Yoga Hand Mudra Classification using Zernike Moments

This study introduces a novel approach to classifying yoga hand mudras using Zernike Moments, emphasizing their relevance in balancing the Tridoshas—Vata, Pitta, and Kapha, as per Ayurvedic philosophy. A dataset of 1,200 images was collected from yoga practitioners in Bangalore, representing six mudras in both correct ("RIGHT") and incorrect ("WRONG") positions, ensuring a balanced distribution. Zernike Moments were used to extract rotation-invariant shape features from the images. However, due to their lack of scale invariance, Scale-Adaptive Zernike Moments (SAZM) were introduced by incorporating a scaling factor to normalize object size. Image preprocessing involved resizing, Gaussian blur for noise reduction, and normalization. Feature extraction was followed by labeling, scaling, and classification using a Support Vector Machine (SVM). Comparative analysis showed that standard Zernike Moments achieved an accuracy of 55.32%, serving as the baseline. In contrast, SAZM significantly improved classification accuracy to 71.01%, highlighting the importance of scale invariance in gesture recognition. This work demonstrates how computational techniques can complement traditional knowledge, offering a promising direction for yoga-based wellness solutions. While SAZM showed superior performance, future work will address challenges like complex transformations to enhance model accuracy and applicability.