Pose Estimation in Yoga: A Bibliometric Exploration of Technological Advancements in Sports Sciences
DOI:
https://doi.org/10.52783/jns.v14.1689Keywords:
Artificial intelligence, Computer vision, deep learning, Sports science, yoga practiceAbstract
The integration of technology into yoga practice has seen significant advancements in recent years, particularly in the realm of pose estimation. Pose estimation refers to the computational process of determining the configuration of a person's body in a given posture, becoming a crucial tool for enhancing physical activity monitoring and improving technique in various sports and fitness applications, including yoga. Accurate analysis of yoga poses offers insights into body alignment, posture correction, and performance improvement, which are essential for efficacy and injury prevention. Traditional instruction relies on visual observation and verbal cues; however, the advent of artificial intelligence (AI) and computer vision technologies has enabled automated systems to assist in posture analysis, thereby enhancing self-guided practice. This paper presents a bibliometric exploration of pose estimation in yoga, highlighting technological advancements and key research trends. Utilizing the Scopus database, a keyword-based search yielded 106 results, refined to 16 high-quality articles through specific inclusion and exclusion criteria. Analysis reveals a steady publication rate, a broad range of interdisciplinary sources, and the collaborative nature of research in this field, with India and the USA being prominent contributors. Despite significant advancements, gaps remain in recognizing nuanced postures and evaluating the long-term outcomes of these technologies. This study underscores the need for ongoing research to refine pose estimation techniques and explore their practical applications effectively, ultimately enhancing the experience and effectiveness of yoga practice.
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