Previous studies have confirmed that the temporal attentional control created by the repetition of stimulus-response compatibility (SRC) tasks was transferred to shooting skills in lacrosse players. In the current study, we investigated whether combining motor imagery training with SRC tasks could enhance the scoring ability of lacrosse players. We grouped 33 male lacrosse players into three groups: an SRC task and motor imagery group (referred as to SRC+Image), an SRC task group, and a control group. Players in the first (...) two groups underwent five sessions of 200 SRC task trials. In addition, the SRC+Image group completed five sessions of motor imagery training. The control group underwent no training interventions. All three groups performed a lacrosse shooting test and a Simon task before and after training sessions to assess the magnitude of the interference effects of the various types of training they underwent. The results of the Simon task showed that repetition of 1,000 trials was enough to create a short-term representation with the incompatible special mapping being transferred to a dynamic activity like lacrosse shooting. Moreover, combination of a computer-based Type 2 task and motor-imagery training could effectively increase players’ scoring ability in a field of large spatial conflict. (shrink)

In order to solve the problems of poor physical fitness of college students and low efficiency of college sport venues' management, an intelligent sports management system based on deep learning technology is designed by using information technology and human-computer interaction under artificial intelligence. Based on the Browser/Server structure, the intelligent sports management system is constructed. The basic framework of Spring Cloud is used to integrate the framework and components of each part, and a distributed microservice system (...) is built. The artificial intelligence recommendation algorithm is used to analyze the user's age, body mass index, and physical health status, and recommend sports programs suitable for students, thus realizing the intelligent sports program recommendation function. At the same time, the recommendation algorithm is used to complete the course recommendation according to the students' preferences, teaching distance, opening time, course evaluation, and other indexes, and the course registration system is constructed; after the analysis of the entity and the relationship between the entities of the intelligent sports system, the database relational model of the system is designed with the entity relationship diagram. The results of the functional test show that the system can run well. In conclusion, the sports training environment instructional system based on artificial intelligence and deep learning technology can meet the teaching needs of colleges, improve the sports' quality for college students, and promote psychological education. (shrink)

With the rapid development of computer science and information technology, artificial intelligence has been developed from theory to application. As a key technology in the modern society, AI is increasingly affecting all aspects of people’s daily lives, including sports training. AI can be considered as an assistive technology to provide specific support to athletes’ physical education training through various means such as data analysis and simulation of training scenarios. Although research on AI is still in the (...) preliminary stage, it is significant to explore how AI can be applied in sports training since this emerging technology could facilitate people’s physical training to some extent. This paper first reviews the existing research on AI applications. Then, based on the fundamental concept and related research results of AI, this study explores three specific cases of AI application in sports training and explains the main principles. This research focuses on discussing the strong relationship between AI technology and physical education training and highlights the advantages of AI, including utilization, convenience, and innovation. (shrink)

Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This (...) paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state. (shrink)

Systems with human-centered artificial intelligence are always as good as their ability to consider their users’ context when making decisions. Research on identifying people’s everyday activities has evolved rapidly, but little attention has been paid to recognizing both the activities themselves and the motions they make during those tasks. Automated monitoring, human-to-computer interaction, and sports analysis all benefit from Web 4.0. Every sport has gotten its move, and every move is not known to everyone. In ice hockey, every (...) move cannot be monitored by the referee. Here, Convolution Neural Network-based Real-Time Image Processing Framework (CNN-RTIPF) is introduced to classify every move in Ice Hockey. CNN-RTIPF can reduce the challenges in monitoring the player’s move individually. The image of every move is captured and compared with the trained data in CNN. These real-time captured images are processed using a human-centered artificial intelligence system. They compared images predicted by probability calculation of the trained set of images for effective classification. Simulation analysis shows that the proposed CNN-RTIPF can classify real-time images with improved classification ratio, sensitivity, and error rate. The proposed CNN-RTIPF has been validated based on the optimization parameter for reliability. To improve the algorithm for movement identification and train the system for many other everyday activities, human-centered artificial intelligence-based Web 4.0 will continue to develop. (shrink)

Abilities such as peripheral reaction are of special importance in soccer. Whether these abilities can be improved by sport-specific on-field interventions remains unclear. The aim of the present controlled trial was to investigate the effect of a soccer-specific perceptual-cognitive on-field training on peripheral reaction of highly talented soccer players aged 12 to 13 years. N = 38 male elite athletes from young talent centers were allocated to an intervention (n = 19) and a control group (n = 19). (...) Computer-based peripheral perception tests were conducted pre- and post-intervention. Combining a sport-specific and a juggling task, the intervention was performed once a week (8 weeks, 20 min. per week) in addition to team training. The control group exclusively underwent usual team training. Analyzes show significant differences between the two groups for Peripheral Reaction Time, with significant improvements for the intervention group and none for the control group. Furthermore, results indicate that improvements in peripheral reaction might be due to changes in the reaction time of right-footed players. Future studies should be conducted to clarify the effect of sport-specific on-field training approaches on peripheral reaction time. These analyzes should consider the influence of lateralization on effectivity of perceptual-cognitive on-field training approaches. (shrink)

