Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants
Objective: The objective of this study was to early-detect gross motor abnormalities through video detection in Taiwanese infants aged 2–6 months. Background: The current diagnosis of infant developmental delays primarily relies on clinical examinations. However, during clinical visits, infants may...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Children |
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| Online Access: | https://www.mdpi.com/2227-9067/12/3/310 |
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| author | Yin-Zhang Yang Jia-An Tsai Ya-Lan Yu Mary Hsin-Ju Ko Hung-Yi Chiou Tun-Wen Pai Hui-Ju Chen |
| author_facet | Yin-Zhang Yang Jia-An Tsai Ya-Lan Yu Mary Hsin-Ju Ko Hung-Yi Chiou Tun-Wen Pai Hui-Ju Chen |
| author_sort | Yin-Zhang Yang |
| collection | DOAJ |
| description | Objective: The objective of this study was to early-detect gross motor abnormalities through video detection in Taiwanese infants aged 2–6 months. Background: The current diagnosis of infant developmental delays primarily relies on clinical examinations. However, during clinical visits, infants may show atypical behaviors due to unfamiliar environments, which might not truly reflect their true developmental status. Methods: This study utilized videos of infants recorded in their home environments. Two pediatric neurologists manually annotated these clips to identify whether an infant possessed the characteristics of gross motor delays through an assessment of his/her gross motor movements. Using transfer learning techniques, four pose recognition models, including ViTPose, HRNet, DARK, and UDP, were applied to the infant gross motor dataset. Four machine learning classification models, including random forest, support vector machine, logistic regression, and XGBoost, were used to predict the developmental status of infants. Results: The experimental results of pose estimation and tracking indicate that the ViTPose model provided the best performance for pose recognition. A total of 227 features related to kinematics, motions, and postures were extracted and calculated. A one-way ANOVA analysis revealed 106 significant features that were retained for constructing prediction models. The results show that a random forest model achieved the best performance with an average F1-score of 0.94, a weighted average AUC of 0.98, and an average accuracy of 94%. |
| format | Article |
| id | doaj-art-4abd48ec297343cf911db73da94426db |
| institution | OA Journals |
| issn | 2227-9067 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj-art-4abd48ec297343cf911db73da94426db2025-08-20T02:11:25ZengMDPI AGChildren2227-90672025-02-0112331010.3390/children12030310Automatic Movement Recognition for Evaluating the Gross Motor Development of InfantsYin-Zhang Yang0Jia-An Tsai1Ya-Lan Yu2Mary Hsin-Ju Ko3Hung-Yi Chiou4Tun-Wen Pai5Hui-Ju Chen6Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDivision of Pediatric Neurology, Department of Pediatrics, MacKay Children Hospital, Taipei 104217, TaiwanDepartment of Pediatric Neurology, Hsinchu Municipal MacKay Children Hospital, Hsinchu 300195, TaiwanInstitute of Population Health Science, National Health Research Institute, Miaoli County 350401, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDivision of Pediatric Neurology, Department of Pediatrics, MacKay Children Hospital, Taipei 104217, TaiwanObjective: The objective of this study was to early-detect gross motor abnormalities through video detection in Taiwanese infants aged 2–6 months. Background: The current diagnosis of infant developmental delays primarily relies on clinical examinations. However, during clinical visits, infants may show atypical behaviors due to unfamiliar environments, which might not truly reflect their true developmental status. Methods: This study utilized videos of infants recorded in their home environments. Two pediatric neurologists manually annotated these clips to identify whether an infant possessed the characteristics of gross motor delays through an assessment of his/her gross motor movements. Using transfer learning techniques, four pose recognition models, including ViTPose, HRNet, DARK, and UDP, were applied to the infant gross motor dataset. Four machine learning classification models, including random forest, support vector machine, logistic regression, and XGBoost, were used to predict the developmental status of infants. Results: The experimental results of pose estimation and tracking indicate that the ViTPose model provided the best performance for pose recognition. A total of 227 features related to kinematics, motions, and postures were extracted and calculated. A one-way ANOVA analysis revealed 106 significant features that were retained for constructing prediction models. The results show that a random forest model achieved the best performance with an average F1-score of 0.94, a weighted average AUC of 0.98, and an average accuracy of 94%.https://www.mdpi.com/2227-9067/12/3/310developmental delaypose estimationtransfer learningmachine learning |
| spellingShingle | Yin-Zhang Yang Jia-An Tsai Ya-Lan Yu Mary Hsin-Ju Ko Hung-Yi Chiou Tun-Wen Pai Hui-Ju Chen Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants Children developmental delay pose estimation transfer learning machine learning |
| title | Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants |
| title_full | Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants |
| title_fullStr | Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants |
| title_full_unstemmed | Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants |
| title_short | Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants |
| title_sort | automatic movement recognition for evaluating the gross motor development of infants |
| topic | developmental delay pose estimation transfer learning machine learning |
| url | https://www.mdpi.com/2227-9067/12/3/310 |
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