Abstract

Human pose detection is a rapidly growing area in computer vision, which has various applications such as action recognition, sports analysis, and human-computer interaction. Yoga, a holistic practice that promotes physical and mental well-being, has gained widespread popularity in recent years. With the advent of technology, there is an increasing demand for innovations that can enhance the yoga experience. The objective is to develop an automated system that can detect and recognize yoga poses in real-time using a live camera. This research tackles the challenging task of real-time yoga pose detection through a live camera feed, aiming to facilitate human-computer interaction and advance the field of computer vision. Yoga poses are complex and highly variable, making their automated detection a noteworthy endeavor. This research aims to develop an iOS application for detecting yoga postures that leverages the capabilities of AV Foundation, Combine, Vision, and Core ML frameworks to construct an advanced system. Its applications extend beyond yoga, encompassing fitness monitoring, rehabilitation, and wellness tracking, further emphasizing its significance in modern society.