Enhancing Workout Precision: AI and Trigonometric Ratio-Based Pose Correction

Abstract

In the digital era, the confluence of technology and fitness has taken home workouts to the next level, making it more accessible but often less supervised. This, however, portends the worry of the risk of injury through wrong exercise postures in the absence of professional guidance. Our project, entitled "Enabling Workout Precision: AI and Trigonometric Ratio-Based Pose Correction," solves this problem by introducing a first-of-its-kind system that integrates Human Pose Estimation with both Trigonometric Ratios and deep learning for real-time and precise posture correction while exercising. Two approaches have been implemented in our project for pose estimation correction. The first method uses trigonometric ratios to calculate angles and alignments directly, offering a mathematical and deterministic basis for evaluating posture. The second method employs a deep learning model, specifically a convolutional neural network (CNN), which classifies key exercises like planks, squats, and shoulder presses. This model is trained to identify correct and incorrect workout poses and predicts specific deviations defined as labels in the dataset for each of the exercises. The real-time feedback mechanism helps with the maintenance of the right form, and it further significantly minimizes the risk of injury; hence, it assures a secure and effective home workout experience. This system provides a practical solution to the challenge of supervision in home fitness routines, leveraging great practical application of trigonometry and AI to personal fitness.