Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis
Abstract Imitation is considered a fundamental skill for learning in children with autism spectrum disorder (ASD). However, existing assessment methods usually provide a qualitative description of whether children imitate while ignoring individual variability in their imitation ability. Therefore, t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-05-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-05068-4 |
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| author | Rujing Zhang Jingying Chen Xiaodi Liu Yanling Gan Guangshuai Wang |
| author_facet | Rujing Zhang Jingying Chen Xiaodi Liu Yanling Gan Guangshuai Wang |
| author_sort | Rujing Zhang |
| collection | DOAJ |
| description | Abstract Imitation is considered a fundamental skill for learning in children with autism spectrum disorder (ASD). However, existing assessment methods usually provide a qualitative description of whether children imitate while ignoring individual variability in their imitation ability. Therefore, this study proposes a computer-assisted method for the refined assessment of imitation ability in children with ASD. First, 25 children with ASD and 25 age-matched typically developing (TD) children between 60 and 78 months old were recruited to imitate meaningful actions on objects and nonmeaningful gestures. In this process, several quantitative indicators were applied to quantify the children’s imitation ability based on a fine-grained analysis of their visual attention and motor execution patterns. Then, three classic machine-learning algorithms were employed to explore whether the indicators could efficiently classify children with imitation difficulties. The results indicated that the proposed indicators could provide a detailed description of individual differences in imitation impairment. Also, the high classification performance revealed that the proposed indicators could classify children with and without imitation difficulties. In addition, the findings contributed to research regarding the diagnostic evaluation of children with ASD. |
| format | Article |
| id | doaj-art-da85bd177ad04da08bc55b711e040a10 |
| institution | OA Journals |
| issn | 2662-9992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj-art-da85bd177ad04da08bc55b711e040a102025-08-20T02:39:03ZengSpringer NatureHumanities & Social Sciences Communications2662-99922025-05-0112111010.1057/s41599-025-05068-4Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysisRujing Zhang0Jingying Chen1Xiaodi Liu2Yanling Gan3Guangshuai Wang4Faculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal UniversityFaculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal UniversityFaculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal UniversitySchool of Computer Science and Engineering, Guangxi Normal UniversityFaculty of Artificial Intelligence in Education, National Engineering Research Center for E-Learning, Central China Normal UniversityAbstract Imitation is considered a fundamental skill for learning in children with autism spectrum disorder (ASD). However, existing assessment methods usually provide a qualitative description of whether children imitate while ignoring individual variability in their imitation ability. Therefore, this study proposes a computer-assisted method for the refined assessment of imitation ability in children with ASD. First, 25 children with ASD and 25 age-matched typically developing (TD) children between 60 and 78 months old were recruited to imitate meaningful actions on objects and nonmeaningful gestures. In this process, several quantitative indicators were applied to quantify the children’s imitation ability based on a fine-grained analysis of their visual attention and motor execution patterns. Then, three classic machine-learning algorithms were employed to explore whether the indicators could efficiently classify children with imitation difficulties. The results indicated that the proposed indicators could provide a detailed description of individual differences in imitation impairment. Also, the high classification performance revealed that the proposed indicators could classify children with and without imitation difficulties. In addition, the findings contributed to research regarding the diagnostic evaluation of children with ASD.https://doi.org/10.1057/s41599-025-05068-4 |
| spellingShingle | Rujing Zhang Jingying Chen Xiaodi Liu Yanling Gan Guangshuai Wang Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis Humanities & Social Sciences Communications |
| title | Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis |
| title_full | Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis |
| title_fullStr | Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis |
| title_full_unstemmed | Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis |
| title_short | Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis |
| title_sort | towards a computer assisted assessment of imitation in children with autism spectrum disorder based on a fine grained analysis |
| url | https://doi.org/10.1057/s41599-025-05068-4 |
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