Computer-based training has attracted increasing attention from researchers in recent years. Several studies have found that computer-based training resulted in improved executive functions in adults. However, it remains controversial whether children can benefit from computer-based training and what moderator could influence the training effects. The focus of the present meta-analysis was to examine the effects of computer-based training on EFs in children: working memory, cognitive flexibility, and inhibitory (...) control. A thorough search of published work yielded a sample of 36 studies with 216 effect sizes. The results indicated that computer-based training showed moderate training effects on improving EFs in children, while training effects of working memory were significantly higher. Furthermore, we found near-transfer effects were marginally significantly higher than far-transfer effects. The standard training method was significantly more effective than training with game elements. In computer-based training, typically developing children had significantly better training effects than atypically developing children. Some additional factors, such as the number of training sessions and age, also modulated the training effects. In conclusion, the present study investigated the effects and moderators of computer-based training for children's EFs. The results provided evidence that computer-based training may serve as an efficient way to improve EFs in children. We also discussed some directions for future computer-based training studies. (shrink)

Computer technology and related Internet of things technology have penetrated into people’s daily life and industrial production; even in competitive sports training and competition, the Internet of things technology has also been a large number of applications. Traditional intelligent wearable devices are mainly used to calculate the steps of athletes or sports enthusiasts, corresponding physical data, and corresponding body indicators. The energy consumption calculated by these indexes is rough and the corresponding error is large. Based on this, (...) this paper will design a wearable device which can accurately calculate and monitor sports energy consumption based on relevant sensors and Internet of things technology. The corresponding core algorithm is the step counting algorithm, which can accurately calculate the relationship between human motion and the corresponding energy consumption and feed back to the intelligent device. In the experiment, the wearable device designed in this paper is compared with the traditional intelligent device. The experiment shows that the wearable device proposed in this paper is more accurate in energy consumption estimation than the traditional device, and its corresponding energy consumption is relatively small. (shrink)

The purpose of this mini-review is to investigate if there are any cognitive benefits of computer-based foreign language training for healthy older individuals aged 55+ years. The author conducted a literature search of peer-reviewed English written research articles found in Pub Med, Web of Science and Scopus. The findings of this mini-review reveal that the research on the cognitive benefits of computer-based foreign language training for healthy older individuals is small-scale. The limited research findings (...) of only three relevant studies indicate that these computer-based foreign language training programs may bring cognitive benefits for healthy elderly people, especially as far as the enhancement of their cognitive functions such as working memory are concerned. In addition, the authors of these studies suggest that foreign language learning is a useful activity for healthy older adults since it has the benefits of being meaningful and provides the chance for acquiring important skills that can benefit other aspects of life, such as travel or communication. In conclusion, the author of this mini-review also provides several implications for practice and future research. (shrink)

Dementia, a prevalent disorder of the brain, has negative effects on individuals and society. This paper concerns using Spontaneous Speech Challenge of Interspeech 2020 to classify Alzheimer's dementia. We used VGGish, a deep, pretrained, Tensorflow model as an audio feature extractor, and Scikit-learn classifiers to detect signs of dementia in speech. Three classifiers were 59.1% accurate, which was 3% above the best-performing baseline models trained on the acoustic features used in the challenge. We also proposed DemCNN, a new PyTorch raw (...) waveform-based convolutional neural network model that was 63.6% accurate, 7% more accurate then the best-performing baseline linear discriminant analysis model. We discovered that audio transfer learning with a pretrained VGGish feature extractor performs better than the baseline approach using automatically extracted acoustic features. Our DepCNN exhibits good generalization capabilities. Both methods presented in this paper offer progress toward new, innovative, and more effective computer-based screening of dementia through spontaneous speech. (shrink)

To effectively train ethical decision-making of nursing students, a case-based computer program was developed using Flash animation. Seven ethical cases collected from practicing registered nurses’ actual clinical experiences and a six-step Integrated Ethical Decision-Making Model developed by the author were employed in the program. In total, 251 undergraduate students from three nursing schools used the program in their nursing ethics course. The usability of the program and its usefulness in improving 11 abilities needed in ethical decision-making were measured; (...) it scored higher than 4 on a 5-point scale. Of the students, 82% recommended the program as a valuable complementary tool in the teaching of a nursing ethics course. A variety of encouraging and positive experiences were reported by the students. The computer program is likely to be usefully practical in the training of abstract skills to nursing students, though certain challenges remain, such as the precise understanding of cognitive or affective responses to ethical issues. (shrink)

This study presents the evaluation of a computer-based learning program for children with developmental dyscalculia and focuses on factors affecting individual responsiveness. The adaptive training program Calcularis 2.0 has been developed according to current neuro-cognitive theory of numerical cognition. It aims to automatize number representations, supports the formation and access to the mental number line and trains arithmetic operations as well as arithmetic fact knowledge in expanding number ranges. Sixty-seven children with developmental dyscalculia from second to fifth (...) grade (mean age 8.96 years) were randomly assigned to one of two groups (Calcularis group, waiting control group). Training duration comprised a minimum of 42 training sessions à 20 min within a maximum period of 13 weeks. Compared to the waiting control group, children of the Calcularis group demonstrated a higher benefit in arithmetic operations and number line estimation. These improvements were shown to be stable after a 3-months post training interval. In addition, this study examines which predictors accounted for training improvements. Results indicate that this self-directed training was especially beneficial for children with low math anxiety scores and without an additional reading and/or spelling disorder. In conclusion, Calcularis 2.0 supports children with developmental dyscalculia to improve their arithmetical abilities and their mental number line representation. However, it is relevant to further adapt the setting to the individual circumstances. (shrink)

