Three-Dimensional Pose Estimation of Infants Lying Supine Using Data From a Kinect Sensor With Low Training Cost

Early diagnosis of cerebral palsy in infants has produced promising results using tools like the General Movement Assessment (GMA). Pose estimation of infants lying supine is an important step towards an automated system for GMA. Developing methods for accurate, reliable, fast estimation of the three-dimensional (3D) position of human limbs and proposing motion features suitable for 3D models to classify typical and atypical movements are attracting increasing research interest lately. In this study, we propose a 3D pose estimation method with low training cost suitable for infants in lying positions. The method uses an existing two-dimensional human body keypoint detection method combined with the in-depth information in Red-Green-Blue Depth (RGB-D) data from a Kinect sensor. The method is evaluated using the Moving INfants In RGB-D (MINI-RGBD) open dataset. The results show that the average error of the estimated body part length is 13.76 mm, while the accuracy of the Percentage of Correctly-localized Parts (PCP) and Percentage of Correct Keypoint (PCK) is 80.7 and 86.1%, respectively. The results are comparable to those achieved in the baseline study performed by the researchers who generated this open dataset. The advantage of our method is its low training cost.