Yoga Pose Estimation and Feedback Generation Using Deep Learning

Thoutam, Vivek Anand; Srivastava, Anugrah; Badal, Tapas; Mishra, Vipul Kumar; Sinha, G. R.; Sakalle, Aditi; Bhardwaj, Harshit; Raj, Manish

doi:10.1155/2022/4311350

Cited by 58 publications

(6 citation statements)

References 27 publications

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“…The potential of this approach for improving the alignment and safety of yoga poses was highlighted, and future research in personalized pose detection systems and integration of other sensing modalities for a more comprehensive yoga practice was suggested. Additionally, high accuracy score of 0.9958 was achieved by a multilayer perceptron (MLP) for testing datasets with modified features, with lower power consumption compared to CNN and CNN + LSTM [1]. In July 2022, Dr. Maya Bembde and the team had presented a comprehensive survey of different techniques for detecting yoga poses, emphasizing the importance of correct alignment and the potential risks of incorrect posture.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

Mane,

Upadhye,

Gite

et al. 2023

IJRITCC

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SVM-based Real-time Pose Detection and Correction System refer to a computer system that uses machine learning techniques to detect and correct a person's yoga pose in real-time. This system can act as a virtual yoga assistant, helping people improve their yoga practice by providing immediate feedback on their form and helping to prevent injury. This paper presents a yoga tracker and correction system that uses computer vision and machine learning algorithms to track and correct yoga poses. The system comprises a camera and a computer vision module that captures images of the yoga practitioner and identifies the poses being performed. The machine learning module analyzes the images to provide feedback on the quality of the poses and recommends corrections to improve form and prevent injuries. This paper proposed a customized support vector machine (SVM) based real-time pose detection and correction system that suggests yoga practices based on specific health conditions or diseases. Paper aims to provide a reliable and accessible resource for individuals seeking to use yoga as a complementary approach to managing their health conditions. The system also includes a practitioner’s interface that enables practitioners to receive personalized recommendations for their yoga practice. The system is developed using Python and several open-source libraries, and was tested on a dataset of yoga poses. The hyper parameter gamma tuned to optimize the classification accuracy on our dataset produced 87% which is better than other approaches. The experiment results demonstrate the effectiveness of the system in tracking and correcting yoga poses, and its potential to enhance the quality of yoga practice.

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Section: Literature Surveymentioning

confidence: 99%

Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

Mane,

Upadhye,

Gite

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…The work was further tested on twelve individuals and proved to be highly effective with an accuracy of 98.92%. Thoutam et al [19] too applied a deep learning technique for training the model to recognize yoga poses. After extracting features through Keras multi-purpose pose estimation, classi cation into one of the six poses: corpse pose, tree pose, mountain pose, triangle pose, lotus pose and cobra pose, according to the angles between twelve keypoints was done using Multilayer Perceptron, resulting in an accuracy of 99.58%.…”

Section: Literature Surveymentioning

confidence: 99%

Yoga Pose Monitoring System using Deep Learning

Swain

Satapathy

Patro

et al. 2022

Preprint

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Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to learn and rehearse yoga postures accurately without help from anyone else. Using Deep learning algorithms, an approach has been taken to accurately detect and recognize various yoga poses. The chosen dataset consists of a total of 85 videos with 6 yoga postures performed by 15 participants. Initially, keypoints of the user are extracted using the Mediapipe library. A combination of convolutional neural network (CNN) and long short-term memory (LSTM) has been used for Yoga pose recognition through real-time monitored videos as a deep learning model. CNN layer is used for extraction of features from the keypoints and it is followed by LSTM that understands the occurrence of sequence of frames for predictions to be made. Then, the poses are classified as correct or incorrect. If a correct pose is identified, the system will give the user feedback through text/speech.

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“…A large number of studies in yoga pose classification make use of pose estimation together with deep learning. Some studies propose pose estimation and feedback generation using extraction of body joints (e.g., OpenPose) and finding differences in angles between the expert's and the user's pose [6,7]. An angle-likelihood mechanism is also used by [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of CNN-based methods for yoga pose classification

ATALAY AYDIN

2024

Turkish Journal of Engineering

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Yoga is an exercise developed in ancient India. People perform yoga in order to have mental, physical, and spiritual benefits. While yoga helps build strength in the mind and body, incorrect postures might result in serious injuries. Therefore, yoga exercisers need either an expert or a platform to receive feedback on their performance. Since access to experts is not an option for everyone, a system to provide feedback on the yoga poses is required. To this end, commercial products such as smart yoga mats and smart pants are produced; Kinect cameras, sensors, and wearable devices are used. However, these solutions are either uncomfortable to wear or not affordable for everyone. Nonetheless, a system that employs computer vision techniques is a requirement. In this paper, we propose a deep-learning model for yoga pose classification, which is the first step of a quality assessment system. We introduce a wavelet-based model that first takes wavelet transform of input images. The acquired subbands, i.e., approximation, horizontal, vertical, and diagonal coefficients of the wavelet transform are then fed into separate convolutional neural networks (CNN). The obtained probability results for each group are fused in order to have the final yoga class prediction. A publicly available dataset with 5 yoga poses is used. Since the number of images in the dataset is not enough for a deep learning model, we also perform data augmentation to increase the number of images. We compare our results to a CNN model and the three models that employ the subbands separately. Results obtained using the proposed model outperforms the accuracy output achieved with the compared models.

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Yoga Pose Estimation and Feedback Generation Using Deep Learning

Cited by 58 publications

References 27 publications

Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

Yoga Pose Monitoring System using Deep Learning

Comparison of CNN-based methods for yoga pose classification

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