Given the widespread acceptance of computational thinking in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things combines artificial intelligence and the Internet of things and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (...) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design. (shrink)

In sport, perceptual skill training software is intended to assist tactical training in the field. The aim of this field study was to test whether ‘laboratory-based’ pattern recall training would augment tactical skill training performed on the field. Twenty-six soccer players between 14-16 years of age from a single team participated in this study and were divided into three groups. The first received field training on a specific tactical skill plus cognitive training (...) sessions on the pattern recall task. The second performed only the field training while the third group served as a control group and had field training on other topics. The task on the pre-, post- and retention tests was to recall specific soccer patterns displayed on a computer screen. Results showed significant changes between pre- to post-test performance. There was no significant interaction between groups and tests but the effect size was large. From pre- to retention-test, there was a significant difference between tests and an interaction between groups and tests, but no main effect difference between groups. On the basis of significance testing only retention was affected by the additional training, however, descriptive results and effect sizes from pre- to post-test were as expected and suggested there were learning benefits. Together these results indicate that augmented perceptual-cognitive training might be beneficial, but some limitations in our study design (e.g., missing field test, missing placebo group, etc.) need to be improved in future work. (shrink)

The aim of this study is the examination of the effect of virtual reality based imagery (VRBI) training programs on the shot performance and imagery skills of athletes and, and to conduct a comparison with Visual Motor Behavior Rehearsal and Video Modeling (VMBR + VM). In the research, mixed research method and sequential explanatory design were used. In the quantitative dimension of the study the semi-experimental model was used, and in the qualitative dimension the case study design was (...) adopted. The research participants were selected from athletes who were involved in our target sports: curling (n= 14), bowling (n= 13), and archery (n= 7). All participants were randomly assigned to VMBR + VM (n= 11), VRBI (n= 12), and Control (n= 11) groups through the “Research Randomizer” program. The quantitative data of the study was: the weekly shot performance scores of the athletes and the data obtained from the “Movement Imagery Questionnaire-Revised.” The qualitative data was obtained from the data collected from the semi-structured interview guide, which was developed by researchers and field experts. According to the results obtained from the study, there were statistically significant differences between the groups in terms of shot performance and imagery skills. VRBI training athletes showed more improvement in the 4-week period than the athletes in the VMBR + VM group, in terms of both shot performance and imagery skills. In addition, the VRBI group adapted to the imagery training earlier than the VMBR + VM group. As a result, it was seen that they showed faster development in shot performances. From these findings, it can be said that VRBI program is more efficient in terms of shot performance and imagery skills than VMBR + VM, which is the most used imaging training model. (shrink)

Ethical training in graduate programs is an important part of the professional development process. Such training has taken a position of prominence in both counseling and clinical psychology but seems to be lagging behind in the field of sport psychology. A debate exists about whether such training is necessary and, if so, how it should be provided. An important step in better understanding these issues is to identify how such training is currently taking place. This (...) study surveyed the program directors of sport psychology programs listed in the Directory of Graduate Programs in Applied Sport Psychology about the ethical training that takes place in their programs and their perceptions of the preparedness of the students in their programs. Of those contacted, 54% responded to the e-mail based survey. The results from these respondents indicated that 64.4% of programs require training in ethics and that the training was most commonly integrated into other nonethics courses. Overall, respondents did not feel as if students were completely prepared for either the ethical or legal issues that they will face in their professional careers. The importance of ethical training and suggestions for improving ethical training are discussed. (shrink)

Ethical training in graduate programs is an important part of the professional development process. Such training has taken a position of prominence in both counseling and clinical psychology but seems to be lagging behind in the field of sport psychology. A debate exists about whether such training is necessary and, if so, how it should be provided. An important step in better understanding these issues is to identify how such training is currently taking place. This (...) study surveyed the program directors of sport psychology programs listed in the Directory of Graduate Programs in Applied Sport Psychology (Burke, Sachs, & Schrader, 2002) about the ethical training that takes place in their programs and their perceptions of the preparedness of the students in their programs. Of those contacted, 54% (n = 47) responded to the e-mail based survey. The results from these respondents indicated that 64.4% of programs require training in ethics and that the training was most commonly integrated into other nonethics courses. Overall, respondents did not feel as if students were completely prepared for either the ethical or legal issues that they will face in their professional careers. The importance of ethical training and suggestions for improving ethical training are discussed. (shrink)

