The remarkable development of human–computer interactions has created an urgent need for machines to be able to recognise human emotions. Human motions play a key role in emphasising and conveying emotions to meet the complexity of daily application scenarios, such as medical rehabilitation and social education. Therefore, this paper aims to explore hidden emotional states from human motions. Accordingly, we proposed a novel approach for emotion recognition using multiple inertial measurement unit (IMU) sensors worn on different body parts. First, the mapping relationship between emotion and human motion was established through fuzzy comprehensive evaluation, and data were collected for six emotional states: sleepy, bored, excited, tense, angry, and distressed. Second, the preprocessed data were used as input in a lightweight convolutional neural network to extract discriminative features. Third, an attention-based sensor fusion module was developed to obtain the importance scores of each IMU sensor for generating a fused feature representation. In the recognition phase, we constructed a weighted kernel support vector machine (SVM) model with an auxiliary fuzzy function to improve the weight calculation method of kernel functions in a multiple kernel SVM. Finally, the results obtained are compared with those of similar state-of-the-art studies, the proposed method showed a higher accuracy (99.02%) for the six emotional states mentioned above. These findings may promote the development of social robots with non-verbal emotional communication capabilities. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/sil2.12201

Attention-based sensor fusion for emotion recognition from human motion by combining convolutional neural network and weighted kernel support vector machine and using inertial measurement unit signals

The achievement of badminton athletes at the international level is a complex result of various factors involving physical, technical, tactical, and psychological aspects, as well as environmental support. This study used a systematic review approach to understand the key factors that contribute to badminton athlete achievement. The purpose of this study was to detail and analyze the existing literature to identify the key factors that are significant in improving the performance of badminton athletes at the international level. The selection of articles was done through a thorough search in several significant databases, with strict inclusion criteria to ensure the relevance and quality of the research. Two independent researchers conducted the article selection process to minimize bias. Once the articles were selected, data analysis was undertaken to describe and categorize the key factors identified. The results of this systematic review revealed that physical factors such as general physical condition, strength, explosive power, flexibility, and speed significantly impact badminton athletes' performance. In addition, technical and tactical aspects, such as basic techniques, game tactics, and doubles, also play a crucial role. Psychological characteristics, including motivation, stress management, concentration, and social support, are also factors that cannot be ignored in achieving optimal performance. By detailing these key factors, this study provides an in-depth insight into the complexity of the determinants of badminton athletes' performance at the international level. The implications of these findings provide a basis for coaches and researchers to develop more effective and focused training strategies and stimulate further research to understand the interactions between these factors deeply. Thus, the results of this systematic review can make an essential contribution to the development of the world of training and coaching badminton athletes. 

pdf/analysis-of-key-factors-affecting-the-achievement-of-nmjkoicgex.pdf

Analysis of key factors affecting the achievement of badminton athletes at the international level: A systematic review

[This retracts the article DOI: 10.1155/2022/8349448.].

Retracted: Analyzing the Effect of Badminton on Physical Health and Emotion Recognition on the account of Smart Sensors

Abstract To address the challenges faced in industrial anomaly detection, including data sample imbalance, lack of anomaly labels, and complex spatiotemporal relationships in high-dimensional data, this paper proposes a novel multi-modal time-series anomaly detection model that combines attention mechanisms and adversarial training. In this model, the first step involves utilizing graph attention mechanisms to extract sequence correlation features from multi-modal time-series data, which are then summed with the original data to form a dual-feature-based data representation. Subsequently, a self-supervised learning approach is employed to input this data representation into a variational autoencoder's encoding-decoding network for reconstruction. Anomaly detection is performed by analyzing the error between the input and reconstructed data. The model also employs spatiotemporal attention mechanisms and adversarial training during reconstruction to enhance feature extraction and model generalization. By comparing our proposed model to five commonly used baseline models, we demonstrate its effectiveness in detecting anomalies in scenarios involving high-dimensional data and imbalanced abnormal samples, demonstrating superior anomaly detection performance, as well as excellent performance on real industrial production and processing datasets. 

Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training

Emotional ability is an important symbol of human intelligence. Human's understanding of emotions, from subjective consciousness to continuous or discrete emotional dimensions, and then to physiological separability, has shown a trend of gradually diverging from psychological research to the field of intelligent human-computer interaction. This article is aimed at studying the effects of smart sensor-based emotion recognition technology and badminton on physical health. It proposes a method of using smart sensor technology to recognize badminton movements and emotions during the movement. And the impact of emotion recognition based on smart sensors and badminton sports on physical health is carried out in this article. Experimental results show that the emotion recognition technology based on smart sensors can well recognize the changes in people's emotions during badminton sports, and the accuracy of emotion recognition is higher than 70%. At the same time, experiments show that badminton can greatly improve people's physical fitness and strengthen people's physique.

/pdf/analyzing-the-effect-of-badminton-on-physical-health-and-2ubhu0lk.pdf

Analyzing the Effect of Badminton on Physical Health and Emotion Recognition on the account of Smart Sensors

Early warning model for industrial internet platform based on graph neural network and time convolution network

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Analyzing the Effect of Badminton on Physical Health and Emotion Recognition on the account of Smart Sensors

Early warning model for industrial internet platform based on graph neural network and time convolution network