# Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
## 1 Introduction  
Computer vision and artificial intelligence have become indispensable in numerous domains, providing invaluable assistance to individuals. Within the realm of sports and security, computer vision plays a pivotal role in the monitoring, recognition, and analysis of human behavior and motion. Similarly, the monitoring, tracking, and analysis of infant movements are subjects of great significance, bestowing substantial benefits for the management of young children/infants and yielding medical advantages. This technology enables specialists to scrutinize the movements of infants, and identify early warning signs pertaining to motor development and associated disorders. In the analysis of infant movements, methods such as live monitoring or video data analysis are being implemented. In addition, the analysis is implemented through the camera with labeling points directed at the body joints. However, manual labeling of joints per data collection is required for the analysis of infant data, which can be time-consuming and challenging to set up. An alternative approach involves the use of artificial intelligence models for pose estimation. This approach is more convenient and allows for more data to be collected from newborns. Recent research based on deep learningapproaches has laid the foundation for computer-supervised motion assessment tools for early intervention and diagnosis of motor neuronal disorders. The utilization of General Movements Assessment (GMA) [1] has proven instrumental in the differentiation of subtle movements from a myriad of smaller movements, offering an early diagnostic avenue for cerebral palsy. In their work, the authors harnessed postural estimation techniques, notably OpenPose, to reconstruct infant posture and meticulously analyze movements for the purpose of cerebral palsy diagnosis. However, the existing methods, while effective, present significant drawbacks in terms of time consumption and computational expenses. The intricate task of developing a novel, compact model is further complicated by the limited availability of public datasets specifically designed for newborns. This scarcity poses a formidable challenge in the pursuit of advancing the field of movement assessment and diagnosis for infants. In the past decade, impressive progress has been made with the accuracy of pose estimation methods [2; 3; 4; 5; 6; 7]. And studies have shifted their focus to developing methods that enhance the speed of these methods. Despite this, the accuracy-focused methods that rely on complex models consume significant computational resources as well as execution equipment resources. Although methods to improve speed have emerged, they often require trade-offs with accuracy. The majority of pose estimation methods can be broadly categorized into two main approaches: top-down and bottom-up. Most of the top-down methods in reference [8] involve detecting the person in the frame first and then estimating the position of each subject. On the other hand, the bottom-up method in this briefing review [9] detects the human keypoints present in the frame and then connects the relevant keypoints to form a human skeleton. The fastest method with high accuracy has been exported in [10], the authors reported this method running at 23 fps with a graphics card for 3 people images and when the number of people if the number of people increases to 20, it will only run at 15 frames per second. Bottom-up is considered to have better stability in the case of a variable number of human objects or error detection (wrong detection of non-human objects). _Pose estimation:_ The majority of pose estimation methods can be broadly categorized into two main approaches: top-down and bottom-up. The top-down method involves detecting the person in the frame first and then estimating the position of each subject. On the other hand, the bottom-up method detects the human keypoints present in the frame and then connects the relevant keypoints to form a human skeleton.