A Computer Vision-Based Yoga Pose Grading Approach Using Contrastive Skeleton Feature Representations

Abstract

The main objective of yoga pose grading is to assess the input yoga pose and compare it to a standard pose in order to provide a quantitative evaluation as a grade. In this paper, a computer vision-based yoga pose grading approach is proposed using contrastive skeleton feature representations. First, the proposed approach extracts human body skeleton keypoints from the input yoga pose image and then feeds their coordinates into a pose feature encoder, which is trained using contrastive triplet examples; finally, a comparison of similar encoded pose features is made. Furthermore, to tackle the inherent challenge of composing contrastive examples in pose feature encoding, this paper proposes a new strategy to use both a coarse triplet example—comprised of an anchor, a positive example from the same category, and a negative example from a different category, and a fine triplet example—comprised of an anchor, a positive example, and a negative example from the same category with different pose qualities. Extensive experiments are conducted using two benchmark datasets to demonstrate the superior performance of the proposed approach.

A Survey on Yogic Posture Recognition

“…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.…”

“…Yoga poses are detected by the YPR system as two approaches using vision and sensor-based methodologies. performance in tackling the yogic posture classification due to its remarkable featured learning capability [108]. With the proliferation of motion sensors, it is now possible to collect motion data and monitor the performance of yoga postures.…”

“…Unlike yoga pose categorization, which seeks to deduce the yoga posture class label, yogic posture grading (YPG) attempts to measure the individual's yogic acts statistically. Despite the fact that there are numerous studies on yogic posture categories, there are very few studies on yogic posture grading [108].…”

“…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.…”

Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis

“…They achieved a 91% accuracy on a homemade dataset, comprising 8 yoga positions, which was created in collaboration with a professional coach, though the exact number of videos in the dataset was unspecified. In [30], the authors sought unify classification and posture evaluation by using skeleton keypoints. This study reported a classification accuracy of 83.21% over 45 postures in a dataset containing 1931 images, by following a method that implies the construction of contrastive examples.…”