This work focuses on the application of machine learning techniques to assist the clinicians in the administration of analgesic drug during general anaesthesia. Specifically, the main objective is to propose the basis of an intelligent system capable of making decisions to guide the opioid dose changes based on a new nociception monitor, the analgesia nociception index. Clinical data were obtained from 15 patients undergoing cholecystectomy surgery. By means of an off-line study, machine learning techniques were applied to analyse the (...) possible relationship between the analgesic dose changes performed by the physician due to the hemodynamic activity of the patients and the evolution of the ANI. After training different classifiers and testing the results under cross validation, a preliminary relationship between the evolution of ANI and the dosage of remifentanil was found. These results evidence the potential of the ANI as a promising index to guide the infusion of analgesia. (shrink)

Motor imagery is often used inducing changes in electroencephalographic (EEG) signals for imagery-based brain-computer interfacing (BCI). A BCI is a device translating brain signals into control signals providing severely motor-impaired persons with an additional, non-muscular channel for communication and control. In the last years, there is increasing interest using BCIs also for healthy people in terms of enhancement or gaming. Most studies focusing on improving signal processing feature extraction and classification methods, but the performance of a BCI can (...) also be improved by optimizing the user’s control strategies, e.g., using more vivid and engaging mental tasks for control. We used multichannel EEG to investigate neural correlates of a sports imagery task (playing tennis) compared to a simple motor imagery task (squeezing a ball). To enhance the vividness of both tasks participants performed a short physical exercise between two imagery sessions. EEG was recorded from 60 closely spaced electrodes placed over frontal, central, and parietal areas of 30 healthy volunteers divided in two groups. Whereas Group 1 (EG) performed a physical exercise between the two imagery sessions, Group 2 (CG) watched a landscape movie without physical activity. Spatiotemporal event-related desynchronization (ERD) and event-related synchronization (ERS) patterns during motor imagery (MI) tasks were evaluated. The results of the EG showed significant stronger ERD patterns in the alpha frequency band (8–13 Hz) during MI of tennis after training. Our results are in evidence with previous findings that MI in combination with motor execution has beneficial effects. We conclude that sports MI combined with an interactive game environment could be a future promising task in motor learning and rehabilitation improving motor functions in late therapy processes or support neuroplasticity. (shrink)

BackgroundThe use of dedicated training programs for improving decision-making (DM) in team sports players has grown in the last several years. Approaches such as imagery training, video-based training, or game-based drills are some of the interventions used in youth players in order to improve DM. However, no systematic reviews or meta-analyses have been conducted to summarize the main evidence regarding the effects of these programs on the players and identify the magnitude of the effects compared (...) to control groups.ObjectiveThis systematic review (with meta-analysis) was conducted to assess the effects of training programs on the DM of youth team sports players.Data SourcesThe data sources utilized were PubMed, PsycINFO, Scopus, SPORTDiscus, and Web of Science.Study eligibility criteriaThe criteria included the following: (i) youth (≤ 18 years old) team sports players with no restriction on sex or competitive level; (ii) players subjected to training programs to develop DM; (iii) control groups; (iv) pre–post outcomes related to tactical behavior, technical execution, reaction, and decision time; and (v) controlled trials.ResultsThe database search initially identified 2497 titles. From these, six articles were eligible for the systematic review and meta-analysis. The results showed a significant beneficial effect of DM interventions on tactical behavior (ES = 1.12;p= 0.035;I2= 80.0%; Egger’s testp= 0.066), whereas no significant effect of DM interventions on technical execution was found (ES = 0.74;p= 0.180;I2= 69.1%; Egger’s testp= 0.873).ConclusionThe DM interventions were significantly effective in improving tactical behavior in youth team sports players independently from the number of sessions to which players were exposed. In addition, DM interventions were significantly effective in improving technical execution. However, the results should be carefully interpreted due to the heterogeneity of the articles’ overall methodological quality. Future DM interventions should consider using combined approaches that allow players to develop both tactical behavior and technical execution. (shrink)

From a cognitive-psychological perspective, physical exercise (PE) and sports are an interesting tool for improving people’s cognitive abilities. One field of application for such a tool is decision making (DM) support in chronic patients, cancer patients, and survivors in particular. On the one hand, cancer patients and survivors have to continually take important decisions about their own care (e.g., treatment choice; changes in lifestyle), in collaboration with caregivers and health providers; on the other hand, side effects of treatment may be (...) detrimental to cognitive abilities, such as attention, which make the health DM tasks even more demanding, complex, and emotionally disruptive for patients. Since cancer patients have to engage in healthy activities both for improving their own quality of life and for sustaining the effects of medications, clinical advice to engage in sport and PE is becoming more and more widespread within interventions. However, while sports are usually seen as healthy physical activities, their impact on cognitive abilities is mostly overlooked in the literature. The hypothesis of the present work is that sports could be fully exploited in their potential as focused exercises for cognitive ability training, in the field of cognitive training for chronic patients specifically. Indeed, literature shows that different sports (e.g., individual or team-based) influence and possibly augment cognitive abilities such as focused and divided attention, working memory, and DM under time constraints. Moreover, besides providing training for cognitive abilities, the experience of sports may represent an opportunity to explore, train and sharpen DM abilities directly: we identify five ways in which sport experiences may influence DM processes, and provide indications for future research on the topic. (shrink)

