AI-Driven Weightlifting Posture Analysis Pipeline
The Narrative
Athletes often lack access to professional biomechanical analysis, which is critical for preventing injury and optimizing power delivery in complex movements like the Snatch and Clean & Jerk.
Developed an automated pipeline that extracts 2D/3D keypoints using MediaPipe, computes joint kinematics, and uses a GRU (Gated Recurrent Unit) network to analyze movement sequences and generate actionable feedback.
Key Features
Automated Kinematic Extraction
End-to-end processing from frame extraction to joint angle and velocity computation for precise movement mapping.
GRU Sequence Analysis
Implemented a multi-layer GRU regression model to analyze the temporal dynamics of weightlifting phases.
Biomechanical Feedback
Automated rule-based and ML-driven feedback system that identifies posture flaws in real-time.
Multi-View Data Synthesis
Orchestrated quintuplet-based data sets combining front, side, and top views for holistic movement analysis.
Engineering
// SYSTEM_OVERVIEW
Deep learning sequence model integrated with a custom computer vision pre-processing pipeline.
01_MODULE: MODEL ARCHITECTURE & TRAINING
The system utilizes a Sequential model with stacked GRU layers, BatchNormalization, and Dropout to achieve high accuracy in posture regression while preventing overfitting on limited biomechanical datasets.
02_MODULE: FEATURE ENGINEERING PIPELINE
Built custom modules for keypoint normalization relative to hip distance and automated cropping to ensure input consistency across varying lighting and backgrounds.