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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. [ABSTRACT FROM AUTHOR]

To overcome the high uncertainty and randomness of wind and enable the grid to optimize advance preparation, a priori-guided and data-driven hybrid method is proposed to provide accurate and reasonable wind power forecasting results. Fuzzy C-Means (FCM) clustering algorithm is used first to recognize the characteristics of the weather in different regions. Then, for the purpose of making full use of both priori information and collected measured data, a three-stage hierarchical framework is designed. First, via fuzzy inference and dimension reduction of Numerical Weather Prediction (NWP), more applicable wind speed information is obtained. Second, the accessible wind power generation patterns are served as a guide for mining the actual power curve. Third, the forecasted power is derived through the recorded data and the predictable wind conditions via data-driven model. This forecasting framework ingeniously introduces a gateway that can import priori knowledge to steer the iterative learning, thus possessing both adaptive learning ability and Volterra polynomial representation, and can present forecasted outcomes with robustness, accuracy and interpretability. Finally, a real-world dataset of a wind farm as well as an open source dataset are used to verify the performance of the proposed forecasting method. Results of the ablation analyses and comparative experiments demonstrate that the introduction of domain knowledge improves the forecasting performance. • Priori-guided and data-driven hierarchical forecasting framework. • Priori-guided adaptive learning algorithm. • Neural network with priori knowledge. • Ultra-short-term wind power forecasting in 4-hour advance. • Forecasted future wind power generation with high interpretability and effectiveness. [ABSTRACT FROM AUTHOR]

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.

• An interpretable hand detection method to predict hand regions at pixel resolution. • Highlight the discriminative features among multiple layers. • A more transparent representation of rotation angle: rotation map. • Use auxiliary losses to accelerate the convergence of the network. The lack of interpretability of existing CNN-based hand detection methods makes it difficult to understand the rationale behind their predictions. In this paper, we propose a novel neural network model, which introduces interpretability into hand detection for the first time. The main improvements include: (1) Detect hands at pixel level to explain what pixels are the basis for its decision and improve transparency of the model. (2) The explainable Highlight Feature Fusion block highlights distinctive features among multiple layers and learns discriminative ones to gain robust performance. (3) We introduce a transparent representation, the rotation map, to learn rotation features instead of complex and non-transparent rotation and derotation layers. (4) Auxiliary supervision accelerates the training process, which saves more than 10 h in our experiments. Experimental results on the VIVA and Oxford hand detection and tracking datasets show competitive accuracy of our method compared with state-of-the-art methods with higher speed. Models and code are available: https://isrc.iscas.ac.cn/gitlab/research/pr2020-phdn. [ABSTRACT FROM AUTHOR]