| Paper ID | BIO-13.2 |
| Paper Title |
HIERARCHICAL POSE CLASSIFICATION FOR INFANT ACTION ANALYSIS AND MENTAL DEVELOPMENT ASSESSMENT |
| Authors |
Zhongyu Jiang, Jianxiong Zhou, University of Washington, United States; Jang-Hee Yoo, Electronics and Telecommunications Research Institute (ETRI), South Korea; Jenq-Neng Hwang, University of Washington, United States |
| Session | BIO-13: Deep Learning for Biomedical Applications |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-MIA] Medical image analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Based on Alberta Infant Motor Scale (AIMS), a questionnaire that tracks an infant's motor function, an infant's mental development can be evaluated by recording poses a baby can achieve. Therefore, it is meaningful to propose a systematic image-based pose classifier to classify infant actions based on AIMS to provide early diagnosis of a potential developmental disorder such as Autism. This paper presents a hierarchical pose classifier, given a baby image frame that combines the benefits of 3D human pose estimation and scene context information. Due to privacy policies, we cannot collect enough real infant images/videos for experiments. Instead, we generate synthetic baby images with the help of the Skinned Multi-Infant Linear (SMIL) model. Images are first fed into a ResNet-50 for coarse-level pose classification. A stacked hourglass CNN and a hierarchical 3D pose estimation scheme are used for 2D/3D pose estimation. Finally, an innovative Hierarchical Infant Pose Classifier (HIPC) takes the estimated 3D keypoints and coarse-level pose classification confidence scores to give the fine-level baby pose classification results. Our experimental results show that our hierarchical pose classifier achieves accurate and stable performance on infant pose recognition. |