Ethical training in graduate programs is an important part of the professional development process. Such training has taken a position of prominence in both counseling and clinical psychology but seems to be lagging behind in the field of sport psychology. A debate exists about whether such training is necessary and, if so, how it should be provided. An important step in better understanding these issues is to identify how such training is currently taking place. This (...) study surveyed the program directors of sport psychology programs listed in the Directory of Graduate Programs in Applied Sport Psychology (Burke, Sachs, & Schrader, 2002) about the ethical training that takes place in their programs and their perceptions of the preparedness of the students in their programs. Of those contacted, 54% (n = 47) responded to the e-mail based survey. The results from these respondents indicated that 64.4% of programs require training in ethics and that the training was most commonly integrated into other nonethics courses. Overall, respondents did not feel as if students were completely prepared for either the ethical or legal issues that they will face in their professional careers. The importance of ethical training and suggestions for improving ethical training are discussed. (shrink)

To effectively improve the efficiency of international service design talent training and make it more in line with society's needs, we analyze the current status of international service design talent training and its professional training focus. Based on the above problems, from the perspective of cognitive psychology, artificial intelligence and human–computer interaction technology are used to construct the international service design talent training mode of the HCI intelligent service robot. This mode can be used (...) to solve the existing teaching problems by using novel means to ensure the quality of teaching. Finally, through the actual analysis of teaching cases, the effectiveness of the proposed talent training mode is verified. The HCI system is based on knowledge of cognitive psychology. According to the characteristics and functions of an educational robot, the robot is combined with traditional teaching activities, and the robot-assisted talent training mode is designed. Robot-assisted talent training is a feasible training method that can improve the efficiency of talent training. Students have confidence in their learning skills before the course, and the confidence is further strengthened after the end of the course. After the course, the students have a stronger sense of cooperation. This study can provide theoretical ideas for the research of international service talent training mode. (shrink)

Through the overall situation of athletes’ competition pressure, the pressure level of participating athletes can be understood and revealed. Analyzing the sources of stress and influencing factors of athletes can find measures to relieve and reduce stress and provide theoretical reference for the regulation of athletes’ competition pressure. Based on genetic algorithm and neural network theory, this paper proposes a method of tracing the sports competition pressure based on genetic algorithm backpropagation neural network to solve the problem that (...) traditional neural network learning algorithm is slow and easy to fall into local minimum. There is no significant difference between male and female athletes in the level of competition pressure. Athletes have the same training methods and the same goals, and the competition pressure tends to be the same, with no obvious difference; athletes with different educational backgrounds have no significant differences in training, academics, sports injuries, interpersonal relationships, social expectations, and evaluations. Due to the particularity of the stage, the competition pressure of fourth-year undergraduate and third-year masters is significantly higher than that of other grades. The number of athletes participating in college table tennis tournaments has very significant differences in competition dimensions. There is significant difference in training and self-expectation dimension difference. The competition pressure of athletes who participated in the college table tennis championship for the first time was significantly higher than that of athletes who participated repeatedly. There were significant differences between athletes before and after adapting to the venue. Before adapting to the venue, the competition pressure of athletes is generally greater. After adapting to the venue, the competition pressure of athletes has been relieved. (shrink)

Brain-computer interface based on steady-state visual evoked potential has been widely studied due to the high information transfer rate, little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations, which can simultaneously elicit visual evoked potential and pupillary response to construct a hybrid BCI system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid (...) accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% for the supervised method and 91.88 ± 3.68% for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range. (shrink)

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which (...) has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task. (shrink)

This article is aimed to compare the efficacy and acceptability of exercise intervention with other nonpharmacological therapies in improving attention in ADHD patients and then rank those therapies. Methods. Relevant RCT studies from the Cochrane Library, Web of Science, Psycinfo, and PubMed were systematically searched from January 2011 to January 2020. The literature was screened, and the researchers extracted and used Stata16 and WinBugs1.4 independently to analyze the data. Results. The improvement of “attention deficit” in ADHD patients was measured in (...) 83 independent studies with 4,998 participants and 14 nonpharmacological therapies. Therapies were ranked as follows, according to the efficacy and acceptability of the cumulative probability area under the curve : meditation and cognitive; sport intervention therapy; vitamin and mineral; cognitive behavioral therapy; computer training; psychological education; working memory training; neurofeedback therapy; polyunsaturated fatty acids; mineral supplements; meditation; vitamin; biofeedback therapy; and school-based training. However, because the effect size of meditation and cognitive therapy was at a marginally significant level ), the robustness of the results was far lower than that of sport intervention therapy. Therefore, careful interpretation is needed in practice. Conclusion. Considering the efficacy and acceptability of various nonpharmacological therapies, sport intervention has a more stable effect on the improvement of attention deficit. Secondly, meditation and cognitive therapy, cognitive behavioral therapy, and computer training can also be effective complementary tools. (shrink)

Significant variation in performance in motor imagery tasks impedes their wide adoption for brain-computer interface applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training protocol based on the up-regulation of the alpha band relative power to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. (...) An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity of the frontal-parietal network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation. (shrink)

