchuan Yu6Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaHenan Provincial Medical Key Lab of Language Rehabilitation for Children, Sanmenxia Center Hospital, Sanmenxia, Henan, ChinaKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaHenan Provincial Medical Key Lab of Child Developmental Behavior and Learning, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaHenan Provincial Engineering Research Center of Children’s Digital Rehabilitation, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaIntroductionWhile meaningless gross motor imitation (GMI) is a common challenge for children diagnosed with autism spectrum disorder (ASD), this topic has not attracted much attention and few appropriate test paradigms have been developed.MethodsThe current study proposed a wrist rotation imitation (WRI) task (a meaningless GMI assignment), and established a WRI ability evaluation system using low-cost wearable inertial sensors, which acquired the simultaneous data of acceleration and angular acceleration during the WRI task. Three metrics (i.e., total rotation time, rotation amplitude, and symmetry) were extracted from those data of acceleration and angular acceleration, and then were adopted to construct classifiers based on five machine learning (ML) algorithms, including k-nearest neighbors, linear discriminant analysis, naive Bayes, support vector machines, and random forests. To illustrate our technique, this study recruited 49 ASD children (aged 3.5-6.5 years) and 59 age-matched typically developing (TD) children.ResultsFindings showed that compared with TD children, those with ASD may exhibit shorter total rotation time, lower rotation amplitude, and weaker symmetry. This implies that children with ASD might exhibit decreased WRI abilities. The classifier with the naive Bayes algorithm outperformed than other four algorithms, and achieved a maximal classification accuracy of 88% and a maximal AUC value of 0.91. Two metrics (i.e., rotation amplitude and symmetry) had high correlations with the gross and fine motor skills [evaluated by Gesell Developmental Schedules-Third Edition and Psychoeducational Profile-3 (PEP-3)]. While, the three metrics had no significant correlation with the visual-motor imitation abilities (evaluated by the subdomain of PEP-3) and the ASD symptom severity [evaluated by the Childhood Autism Rating Scale (CARS)] .DiscussionThe strengths of this study are associated with the low-cost measurement system, correlation between the WRI metrics and clinical measures, decreased WRI abilities in ASD, and high classification accuracy.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1349879/fullautism spectrum disordermeaningless gross motor imitationwrist rotation imitationinertial sensormachine learningclassifier |
| spellingShingle | Fulin Liu Kai Qiu Hongan Wang Yuhong Dong Dongchuan Yu Dongchuan Yu Dongchuan Yu Decreased wrist rotation imitation abilities in children with autism spectrum disorder Frontiers in Psychiatry autism spectrum disorder meaningless gross motor imitation wrist rotation imitation inertial sensor machine learning classifier |
| title | Decreased wrist rotation imitation abilities in children with autism spectrum disorder |
| title_full | Decreased wrist rotation imitation abilities in children with autism spectrum disorder |
| title_fullStr | Decreased wrist rotation imitation abilities in children with autism spectrum disorder |
| title_full_unstemmed | Decreased wrist rotation imitation abilities in children with autism spectrum disorder |
| title_short | Decreased wrist rotation imitation abilities in children with autism spectrum disorder |
| title_sort | decreased wrist rotation imitation abilities in children with autism spectrum disorder |
| topic | autism spectrum disorder meaningless gross motor imitation wrist rotation imitation inertial sensor machine learning classifier |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1349879/full |
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