Academic emotions can have different influences on learning effects, but these have not been systematically studied. In this paper, we objectively evaluate the influence of various academic emotions on learning effects and studied the relationship between positive and negative academic emotions and learning effects by using five electronic databases, including WOS, EMBASE, PubMed, PsycINFO, and Google Scholar. According to established standards, a total of 14 articles from 506 articles were included in the analysis. We divided the 14 studies into nine intervention studies and five observational studies; five of the nine intervention studies found that students who used active learning materials performed better and had higher mental loads than those who used neutral learning materials. Positive academic emotions promoted the learning effect. Four of the five observational studies with high school, college, and postgraduate participants reported that regulating academic emotions can improve learning effects. In conclusion, this paper holds that positive academic emotions are better than negative academic emotions at improving academic performance. In future research, a new method combining multichannel video observation, physiological data, and facial expression data is proposed to capture learners’ learning behavior in various learning environments.

Understanding human emotions and psychology is a critical step toward realizing artificial intelligence, and correct recognition of facial expressions is essential for judging emotions. However, the differences caused by changes in facial expression are very subtle, and different expression features are less distinguishable, making it difficult for computers to recognize human facial emotions accurately. Therefore, this paper proposes a novel multi-layer interactive feature fusion network model with angular distance loss. To begin, a multi-layer and multi-scale module is designed to extract global and local features of facial emotions in order to capture part of the feature relationships between different scales, thereby improving the model's ability to discriminate subtle features of facial emotions. Second, a hierarchical interactive feature fusion module is designed to address the issue of loss of useful feature information caused by layer-by-layer convolution and pooling of convolutional neural networks. In addition, the attention mechanism is also used between convolutional layers at different levels. Improve the neural network's discriminative ability by increasing the saliency of information about different features on the layers and suppressing irrelevant information. Finally, we use the angular distance loss function to improve the proposed model's inter-class feature separation and intra-class feature clustering capabilities, addressing the issues of large intra-class differences and high inter-class similarity in facial emotion recognition. We conducted comparison and ablation experiments on the FER2013 dataset. The results illustrate that the performance of the proposed MIFAD-Net is 1.02–4.53% better than the compared methods, and it has strong competitiveness.

The main clinical manifestations of stroke are motor, language, sensory, and mental disorders. After treatment, in addition to being conscious, other symptoms will still remain in varying degrees. This is the sequelae of stroke, including numbness, facial paralysis, central paralysis, and central paralysis. If the sequelae of stroke are not treated effectively, they can easily develop into permanent sequelae. Most of the affected people have sequelae, and most of them have symptoms of upper limb paralysis. Therefore, it is of great significance to study how to carry out effective rehabilitation training for stroke patients to reduce the disease and even restore their motor function. Based on this background, this research aims to use deep learning technology to design a stroke rehabilitation model based on electroencephalography (EEG) signals. First, the patient’s EEG signal will be preprocessed. Then, an improved deep neural network model (IDNN) is used to get the EEG classification results. The traditional DNN model construction process is simple and suitable for scenarios where there is no special requirement for the data format, but the generalization of a single DNN model is usually poor. Large margin support vector machine (LM_SVM) is an extension method of support vector machine (SVM), suitable for any occasion. By optimizing the edge distribution, better generalization performance can be obtained. Taking into account the advantages of DNN and LM_SVM and the high aliasing characteristics of stroke data, an improved DNN model is proposed. Finally, based on the EEG recognition result of the model, the rehabilitation equipment is controlled to assist the patient in rehabilitation treatment. The experimental results verify the superiority of the EEG classification model used, and further prove that this research has good practical value.

Electroencephalogram (EEG) is often used in clinical epilepsy treatment to monitor electrical signal changes in the brain of patients with epilepsy. With the development of signal processing and artificial intelligence technology, artificial intelligence classification method plays an important role in the automatic recognition of epilepsy EEG signals. However, traditional classifiers are easily affected by impurities and noise in epileptic EEG signals. To solve this problem, this paper develops a noise robustness low-rank learning (NRLRL) algorithm for EEG signal classification. NRLRL establishes a low-rank subspace to connect the original data space and label space. Making full use of supervision information, it considers the local information preservation of samples to ensure the low-rank representation of within-class compactness and between-classes dispersion. The asymmetric least squares support vector machine (aLS-SVM) is embedded into the objective function of NRLRL. The aLS-SVM finds the maximum quantile distance between the two classes of samples based on the pinball loss function, which further improves the noise robustness of the model. Several classification experiments with different noise intensity are designed on the Bonn data set, and the experiment results verify the effectiveness of the NRLRL algorithm.

No abstract

The training of athletes’ anticipation and decision-making skills has received increasing attention from researchers, who developed and implemented training programs to achieve this. Video-based training (VBT) has become a popular method in anticipation and decision-making skills training. However, little is known about the benefits of implementing VBT in soccer. This systematic review considered the results of studies on VBT aiming to develop decision-making and anticipation skills in football players, and analyzed its effects. Literature published up to March 2022 was systematically searched on the scientific electronic databases Web of Science, PubMed, Scopus, SportDiscus, and Google Scholar. In total, 5,749 articles were identified. After screening the records according to the set exclusion and inclusion criteria, ten articles were considered eligible, including six longitudinal studies and four acute studies. Eight of the ten included studies (80%) showed that VBT group performance in anticipation or decision-making skills was significantly better at post-test than at pre-test, as evidenced by improvements in response accuracy (RA), response times (RT), mean distance scores (MDS) and passing decision-making performance. In six studies that included the no video-based training (NVBT) group, results showed that athletes in the VBT group performed better in anticipation or decision-making skills than in the NVBT group, as evidenced by improvements in RA and RT performance. The studies used different methods for VBT, both explicit and implicit training effectively improved participants’ anticipation and decision-making skills. In addition, the implementation of the “first-person” perspective (i.e., the player’s perspective) and virtual reality (VR) improved the presentation of video stimuli, effectively improving anticipation and decision-making. The findings of this review suggest that VBT is beneficial in developing anticipation and decision-making judgments in football players. However, some findings were inconsistent with previous studies due to differences in intervention duration and experimental protocols, and further studies are needed. Furthermore, future research should actively seek to design appropriate retention tests and transfer tests to truly understand the benefits of VBT for athletes.

Objective Extended reality technologies (e.g. virtual reality (VR), augmented reality (AR) and mixed reality (MR)) are gaining popularity in sports owing to their unique advantages. This study aims to analyse the progress of the application of extended reality technology in sports and reveal its cooperative features, research hotspots and development trends. Methods We searched the literature in the Web of Science Core Collection (WoSCC) database within the period 2000 to 2021 and conducted a bibliometric analysis. The analysis methods included statistical, co-occurrence, hierarchical clustering and social network analyses. Results A total of 340 articles were gathered. The literature related to its research showed an increasing trend over time. The paper collaboration rate was 90.88% (309/340 papers), and the degree of author collaboration was 3.96 (1345/340). VR was found to be the most productive journal, and Queen's University Belfast was the most productive institution. The United States, China and the United Kingdom were the three main contributors to the field. The foundational themes in sports extended reality research were (i) sports games and extended reality systems, (ii) virtual simulation devices and artificial intelligence, (iii) sports training and performance and (iv) age-appropriate physical activity, sports rehabilitation and physical education. Conclusion The level of author collaboration was low, but the degree of author collaboration is largely on the rise. The closeness of the collaboration between institutions and countries was also low. In addition, the subject of sport extended reality is relatively fragmented. Therefore, more research is needed to strengthen it in the future.

No abstract