Dynamic, interactive sports require athletes to identify, pick-up and process relevant information in a very limited time, in order to then make an appropriate response. Perceptual-cognitive skills are, therefore, a key determinant of elite sporting performance. Recently, sport scientists have investigated ways to assess and train perceptual-cognitive skills, with one such method involving the use of blurred stimuli. Here, we describe the two main methods used to generate blur and then review the current findings in a sports context. Overall, (...) it has been shown the use of blur can enhance performance and learning of sporting tasks in novice participants, especially when the blur is applied to peripheral stimuli. However, while intermediate and expert level participants are relatively impervious to the presence of blur, it remains to be determined if they are positive effects on learning. In a final section, we describe some of the methodological issues that limit the application of blur and then discuss the potential use of virtual reality to extend the current research base in sporting contexts. (shrink)

Basic Digital Education is already planned to be integrated with the forthcoming curriculum for Austrian primary schools as it was already implemented for lower secondary schools in 2018. BDE includes the most essential and novel developments of Computational Thinking, which are fundamentally responsible for nurturing students' problem-solving skills. Thus, evaluating teaching materials, scaffolding guidelines, and assessments is becoming increasingly important for the successful implementation of CT in Austrian classrooms. This study is a part of a longitudinal multi-cycle educational design research (...) project aiming to explore how to foster CT and to raise the awareness, importance, and confidence of teachers and students in applying CT for everyday uses. Our paper focuses on a sub-study in which teaching units for grade 3 and 4 students were designed by combining an Open Educational Resource textbook and Physical Computing with the micro:bit device. The designed learning environment consists of three units and was implemented in two classes over 3 weeks. The two classes were further split into two groups each, to ensure better support during implementation. The class teachers received upfront teacher training and conducted pre- and post-test assessments with the students. The resulting data was then analyzed to gain insights into the effects on CT skills of the young learners. Results showed that combining block-based programming and physical computing devices could become a promising approach to promote computational thinking skills in lower school grades. Furthermore, the observed direction of the designed units supports low-barrier access to increase the desired uses of CT in classrooms. (shrink)

The question “How can humans learn efficiently to make decisions in a complex, dynamic, and uncertain environment” is still a very open question. We investigate what effects arise when feedback is given in a computer-simulated microworld that is controlled by participants. This has a direct impact on training simulators that are already in standard use in many professions, e.g., for flight simulators for pilots, and a potential impact on a better understanding of human decision making in general. Our (...) study is based on a benchmark microworld with an economic framing, the IWR Tailorshop. N=94 participants played four rounds of the microworld, each 10 months, via a web interface. We propose a new approach to quantify performance and learning, which is based on a mathematical model of the microworld and optimization. Six participant groups receive different kinds of feedback in a training phase, then results in a performance phase without feedback are analyzed. As a main result, feedback of optimal solutions in training rounds improved model knowledge, early learning, and performance, especially when this information is encoded in a graphical representation. (shrink)

Entropy-based algorithms have been suggested as robust estimators of electroencephalography (EEG) predictability or regularity. This study aimed to examine possible disturbances in EEG complexity as a means to elucidate the pathophysiological mechanisms in chronic stroke, before and after a brain computer interface (BCI)-motor observation intervention. Eleven chronic stroke subjects and nine unimpaired subjects were recruited to examine the differences in their EEG complexity. The BCI-motor observation intervention was designed to promote functional recovery of the hand in stroke subjects. (...) Fuzzy approximate entropy (fApEn), a novel entropy-based algorithm designed to evaluate complexity in physiological systems, was applied to assess the EEG signals acquired from unimpaired subjects and stroke subjects, both before and after training. The results showed that stroke subjects had significantly lower EEG fApEn than unimpaired subjects (p < 0.05) in the motor cortex area of the brain (C3, C4, FC3, FC4, CP3, CP4) in both hemispheres before training. After training, motor function of the paretic upper limb, assessed by the Fugl-Meyer Assessment-Upper Limb (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT) improved significantly (p < 0.05). Furthermore, the EEG fApEn in stroke subjects increased considerably in the central area of the contralesional hemisphere after training (p < 0.05). A significant correlation was noted between clinical scales (FMA-UL, ARAT, and WMFT) and EEG fApEn in C3/C4 in the contralesional hemisphere (p < 0.05). This finding suggests that the increase in EEG fApEn could be an estimator of the variance in upper limb motor function improvement. In summary, fApEn can be used to identify abnormal EEG complexity in chronic stroke, when used with BCI-motor observation training. Moreover, these findings based on the fApEn of EEG signals also expand the existing interpretation of training-induced functional improvement in stroke subjects. Entropy-based analysis might serve as a novel approach to understanding the abnormal cortical dynamics of stroke and the neurological changes induced by rehabilitation training. (shrink)

Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain–computer interface combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI–FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation rehabilitation (...) training and the other group received BCI combined with FES training. We constructed functional networks in both groups of patients based on direct directed transfer function and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: after rehabilitation training, the Fugl–Meyer assessment scale score was significantly improved in the BCI–FES group, and there was no significant difference in the FES group. Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI–FES group were improved after rehabilitation training. The node strength in the contralesional hemisphere and central region of patients in the BCI–FES group was significantly higher than that in the FES group after the intervention, and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI–FES group. These results suggest that BCI–FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI–FES rehabilitation training. (shrink)

The twenty-first century is arguably the century of computing. In such a world saturated by computing, Computational Thinking is now recognized as a foundational competency for being an informed citizen and being successful in STEM work. Nevertheless, how to effectively import different types of teaching methods in university courses is subjected to further evaluation. Currently, the arguments in favor of tangible robots including high interaction, great practicality, and specific operation results make themselves to be often used as a teaching medium (...) and tool for teaching activities between teachers and students. Hence, in addition to cultivating students with computational thinking ability, this paper discussed how to integrate tangible robots into project-based learning courses of thinking skills training to improve the learning performance of the computational thinking ability. This study conducted in one semester on the 105 students from three classes. Experimental results show that the project-based learning method integrated with the teaching material of robotic visual programs approach had significantly better effectiveness in improving students' learning achievements than the traditional teaching method integrated with paper practice teaching materials approach. Analysis of the questionnaire results showed that the proposed learning approach did not increase the students' cognitive burden. In sum, the proposed approach helps students' learning achievement and cognitive load. (shrink)

To shorten the travel time and improve comfort, the automatic train driving system is considered to replace manual driving. In this article, an automatic control method of computer application data-processing system based on artificial intelligence is proposed. An automatic train operation (ATO) introduced the structure and function of an autopilot system (train), optimized the train running on the target curve, introduced the basic principle of fuzzy generalized predictive control (PC) algorithm, and combined with the characteristics of ATO system (...) design the speed controller based on optimization algorithm, the target curve to make use of the designed controller to track and simulation validation. The experimental outcomes demonstrate that when the train runs to 90 s, the displacement difference reaches about 40 m, which proves that the fuzzy PC has better displacement tracking, punctual arrival, and higher stopping accuracy. (shrink)

Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at the 6-month follow-up. Therefore, the clinical improvement and the neurological changes (...) before, immediately after, and 6 months after training were explored in 14 chronic stroke subjects. The upper-limb motor function was assessed by Action Research Arm Test (ARAT) and Fugl-Meyer Assessment for Upper-Limb (FMA), and the neurological changes were assessed using resting-state functional magnetic resonance imaging. Repeated-measure ANOVAs indicated that long-term motor improvement was found by both FMA (F[2,26]= 6.367,p= 0.006) and ARAT (F[2,26]= 7.230,p= 0.003). Seed-based functional connectivity analysis exhibited that significantly modulated FC was observed between ipsilesional motor regions (primary motor cortex and supplementary motor area) and contralesional areas (supplementary motor area, premotor cortex, and superior parietal lobule), and the effects were sustained after 6 months. The fALFF analysis showed that local neuronal activities significantly increased in central, frontal and parietal regions, and the effects were also sustained after 6 months. Consistent results in FC and fALFF analyses demonstrated the increase of neural activities in sensorimotor and fronto-parietal regions, which were highly involved in the BCI-guided training.Clinical Trial Registration:This study has been registered atClinicalTrials.govwith clinical trial registration number NCT02323061. (shrink)

Regarding the continuous development of high-speed trains and the increase of running speeds, the aerodynamic design of high-speed trains has become significantly important, while reduction of drag and noise comprises a significant challenge in order to optimize aerodynamic design of high-speed trains. The design form factor of a high-speed train is highly influenced by aerodynamic aspects including aerodynamic drag, lift force, and noise. With the high-speed train as the object, the paper aims to take bionic concept as the entry point, (...) selecting the hummingbird as the bionic prototype and extracting bionic elements to establish a bionic train model. Then, the finite volume method was used for numerical simulation and analysis of the aerodynamic performance and aerodynamic noise of the bionic high-speed train. Computational results prove that drag and noise of the bionic head type were lower than those of the original train; drag of the head train of the bionic model was reduced by 2.21% in comparison with the original model, while the whole-train drag was reduced by 3.53%, indicating that drag reduction effects are available and implying that the bionic head type could reduce drag and noise. Noise sources of the bionic train are mainly located at positions with easy airflow separation and violent turbulence motion. Large turbulence energy is in bogie areas and mainly exists at the leeward side of the bogie area. Obviously, the bogie area is the major noise source of the train. Aerodynamic noise of the bionic train in far-field comprises a wide-frequency range. Noises were concentrated within 613 Hz~3150 Hz. When the bionic high-speed train ran at 350 km/h, through comparative analysis of total noise levels at observed points of the high-speed train, it is found that this position with the maximum noise level was 25 m away from the head train nose tip, with the maximum value of 88.4 dB. When the bionic train ran at 600 km/h, the maximum sound pressure level at the longitudinal point was 99.7 dB and the average noise level was 96.6 dB. When the running speed increased from 350 km/h to 600 km/h, the maximum noise level increased by 11.3 dB and the average noise level increased by 11.6 dB. Computation results of aerodynamic noise at the point which is 7.5 m away from the rail center show that the maximum aerodynamic noise level existed at the first-end bogie of the head train, while the noise level was larger at the position closer to the ground. (shrink)

