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
Collection
Citation
Haritha, V.N.; Ummesalma, M., “Yoga Hand Mudra Classification using Zernike Moments,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25911.
