Use of Keypoint-RCNN and YOLOv7 for Capturing Biomechanics and Barbell Trajectory in Weightlifting

Weightlifting is a demanding sport requiring power, flexibility, and the correct technique. The snatch and the clean and jerk involve the fast lifting of weight to an overhead position. Incorrect technique or posture may lead to inefficient lifts or even injury. This paper presents a new framework for biomechanics analysis and barbell trajectory tracking in weightlifting by leveraging the capabilities of Keypoint-RCNN and YOLOv7 deep learning models. The proposed framework extracts skeletal information from weightlifting video sequences using a pre-trained Keypoint-RCNN model for human pose estimation and a custom YOLOv7 model to detect and track barbell trajectories. The Keypoint-RCNN model estimates human pose without manual annotation or specialised apparatus, while the YOLOv7 model provides real-time, non-intrusive barbell tracking. The efficacy of barbell trajectory tracking with YOLOv7 on a public weightlifting dataset of 973 images (70–30 train-test ratio) was evaluated, obtaining high precision (0.9214), recall (0.9678), and mAP@0.5 of 0.9792 and mAP@0.5:0.95 of 0.7765, indicating the applicability of this model to weight training applications. The proposed framework presents a cost-effective, user-friendly, and easily accessible alternative to conventional motion capture and analysis systems, making it accessible for lifters of all skill levels and training environments.