Yoga in Digital Age: A Hybrid Approach Using ML and Computer Vision

In today's fast-paced world, stress and pressure affect people of all ages, both physically and mentally. Yoga has emerged as a highly sought-after solution due to its numerous benefits. In the present technology-dependent era of digital age, it's only natural that we turn to technology for assistance in their yoga journey. This is where the proposed article comes into play, serving as one's digital Yoga Guru. In this paper, seven classifiers, out of which six are Machine Learning classifiers namely Support Vector Machine, Naïve Bayes, Random Forest, K-Nearest Neighbor, Logistic Regression, and Ensembler are employed to classify the dataset. These classifiers exhibit a promising average accuracy exceeding 90%. Additionally, the proposed method incorporates a Deep Learning model, specifically the Multi-Perceptron Neural Network, which has been trained to predict outputs with an impressive accuracy of 92%. This Paper utilizes the power of MediaPipe, a cutting-edge technology that plays a pivotal role in this innovative approach to yoga. MediaPipe is capable of identifying individuals on the screen and provides 33 key-points, including critical markers like facial key-points, and main key-points of the body such as shoulders, elbows, knees, feet, wrists, and etc. These key points are essential for pose recognition, and assessing the correctness of yoga postures. What sets the proposed method apart is its real-time, live tracing feature, boasting an impressive accuracy rate of 99%. As one engages in yoga practice, this technology tracks their movements, and compares them to the ideal yoga postures. This immediate feedback ensures that one performs each pose correctly, promoting better results and minimizing the risk of injury. In essence, the proposed method bridges the gap between ancient yoga wisdom and modern technology, offering a digital Yoga Guru that guides one to achieve physical and mental well-being with precision and confidence.