Advancing Pose Correction Efficiency Through Video Analysis and Incremental Learning in Diverse Domains

The burgeoning demand for precise and efficient pose correction in diverse applications, ranging from sports training to performing arts, underscores the necessity for advancements in this domain. Existing methodologies, while effective to a degree, often grapple with limitations in accuracy, delay, and adaptability across varied contexts. To address these challenges, this paper introduces a novel approach that significantly elevates the precision and efficiency of pose correction using a multifaceted video analysis and learning framework. Our proposed model capitalizes on Google's Mediapipe API for initial video pose detection, transforming keypoints into multidomain features through an innovative amalgamation of frequency, entropy, and Gabor components. This transformation is pivotal in capturing the intricate nuances of different poses. The core of our methodology lies in the use of recurrent graph neural networks, which classify these multidomain features into distinct pose types, demonstrating a marked improvement in pose classification accuracy and adaptability. A key innovation in our approach is the employment of GridCAM++, an advanced recommendation engine that not only suggests corrections but also provides insights into the classification decisions, thereby enhancing the interpretability and effectiveness of the pose correction process. This feature is particularly beneficial for users seeking to understand and improve their technique in real-time scenarios. The efficacy of our model was rigorously tested across diverse domains, including cricket, classical dance, and western dance. The results were remarkable, showcasing a 10.5% increase in precision, 9.5% in accuracy, 3.5% in recall, 4.9% in area under the curve (AUC), and 3.9% in specificity, coupled with a notable 4.5% reduction in delay when compared to existing methods. These improvements are not just statistically significant but also practically impactful, offering tangible benefits in training and performance enhancement. In conclusion, this work not only presents a groundbreaking approach to pose correction but also sets a new benchmark in the field. Its implications are far-reaching, potentially revolutionizing how pose correction is approached in various disciplines, thereby contributing significantly to the fields of sports training, performing arts, and beyond. This research paves the way for more intuitive, accurate, and efficient pose correction systems, aligning with the evolving demands of these dynamic and diverse fields.