In the recent days, most of the people stay in a hunchback position for a long time, due to usage of electronic gadgets like smartphones, personal computers, laptops and tablets, which causes neck and back pain in large numbers, which causes Text neck syndrome, Musculoskeletal disorders, Carpal Tunnel Syndrome and Computer Vision Syndrome. Hence it has become mandatory for people to be mindful of their posture while sitting for long hours. Human pose estimation has gained a lot of attention amongst researchers in a wide range of applications including computer vision, video analytics and motion analysis. To address these risk factors, attempts have been made to develop a 3D Human Pose Estimation (3D-HPE) model for detecting and correcting frontal plane (anterior) sitting postures in computer workstation ergonomics using MediaPipe, and various variants of YOLO, algorithms. The proposed model locates landmarks and analyzes kinematic points from the input video captured through a web camera. From these kinematic points, the 3D-HPE model analyzes whether the human postures are good or bad based on the temporal duration of prolonged time of poor posture. YOLOv8 gives the highest accuracy which is determined by mAP (Mean Average Precision) value found 91.2 in terms of computational time and human pose estimation. Hence, YOLOv8x-pose is the most suited deep learning algorithm for Real-time 3D Human Pose Estimation in Ergonomics. The proposed model notifies the human by sending an alert message to the device for posture correction.

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J. Radhakrishnan , A. Vijayaraghavan, A. Karthik, R. Prasath and M. Arshath “Real-Time 3D Human Pose Estimation Using Deep Learning Model for Ergonomics” International Journal of Technology and Engineering Studies, vol. 8, no. 1, pp. 34–40, 2022.