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Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants
Yin-Zhang Yang1, Jia-An Tsai1, Ya-Lan Yu2
1Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Insights
This study developed a video analysis method for early detection of gross motor delays in infants. Machine learning models accurately identified developmental abnormalities from home videos, achieving 94% accuracy.
Area of Science:
- Pediatric neurology
- Machine learning applications in healthcare
- Infant motor development assessment
Background:
- Current infant developmental delay diagnosis relies on clinical exams, which can be influenced by unfamiliar environments.
- Infants may exhibit atypical behaviors in clinical settings, potentially misrepresenting their true developmental status.
Purpose of the Study:
- To develop and validate a video-based method for early detection of gross motor abnormalities in Taiwanese infants aged 2-6 months.
- To leverage machine learning and pose recognition for objective assessment of infant motor development.
Main Methods:
- Utilized home-recorded infant videos for analysis.
- Employed two pediatric neurologists for manual annotation of gross motor delays.
- Applied transfer learning with ViTPose, HRNet, DARK, and UDP for pose recognition.
- Trained random forest, support vector machine, logistic regression, and XGBoost models for developmental status prediction.
Main Results:
- ViTPose demonstrated superior performance in pose recognition.
- 227 kinematic, motion, and posture features were extracted, with 106 identified as significant.
- The random forest model achieved the highest performance: 0.94 F1-score, 0.98 weighted average AUC, and 94% accuracy.
Conclusions:
- Video analysis combined with machine learning offers a promising approach for early and accurate detection of gross motor delays in infants.
- Home-based video assessment can provide a more reliable reflection of infant development compared to clinical visits.
- The developed model demonstrates high efficacy in identifying infants requiring further developmental evaluation.
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%.
