Classification of yoga pose using machine learning techniques

Palanimeera, J.; Ponmozhi, K.

doi:10.1016/j.matpr.2020.08.700

Cited by 29 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study focused on classifying sun salutation yoga poses using machine learning techniques, achieving a remarkable accuracy of 96% with the KNN model. This research highlighted the potential of machine learning models in yoga pose classification when combined with pose estimation algorithms [17]. Additionally, a deep learning-based approach for yoga pose classification was presented at the 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, underscoring the applicability of deep neural networks in this domain [18].…”

Section: Introductionmentioning

confidence: 90%

Yoga Pose Estimation Using Angle-Based Feature Extraction

Borthakur,

Paul,

Kapil

et al. 2023

Healthcare

View full text Add to dashboard Cite

Objective: This research addresses the challenges of maintaining proper yoga postures, an issue that has been exacerbated by the COVID-19 pandemic and the subsequent shift to virtual platforms for yoga instruction. This research aims to develop a mechanism for detecting correct yoga poses and providing real-time feedback through the application of computer vision and machine learning (ML) techniques. Methods and Procedures: This study utilized computer vision-based pose estimation methods to extract features and calculate yoga pose angles. A variety of models, including extremely randomized trees, logistic regression, random forest, gradient boosting, extreme gradient boosting, and deep neural networks, were trained and tested to classify yoga poses. Our study employed the Yoga-82 dataset, consisting of many yoga pose images downloaded from the web. Results: The results of this study show that the extremely randomized trees model outperformed the other models, achieving the highest prediction accuracy of 91% on the test dataset and 92% in a fivefold cross-validation experiment. Other models like random forest, gradient boosting, extreme gradient boosting, and deep neural networks achieved accuracies of 90%, 89%, 90%, and 85%, respectively, while logistic regression underperformed, having the lowest accuracy. Conclusion: This research concludes that the extremely randomized trees model presents superior predictive power for yoga pose recognition. This suggests a valuable avenue for future exploration in this domain. Moreover, the approach has significant potential for implementation on low-powered smartphones with minimal latency, thereby enabling real-time feedback for users practicing yoga at home.

show abstract

Section: Introductionmentioning

confidence: 90%

Yoga Pose Estimation Using Angle-Based Feature Extraction

Borthakur,

Paul,

Kapil

et al. 2023

Healthcare

View full text Add to dashboard Cite

show abstract

“…In Rishan et al, (2020) [14], They created a system that can recognize six distinct yoga poses utilizing the BODY 25 dataset, the OpenPose module to identify 25 important points in the human body, and a pose recognition part which contains the basic Deep Learning models. The main goal of Palanimeera et al, (2021) [15], They primarily used a sun salutation dataset. Four machine learning models are utilized to identify the postures (asanas), and skeleton sketching in real-time using a posture estimate approach for a person's body.…”

Section: Literature Reviewmentioning

confidence: 99%

Yoga Pose prediction using Transfer Learning Based Neural Networks

Maddukuri

Ummity

2023

Preprint

View full text Add to dashboard Cite

The rapid increase of the technology made human to work harder irrespective of the body condition. This has caused many people with mental pressure and lack of body fitness. Covid – 19 has also shown the serious effect on human mental health. Therefore, many physicians suggested to practice yoga for breathing and physical fitness. But it is not possible for all people to come to fitness centers in lockdown, tight work schedule, staying far from fitness centers etc. So, this research uses transfer learning approach on convolutional neural networks in order to predict the yoga posture made by the person in real – time. 85 different types of yoga postures dataset is constructed by web scrapping and by capturing the images which is more compared to existing works. Training, validation and test set were divided in the ration 70:10:20 respectively. The pre-trained models like EfficientNet – B0, Xception, ResNet – 50 and MobileNet were chosen based on their past performances and were trained on the constructed yoga dataset. The experimental results shows that, Xception model using transfer learning gave the best results with the testing accuracy of 95.67% and also second best in execution time.

show abstract

“…[ 5 ] The system used deep learning techniques for the classification of yoga poses. In traditional machine learning, [ 22 ] models require extracted features and engineering, but deep learning understands data and extracts features. [ 2 ] A self-instructed system is built for the yoga pose using star skeleton computation.…”

Section: Related Workmentioning

confidence: 99%

Yoga Pose Estimation and Feedback Generation Using Deep Learning

Thoutam

Srivastava

Badal

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Yoga is a 5000-year-old practice developed in ancient India by the Indus-Sarasvati civilization. The word yoga means deep association and union of mind with the body. It is used to keep both mind and body in equilibration in all flip-flops of life by means of asana, meditation, and several other techniques. Nowadays, yoga has gained worldwide attention due to increased stress levels in the modern lifestyle, and there are numerous methods or resources for learning yoga. Yoga can be practiced in yoga centers, through personal tutors, and can also be learned on one’s own with the help of the Internet, books, recorded clips, etc. In fast-paced lifestyles, many people prefer self-learning because the abovementioned resources might not be available all the time. But in self-learning, one may not find an incorrect pose. Incorrect posture can be harmful to one’s health, resulting in acute pain and long-term chronic concerns. In this paper, deep learning-based techniques are developed to detect incorrect yoga posture. With this method, the users can select the desired pose for practice and can upload recorded videos of their yoga practice pose. The user pose is sent to train models that output the abnormal angles detected between the actual pose and the user pose. With these outputs, the system advises the user to improve the pose by specifying where the yoga pose is going wrong. The proposed method was compared to several state-of-the-art methods, and it achieved outstanding accuracy of 0.9958 while requiring less computational complexity.

show abstract

Classification of yoga pose using machine learning techniques

Cited by 29 publications

References 4 publications

Yoga Pose Estimation Using Angle-Based Feature Extraction

Yoga Pose Estimation Using Angle-Based Feature Extraction

Yoga Pose prediction using Transfer Learning Based Neural Networks

Yoga Pose Estimation and Feedback Generation Using Deep Learning

Contact Info

Product

Resources

About