A Survey on Yogic Posture Recognition

Rajendran, Arun Kumar; Sethuraman, Sibi Chakkaravarthy

doi:10.1109/access.2023.3240769

Cited by 18 publications

(14 citation statements)

References 153 publications

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“…How to potentially detect a broad range of yogic postures performed by multiple persons and generalizing the method for use in the real world in Yogic Posture Recognition (Rajendran & Sethuraman, 2023). The possibility of testing techniques capable of separating an image from its background automatically using instance segmentation (Mask-RCNN) or classifying with superpixels (Sant'Ana et al, 2021).…”

Section: Imminent Trends and Challengesmentioning

confidence: 99%

Artificial Intelligence for Computer Vision: Bibliometric Analysis

Oke,

Cavus

2024

Preprint

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Computer Vision (CV) is a prominent area of focus in Artificial Intelligence (AI) research, with applications ranging from self-driving cars to medical imaging. A bibliometric analysis is presented in this study for the latest research in AI for CV, focusing on advancements in CV models, their evaluation, and their use in various applications from 1981 to 2023 using Web of Science Clarivate Core Collection database and a dataset of 1857 retrieved publication. VOS viewer and CiteSpace software were implemented to perform science mappings and bibliometric analysis techniques in the study. Hence, analysing citation networks, publication output, and collaboration patterns in the field to identify influential research publications, researchers, and institutions. The analysis reveals the top publications and researchers in the field, as well as the most common research topics and their relative importance. This study finds that deep learning techniques, such as convolutional neural networks (CNNs), are the dominant approach in CV research, with applications in object detection, feature extraction, and image analysis. Also, it found that USA has a wide partnership and collaborative range amongst making it the most productive country. This study also discussed few of the challenges and opportunities in AI for CV, including U-Net not generating more precise segmentation in biomedical image segmentation. The recommendation of this study for future research direction is the need for more interdisciplinary collaboration, the development of new evaluation techniques, and the exploration of novel applications of AI for CV. The bibliometric analysis results will be of interest to researchers, practitioners, and policymakers interested in AI, CV, and related fields, as well as anyone interested in the latest advancements in this exciting and rapidly evolving field.

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Section: Imminent Trends and Challengesmentioning

confidence: 99%

Artificial Intelligence for Computer Vision: Bibliometric Analysis

Oke,

Cavus

2024

Preprint

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“…However, the yogic concept states that “disease is considered to be the absence of vibrant health” [ 22 ]. When an individual practices yoga daily, there is the development of a yogic attitude (patience, persistent practice, overcoming obstacles within the self, such as trouncing laziness, anger, anxiety, and desire for being unique from others) causing several physiological changes which are found to be beneficial to health [ 30 ]. India is gifted with the valuable treasure of ancient knowledge in several domains.…”

Section: Background Of Yogamentioning

confidence: 99%

“…Similarly, some of the surveys focused on the general background along with specific application domains, such as yoga for COVID-19 [ 23 ], asthma [ 24 ], cancer [ 25 , 26 ], migraine [ 27 ], cardiovascular disease [ 28 ], and enhancing immunity [ 29 ], as shown in Table 1 . All those surveys focused on general background with applications on human health based on the traditional aspects only, whereas one survey [ 30 ] focused only the posture recognition along with the general background of yoga. In this survey, we focus on integrating traditional yoga mechanisms with an IIoT for the monitoring, detection, and analysis of yoga.…”

Section: Introductionmentioning

confidence: 99%

Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions

et al. 2023

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The physical and mental health of people can be enhanced through yoga, an excellent form of exercise. As part of the breathing procedure, yoga involves stretching the body organs. The guidance and monitoring of yoga are crucial to ripe the full benefits of it, as wrong postures possess multiple antagonistic effects, including physical hazards and stroke. The detection and monitoring of the yoga postures are possible with the Intelligent Internet of Things (IIoT), which is the integration of intelligent approaches (machine learning) and the Internet of Things (IoT). Considering the increment in yoga practitioners in recent years, the integration of IIoT and yoga has led to the successful implementation of IIoT-based yoga training systems. This paper provides a comprehensive survey on integrating yoga with IIoT. The paper also discusses the multiple types of yoga and the procedure for the detection of yoga using IIoT. Additionally, this paper highlights various applications of yoga, safety measures, various challenges, and future directions. This survey provides the latest developments and findings on yoga and its integration with IIoT.

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“…In past decade, with the development of vision and sensor technology, many works have been performed on yoga pose detection [ 27 ]. In order to help people learn yoga independently, researchers have developed many methods to aid people in self-training and avoiding injury.…”

Section: Related Workmentioning

confidence: 99%

Spatial–Temporal Self-Attention Enhanced Graph Convolutional Networks for Fitness Yoga Action Recognition

Wei

Zhou

Zhang

et al. 2023

Sensors

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Fitness yoga is now a popular form of national fitness and sportive physical therapy. At present, Microsoft Kinect, a depth sensor, and other applications are widely used to monitor and guide yoga performance, but they are inconvenient to use and still a little expensive. To solve these problems, we propose spatial–temporal self-attention enhanced graph convolutional networks (STSAE-GCNs) that can analyze RGB yoga video data captured by cameras or smartphones. In the STSAE-GCN, we build a spatial–temporal self-attention module (STSAM), which can effectively enhance the spatial–temporal expression ability of the model and improve the performance of the proposed model. The STSAM has the characteristics of plug-and-play so that it can be applied in other skeleton-based action recognition methods and improve their performance. To prove the effectiveness of the proposed model in recognizing fitness yoga actions, we collected 960 fitness yoga action video clips in 10 action classes and built the dataset Yoga10. The recognition accuracy of the model on Yoga10 achieves 93.83%, outperforming the state-of-the-art methods, which proves that this model can better recognize fitness yoga actions and help students learn fitness yoga independently.

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A Survey on Yogic Posture Recognition

Cited by 18 publications

References 153 publications

Artificial Intelligence for Computer Vision: Bibliometric Analysis

Artificial Intelligence for Computer Vision: Bibliometric Analysis

Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions

Spatial–Temporal Self-Attention Enhanced Graph Convolutional Networks for Fitness Yoga Action Recognition

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