Digital fitness has become a widely used tool for remote exercise guidance, leveraging artificial intelligence to analyze exercise videos and support self-training. This paper introduces a method for self-training in golf, a sport where automated posture analysis can significantly reduce the costs associated with professional coaching. Our system utilizes a pose refinement methodology and an explainable golf swing embedding for analyzing the swing motions of learners and professional golfers. By leveraging sequential coordinate information, we detect biased pose joints and refine the 2D and 3D human pose estimation results. Furthermore, we propose a swing embedding method that considers geometric information extracted from the swing pose. This approach enables not only the comparison of the similarity between two golf swing poses but also the visualization of different points, providing learners with specific and intuitive feedback on areas that require correction. Our experimental results demonstrate the effectiveness of our swing guide system in identifying specific body points that need adjustment to align more closely with a professional golfer’s swing. This research contributes to the digital fitness domain by enhancing the accuracy of posture analysis and providing a specialized and interpretable golf swing analysis system. Our proposed system offers a low-cost and time-efficient approach for users who wish to improve their golf swing, paving the way for broader applications of digital fitness technologies in self-training contexts.

During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model’s generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions.