AI-Powered Assessment of Motor Development: Using Platforms Like KineticAI to Analyze Fundamental Movement Skills in Children.
The aim of this study is to examine the precision, dependability, and relevance of AI-based evaluations in contrast to conventional human evaluations. In all, 200 7–8-year-old students from urban and suburban schools participated in the study. Based on movement speed, accuracy, and smoothness, Kinet...
| Published in: | Perceptual & Motor Skills Vol. 133; no. 2; pp. 276 - 299 |
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| Main Authors: | , , |
| Format: | Article |
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Sage Publications Inc.
Apr2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=191809066&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 191809066 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00315125 PSK jtl: Perceptual & Motor Skills issn: 00315125 maglogo: Y pubinfo: dt: Apr2026 vid: 133 iid: 2 pid: 344 pub: Sage Publications Inc. artinfo: ui: 191809066 10.1177/00315125251357047 ppf: 276 ppct: 23 formats: tig: atl: AI-Powered Assessment of Motor Development: Using Platforms Like KineticAI to Analyze Fundamental Movement Skills in Children. aug: au: Guo, Jing Xuan Zhang, Gao Hua Zhang, You Ming affil: School of Physical Education and Health Science, Mudanjiang Normal University, Mudanjiang, China School of Economics and Management, Wuhan Sports University, Wuhan, China su: Motor ability Suburbs Elementary schools Artificial intelligence Sex distribution Socioeconomic factors Child development Metropolitan areas Psychometrics Analysis of variance Children Multitrait multimethod techniques Random forest algorithms Pearson correlation (Statistics) T-test (Statistics) Research funding Research evaluation Running Questionnaires Descriptive statistics Support vector machines Intraclass correlation Statistical reliability Artificial neural networks Body movement Jumping Machine learning Confidence intervals Data analysis software Sensitivity & specificity (Statistics) Postural balance Physical activity Motion capture (Human mechanics) sug: subj: Motor ability Suburbs Elementary schools Artificial intelligence Sex distribution Socioeconomic factors Child development Metropolitan areas Psychometrics Analysis of variance Children Elementary and Secondary Schools Multitrait multimethod techniques Random forest algorithms Pearson correlation (Statistics) T-test (Statistics) Research funding Research evaluation Running Questionnaires Descriptive statistics Support vector machines Intraclass correlation Statistical reliability Artificial neural networks Body movement Jumping Machine learning Confidence intervals Data analysis software Sensitivity & specificity (Statistics) Postural balance Physical activity Motion capture (Human mechanics) keyword: artificial intelligence child development motor skill assessment movement analysis psychometric validation artificial intelligence child development motor skill assessment movement analysis psychometric validation ab: The aim of this study is to examine the precision, dependability, and relevance of AI-based evaluations in contrast to conventional human evaluations. In all, 200 7–8-year-old students from urban and suburban schools participated in the study. Based on movement speed, accuracy, and smoothness, KineticAI's assessment of their motor skills divided them into three categories: proficiency, developing, and emerging. A thorough examination of KineticAI's validity and reliability was ensured by evaluating its psychometric qualities using COSMIN criteria. Furthermore, AI-generated scores and human evaluator ratings were compared using TGMD-3 as a standard. Mean Absolute Error (MAE), Intraclass Correlation Coefficients (ICC), and Bland-Altman plots were among the statistical techniques used to evaluate the degree of agreement. With an ICC of 0.94, the results show that KineticAI achieves great accuracy and dependability, showing strong consistency with human judgments. With running (3.8), jumping (4.2), hopping (5.1), and balancing (4.9) points, the AI system demonstrated a negligible mean absolute error (MAE) across motor skills, thereby proving its accuracy. Disparities in motor proficiency were also found by gender and school, with suburban girls scoring the lowest and urban boys the highest. These results highlight how crucial it is to provide everyone with fair access to organized physical activity programs to close developmental gaps. The study indicates that KineticAI offers a scalable, objective, and efficient alternative to traditional motor assessments. It is a valuable tool for use in schools, rehabilitation clinics, and sports training programs. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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