Yoga Hand Mudra Classification using Zernike Moments

Title

Yoga Hand Mudra Classification using Zernike Moments

Creator

Haritha, V.N.; Ummesalma, M.

Description

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. 2025 IEEE.

Source

2025 International Conference on Biomedical Engineering and Sustainable Healthcare, ICBMESH 2025 - Proceedings;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Computer vision; Feature extraction; Gesture recognition; Image classification; Pattern recognition; Scale-invariant features; Support vector machines (SVM); Zernike moments

Coverage

Haritha V.N., CHRIST University, Department of Data Science and Statistics, Bangalore, India; Ummesalma M., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833150207-2;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICBMESH66209.2025.11182008

https://www.scopus.com/pages/publications/105021645153?origin=resultslist