An OpenPose-Based Exercise and Performance Learning Assistant Design for Self-Practice Yoga

Lin, Cheng-Hsien; Shen, Shih-Wei; Anggraini, Irin Tri; Funabiki, Nobuo; Fan, Chih‐Peng

doi:10.1109/gcce53005.2021.9621907

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…Over the past decade, numerous works have been done in yoga posture recognition using vision and sensor technology. Motion capture sensors [81], [82], [83], [84], [85], [86], [87], [88], accelerometers [89], [90], gyroscopes [91], magnetometers [89], motion sensors [92], pressure sensors [93], other visual [24], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105], [106], [107], [108] and wearable sensors [97], [109] are some of the most utilized in postural estimations. Radio Frequency Identification [RFID] [110] and Wireless Fidelity (Wi-Fi) [111], for example, are location-based sensors.…”

Section: F Yoga Posture Recognition (Ypr)mentioning

confidence: 99%

“…Data collection [24], [81], [82], [83], [84], [85], [86], [87], [88], [91], [92], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105], [106], [109], [110], [111], [112], [113], [114], [115], [116], keypoint extraction [81], [82], [83], [84], [85], [86], [87], [88], [91], [92], [94], [95], [96], [97], [98], [99], [100], [101], [102]<...…”

Section: F Yoga Posture Recognition (Ypr)unclassified

“…Every competent yoga teacher is familiar with the prerequisites for each Yogic position. The major vital points would receive an 80, while the other key points would receive a 20 if the appropriate weighting were applied [99].…”

Section: B Yoga Posture Gradingmentioning

confidence: 99%

“…Selecting two yoga posture photos of the student and the teacher, retrieving the human skeleton keypoints, and entering those into the posture characteristic encoder are methods for grading yoga postures. At last, in terms of generating the posture grade, overall feature similarities in-between them were computed [24], [94], [99], [107], [108]. Table 12 represents a detailed overview of these approaches used in the recent ML and DL models.…”

Section: B Yoga Posture Gradingmentioning

confidence: 99%

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A Survey on Yogic Posture Recognition

Rajendran

Sethuraman

2023

IEEE Access

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Yoga has been a great form of physical activity and one of the promising applications in personal health care. Several studies prove that yoga is used as one of the physical treatments for cancer, musculoskeletal disorder, depression, Parkinson's disease, and respiratory heart diseases. In yoga, the body should be mechanically aligned with some effort on the muscles, ligaments, and joints for optimal posture. Postural-based yoga increases flexibility, energy, overall brain activity and reduces stress, blood pressure, and back pain. Body Postural Alignment is a very important aspect while performing yogic asanas. Many yogic asanas including uttanasana, kurmasana, ustrasana, and dhanurasana, require bending forward or backward, and if the asanas are performed incorrectly, strain in the joints, ligaments, and backbone can result, which can cause problems with the hip joints. Hence it is vital to monitor the correct yoga poses while performing different asanas. Yoga posture prediction and automatic movement analysis are now possible because of advancements in computer vision algorithms and sensors. This research investigates a thorough analysis of yoga posture identification systems using computer vision, machine learning, and deep learning techniques.INDEX TERMS Yogic posture recognition, optimal posture, machine learning methods, deep learning methods.

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Section: F Yoga Posture Recognition (Ypr)mentioning

confidence: 99%

Section: F Yoga Posture Recognition (Ypr)unclassified

Section: B Yoga Posture Gradingmentioning

confidence: 99%

Section: B Yoga Posture Gradingmentioning

confidence: 99%

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A Survey on Yogic Posture Recognition

Rajendran

Sethuraman

2023

IEEE Access

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“… Lin et al (2021) introduced a self-practice yoga system for tracking the performance of body posture during exercises. The OpenPose model is used to detect 24 keypoints of body joints.…”

Section: Related Workmentioning

confidence: 99%

A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy

Desai

Mewada

2023

PeerJ Computer Science

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Virtual motion and pose from images and video can be estimated by detecting body joints and their interconnection. The human body has diverse and complicated poses in yoga, making its classification challenging. This study estimates yoga poses from the images using a neural network. Five different yoga poses, viz. downdog, tree, plank, warrior2, and goddess in the form of RGB images are used as the target inputs. The BlazePose model was used to localize the body joints of the yoga poses. It detected a maximum of 33 body joints, referred to as keypoints, covering almost all the body parts. Keypoints achieved from the model are considered as predicted joint locations. True keypoints, as the ground truth body joint for individual yoga poses, are identified manually using the open source image annotation tool named Makesense AI. A detailed analysis of the body joint detection accuracy is proposed in the form of percentage of corrected keypoints (PCK) and percentage of detected joints (PDJ) for individual body parts and individual body joints, respectively. An algorithm is designed to measure PCK and PDJ in which the distance between the predicted joint location and true joint location is calculated. The experiment evaluation suggests that the adopted model obtained 93.9% PCK for the goddess pose. The maximum PCK achieved for the goddess pose, i.e., 93.9%, PDJ evaluation was carried out in the staggering mode where maximum PDJ is obtained as 90% to 100% for almost all the body joints.

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