Implementation of a Novel Gesture Recognition Technique for Real-Time Exercise Motion Detection

Abstract

Human gesture and motion recognition systems have garnered major attention in recent years owing to their potential applications in fitness tracking, rehabilitation, and sports performance analysis. This research presents the implementation of a novel technique for the real-time detection and recognition of human gestures, specifically focusing on lower-body exercises such as squats. The proposed method leverages deep learning models combined with computer vision and motion capture technologies to accurately distinguish between correct and incorrect exercise forms. The system is trained using a dataset comprising annotated video recordings of various squat exercises, with key body landmarks extracted to track joint movements and detect posture anomalies. The core of the proposed technique involves a machine learning-based classification model that analyses the temporal and spatial features of human movement, providing corrective feedback to users. Gauge the model's performance utilising standard metrics like accuracy, and precision, and recall, along with F1-score, achieving an impressive accuracy rate of 97%, with high precision (95%), and recall (96%), and F1-score (95.5%). Moreover, a confusion matrix along with classification report are generated to gauge the model's effectiveness in distinguishing between correct and incorrect squat forms. This research adds to human motion detection by offering a robust, accurate, and scalable solution for real-time exercise correction, with potential applications in both fitness and rehabilitation domains.