Dance style recognition through video analysis during university training can significantly benefit both instructors and novice dancers. Employing video analysis in training offers substantial advantages, including the potential to train future dancers using innovative technologies. Over time, intricate dance gestures can be honed, reducing the burden on instructors who would, otherwise, need to provide repetitive demonstrations. Recognizing dancers’ movements, evaluating and adjusting their gestures, and extracting cognitive functions for efficient evaluation and classification are pivotal aspects of our model. Deep learning currently stands as one of the most effective approaches for achieving these objectives, particularly with short video clips. However, limited research has focused on automated analysis of dance videos for training purposes and assisting instructors. In addition, assessing the quality and accuracy of performance video recordings presents a complex challenge, especially when judges cannot fully focus on the on‐stage performance. This paper proposes an alternative to manual evaluation through a video‐based approach for dance assessment. By utilizing short video clips, we conduct dance analysis employing techniques such as fine‐grained dance style classification in video frames, convolutional neural networks (CNNs) with channel attention mechanisms (CAMs), and autoencoders (AEs). These methods enable accurate evaluation and data gathering, leading to precise conclusions. Furthermore, utilizing cloud space for real‐time processing of video frames is essential for timely analysis of dance styles, enhancing the efficiency of information processing. Experimental results demonstrate the effectiveness of our evaluation method in terms of accuracy and F1‐score calculation, with accuracy exceeding 97.24% and the F1‐score reaching 97.30%. These findings corroborate the efficacy and precision of our approach in dance evaluation analysis.

This study bridges a crucial gap in athletic performance analysis by introducing a novel machine learning (ML) framework that leverages integrated physiological signals (from the DB 2.0 database) towards Sport Artificial Intelligence of Things (S‐AIoT). Understanding athletic performance is key to developing effective training programs and enhancing overall physical education. However, traditional methods often fall short in capturing the nuances of human movement. Our primary goal is to develop an innovative method for accurately classifying sports activities using advanced analytical techniques that consider various physiological signals. This study aims to improve classification accuracy and provide real‐time analytics for sports performance. To achieve this, we employ spatial and temporal attention mechanisms to dynamically weight critical signals, enabling precise tracking of movement transitions across different sports. The model is trained on comprehensive datasets comprising respiration rate, ECG, and heart rate (HR), providing a multifaceted analysis of athletic performance. Extensive experiments validate the model, which achieves a remarkable accuracy of 90.32%. It is the first model of its kind, outperforming established models like 1D convolutional neural network (CNN), LSTM, BiLSTM, and 1D CNN‐BiLSTM. The model demonstrates strong generalization ability on unseen data, proving its effectiveness in diverse scenarios, and exhibits moderate noise resilience, enhancing its practical applicability.

Action recognition (AR) has many applications, including surveillance, health/disabilities care, man-machine interactions, video-content-based monitoring, and activity recognition. Because human action videos contain a large number of frames, implemented models must minimize computation by reducing the number, size, and resolution of frames. We propose an improved method for detecting human actions in low-size and low-resolution videos by employing convolutional neural networks (CNNs) with channel attention mechanisms (CAMs) and autoencoders (AEs). By enhancing blocks with more representative features, convolutional layers extract discriminating features from various networks. Additionally, we use random sampling of frames before main processing to improve accuracy while employing less data. The goal is to increase performance while overcoming challenges such as overfitting, computational complexity, and uncertainty by utilizing CNN-CAM and AE. Identifying patterns and features associated with selective high-level performance is the next step. To validate the method, low-resolution and low-size video frames were used in the UCF50, UCF101, and HMDB51 datasets. Additionally, the algorithm has relatively minimal computational complexity. Consequently, the proposed method performs satisfactorily compared to other similar methods. It has accuracy estimates of 77.29, 98.87, and 97.16%, respectively, for HMDB51, UCF50, and UCF101 datasets. These results indicate that the method can effectively classify human actions. Furthermore, the proposed method can be used as a processing model for low-resolution and low-size video frames.