Computer vision is widely used in manufacturing, sports, medical diagnosis, and other fields. In this article, a multifeature fusion error action expression method based on silhouette and optical flow information is proposed to overcome the shortcomings in the effectiveness of a single error action expression method based on the fusion of features for human body error action recognition. We analyse and discuss the human error action recognition method based on the idea of template matching to analyse (...) the key issues that affect the overall expression of the error action sequences, and then, we propose a motion energy model based on the direct motion energy decomposition of the video clips of human error actions in the 3 Deron action sequence space through the filter group. The method can avoid preprocessing operations such as target localization and segmentation; then, we use MET features and combine with SVM to test the human body error database and compare the experimental results obtained by using different feature reduction and classification methods, and the results show that the method has the obvious comparative advantage in the recognition rate and is suitable for other dynamic scenes. (shrink)

In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network are (...) used as the feature extraction network. In order to be efficient in computing and processing, the multilayer perceptron in the network model is implemented by a one-dimensional convolutional network. The input is a randomly sampled point set, and after a layer of perceptron, it outputs a feature set of n × 16. Then, the feature set is sent to two network branches, one is to continue to use the perceptron method to generate the feature set of n × 1024, and the other network is used to extract the local features of points. After the RGB basketball sports picture passes through the semantic segmentation network, a picture containing the target object is obtained, and the picture is input to the constructed feature fusion network model. After feature extraction is performed on the RGB image and the depth image, respectively, the RGB feature, the local feature of the point cloud, and the global feature are spliced and fused to form a feature vector of N × 1152. There are three branches for this vector network, which, respectively, predict the object position, rotation, and confidence. Among them, the feature dimensionality reduction is realized by one-dimensional convolution, and the activation function is the ReLU function. After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. It can be seen from the research results that the addition of the feature mapping module improves the recognition effect of the network to a certain extent. (shrink)

The radical transformations caused by the rapid development of information and communication technologies in the mid-1990s prompted the transition to the knowledge society which identified the key role of knowledge as the most important and valuable capital of organizations and had a decisive impact on the development of corporate training. In our study, we aimed to analyze the training methods used in American companies in the knowledge society, particularly, their feasibility, features, benefits and possible limitations. The results of (...) our study show that, at present, e-learning is considerably more widely used in American companies than instructor-led classroom training. Most organizations use blended learning which implies the combination of e-learning methods and instructor-led learning methods. A powerful stimulus for the wide implementation of e-learning methods was produced by the COVID-19 pandemic when the use of educational technologies made it possible for the companies not only to continue training and development of their employees but also to implement tactical and strategic practices which will have long-term implications for the future of the training industry. The most widely used e-learning methods in American companies are virtual classroom/webcast, online or computer-based methods, social learning, augmented reality, virtual reality, and artificial intelligence. In spite of certain limitations of e-learning methods, they have a number of important benefits which allow companies to expand access to training, save costs, increase adaptability and flexibility of learning programs, etc., and thus satisfy their needs in qualified employees. (shrink)

Tennis players have more physical training content, and the training items are complex. For athletes, training programs that adapt to their individual characteristics should be formulated according to their physical characteristics. The current development of big data has brought about changes in thinking, management, and business models. The combination of complex systems and big data can also make breakthroughs in the sports field. Based on this, this article proposes a tennis player training schedule intelligent formulation (...) system based on complex system big data. First of all, this article adopts the literature data method, comparative analysis method, experimental analysis method, etc., in-depth study of the concepts of big data, complex system, and the physical structure characteristics of tennis sports. This paper designs an intelligent system for making tennis players’ training schedule, which collects, transforms, and integrates tennis training data through the characteristics of big data. Then, the dynamic time regulation of tennis is performed through a complex system, and finally, the experimental system is analyzed. This article mainly analyzes the comparison of physical indicators between the experimental group and the control group before and after the experiment, the evaluation indicators of sports events, the strength training effects of tennis events, and the analysis of shoulder joint tests. There is no significant difference between the experimental group and the control group in the items before the experiment, P > 0.05, which suggests that the physical fitness of the two groups of athletes is similar; in the posttest data, the experimental group and the control group have significant differences, P < 0.05, indicating an effect from the experiment. In particular, in fan running, forward and backward strokes, and the serve, the scores of the experimental group were higher than those of the control group, indicating that the use of the formulated training system demonstrated significant results. (shrink)

Functional near-infrared spectroscopy has attracted increasing attention in the field of brain–computer interfaces owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network -based approach for implementing a subject-independent fNIRS- (...) class='Hi'>based BCI. A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication as well as that obtained using conventional shrinkage linear discriminant analysis. To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly. (shrink)

A passive brain–computer interface based upon functional near-infrared spectroscopy brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation state was continuously measured. Vector (...) phase analysis was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features along with six VPA features were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects’ data. Trajectory slopes of CORE vector magnitude and angle: m and m are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification. (shrink)