Yang Yin-Zhang, Tsai Jia-An, Yu Ya-Lan, Ko Mary Hsin-Ju, Chiou Hung-Yi, Pai Tun-Wen, Chen Hui-Ju
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Division of Pediatric Neurology, Department of Pediatrics, MacKay Children Hospital, Taipei 104217, Taiwan.
Children (Basel). 2025 Feb 28;12(3):310. doi: 10.3390/children12030310.
The objective of this study was to early-detect gross motor abnormalities through video detection in Taiwanese infants aged 2-6 months.
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.
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.
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%.
本研究的目的是通过视频检测对2至6个月大的台湾婴儿进行粗大运动异常的早期检测。
目前婴儿发育迟缓的诊断主要依赖于临床检查。然而，在临床就诊期间，婴儿可能因环境陌生而表现出非典型行为，这可能无法真正反映其真实的发育状况。
本研究使用在婴儿家中环境录制的视频。两名儿科神经科医生对这些视频片段进行人工标注，通过评估婴儿的粗大运动来确定其是否具有粗大运动迟缓的特征。利用迁移学习技术，将包括ViTPose、HRNet、DARK和UDP在内的四种姿态识别模型应用于婴儿粗大运动数据集。使用包括随机森林、支持向量机、逻辑回归和XGBoost在内的四种机器学习分类模型来预测婴儿的发育状况。
姿态估计和跟踪的实验结果表明，ViTPose模型在姿态识别方面表现最佳。共提取并计算了227个与运动学、动作和姿势相关的特征。单因素方差分析显示保留了106个显著特征用于构建预测模型。结果表明，随机森林模型表现最佳，平均F1分数为0.94，加权平均AUC为0.98，平均准确率为94%。
