Real-time Conventional Deadlift Posture Correction System Using Mediapipe

This paper proposes a real-time, stage-based posture correction system for conventional deadlift exercises, aiming to prevent injuries through predictive movement analysis and immediate corrective feedback. The novelty of this work lies in its ability to continuously monitor joint-specific angles across distinct deadlift stages—Set-Up, Lifting, and Lock-Out—and proactively detect improper postures before they result in incorrect execution or potential injury. The system leverages MediaPipe and OpenCV to extract 33 body keypoints and analyzes critical joint angles involving the shoulders, hips, knees, ankles, and trunk to ensure proper alignment and maintenance of a neutral spine throughout the exercise. A rule-based angle evaluation framework is applied in real time, triggering multi-modal feedback—including visual indicators, textual instructions, and audio guidance via text-to-speech—whenever predefined safety thresholds are exceeded. Additionally, the system tracks exercise repetitions and classifies each repetition as correct or incorrect based on joint-specific criteria, enabling users to quantitatively monitor training quality. The system operates at an average frame rate of 24–30 FPS on a standard CPU, ensuring smooth real-time performance without specialized hardware. Experimental evaluations were conducted using both real exercise performances validated by a professional trainer and multiple professional deadlift training videos under diverse conditions, including varying lighting, complex backgrounds, and crowded scenes. Results demonstrate the system’s effectiveness in detecting stage-specific posture errors and delivering timely, actionable feedback. Identified limitations include sensitivity to camera misalignment, keypoint occlusions, and variations in body types, which are addressed as directions for future improvement. Overall, the proposed system offers a practical and accessible solution for predictive deadlift posture correction, enhancing exercise safety and effectiveness in real-world training environments.