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Revolutionising Yoga Practice with Computer Vision: The Yoga Pose Corrector
Ensuring correct posture in yoga is crucial for maximizing its benefits and preventing injuries. Incorrect execution of yoga poses can lead to a range of issues, from minor muscle strains to long-term chronic injuries, undermining the physical and mental benefits yoga aims to provide. Proper alignment is essential for achieving the desired stretch and strength and maintaining overall bodily health and balance.
Advancements in image processing and computer vision have paved the way for innovative applications across various fields, and one of the most promising is the Yoga Pose Corrector app. By leveraging sophisticated vision models, this app provides real-time feedback on yoga postures, enhancing the learning experience and ensuring proper form. This technology has the potential to revolutionize the health industry by making personalized, corrective feedback accessible to anyone, anywhere, significantly reducing the risk of injury and promoting better health outcomes for yoga practitioners.
How does the Yoga Pose Corrector work?
Preprocessing and Image Superimposition
The initial step involves preprocessing images of correct yoga poses. These reference images serve as a benchmark against which users’ poses are compared. The key to this process is superimposing the user’s yoga pose image onto the reference image. This allows for a direct visual comparison, highlighting deviations from the ideal posture.
Identifying Key Points
Key points in a yoga pose are critical markers such as the position of the left leg, right hand, head, and torso. These points are essential for aligning the user’s image with the reference image. The system uses advanced deep learning techniques to identify key points on the human body, including the eyes, ears, shoulders, elbows, knees, and more.
Image Alignment and Processing
The system performs additional image processing using simple arithmetic operations to ensure a meaningful comparison. This involves adjusting the width and height of the images to make them similar, facilitating an accurate superimposition. This step ensures that the differences in posture are clearly visible and measurable.
