Recognition of Yoga Asana from Real-Time Videos using Blaze-pose

Mohammed, Sharfuddin Waseem; Garrapally, Vignesh; Manchala, Suraj; Reddy, Sreedhar; Naligenti, Santosh Kumar

doi:10.12785/ijcds/1201104

Cited by 5 publications

(1 citation statement)

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“…Compared to other open-sourced 3D pose estimation libraries, such as OpenPose [19,25], BlazePose is computationally lightweight and can be deployed across various platforms, such as in a web browser as a JavaScript application. As a result of its versatility, BlazePose and other video-based pose estimation libraries have been used by researchers to develop different applications, including in the context of sports for movement abnormality detection [26], gait assessment [27], hypermobility assessment [28], yoga training [29], postural disorder monitoring for Parkinson's patients, [30] and spinal dysfunction risk estimation [31]. However, studies that explicitly examine the effectiveness of applying opensourced 3D pose-estimation library for range of motion evaluation remain scarce [32].…”

Section: Introductionmentioning

confidence: 99%

A webcam-based machine learning approach for three-dimensional range of motion evaluation

Wang,

Smith,

Zhu

2023

PLoS ONE

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Background Joint range of motion (ROM) is an important quantitative measure for physical therapy. Commonly relying on a goniometer, accurate and reliable ROM measurement requires extensive training and practice. This, in turn, imposes a significant barrier for those who have limited in-person access to healthcare. Objective The current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam. Methods To evaluate its reliability, the ROM measurements for a diverse set of joints (neck, spine, and upper and lower extremities) derived using this method were compared to those obtained from a marker-based optical motion capture system. Results Data collected from 25 healthy adults demonstrated that the webcam solution exhibited high test-retest reliability, with substantial to almost perfect intraclass correlation coefficients for most joints. Compared with the marker-based system, the webcam-based system demonstrated substantial to almost perfect inter-rater reliability for some joints, and lower inter-rater reliability for other joints (e.g., shoulder flexion and elbow flexion), which could be attributed to the reduced sensitivity to joint locations at the apex of the movement. Conclusions The proposed webcam-based method exhibited high test-retest and inter-rater reliability, making it a versatile alternative for existing ROM evaluation methods in clinical practice and the tele-implementation of physical therapy and rehabilitation.

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Section: Introductionmentioning

confidence: 99%

A webcam-based machine learning approach for three-dimensional range of motion evaluation

Wang,

Smith,

Zhu

2023

PLoS ONE

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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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