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This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)-electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids.

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The recognition of head movements plays an important role in human-computer interface domains. The data collected with image sensors or inertial measurement unit (IMU) sensors are often used for identifying these types of actions. Compared with image processing methods, a recognition system using an IMU sensor has obvious advantages in terms of complexity, processing speed, and cost. In this paper, an IMU sensor is used to collect head movement data on the legs of glasses, and a new approach for recognizing head movements is proposed by combining activity detection and dynamic time warping (DTW). The activity detection of the time series of head movements is essentially based on the different characteristics exhibited by actions and noises. The DTW method estimates the warp path distances between the time series of the actions and the templates by warping under the time axis. Then, the types of head movements are determined by the minimum of these distances. The results show that a 100% accuracy was achieved in the task of classifying six types of head movements. This method provides a new option for head gesture recognition in current human-computer interfaces.

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Introduction: Chronic stroke survivors with severe arm impairment have limited options for effective rehabilitation. High intensity, repetitive task practice (RTP) is known to improve upper limb function among stroke survivors who have some volitional muscle activation. However, clients without volitional movement of their arm are ineligible for RTP-based interventions and require hands-on facilitation from a clinician or robotic therapy to simulate task practice. Such approaches can be expensive, burdensome, and have marginal effects. Alternatively, supervised at-home telerehabilitation using muscle biofeedback may provide a more accessible, affordable, and effective rehabilitation option for stroke survivors with severe arm impairment, and could potentially help people with severe stroke regain enough volitional activation to be eligible for RTP-types of therapies. Feedback of muscle activity via electromyography (EMG) has been previously used with clients who have minimal or no movement to improve functional performance. Specifically, training to reduce unintended co-contractions of the impaired hand using EMG biofeedback may modestly improve motor control in people with limited movement. Importantly, these modest and covert functional changes may influence the perceived impact of stroke-related disability in daily life. In this manuscript, we examine whether physical changes following use of a portable EMG biofeedback system (Tele-REINVENT) for severe upper limb hemiparesis also relate to perceived quality of life improvements. Secondarily, we examined the effects of Tele-REINVENT, which uses EMG to quantify antagonistic muscle activity during movement attempt trials and transform individuated action into computer game control, on several different domains of stroke recovery. Methods: For this pilot study, nine stroke survivors (age = 37-73 years) with chronic impairment (Fugl-Meyer = 14-40/66) completed 30 1-hour sessions of home-based training, consisting of six weeks of gaming that reinforced wrist extensor muscle activity while attenuating coactivation of flexor muscles. To assess motor control and performance, we measured changes in active wrist ranges of motion, the Fugl-Meyer Assessment, and Action Research Arm Test. We also collected an EMG-based test of muscle control to examine more subtle changes. To examine changes in perceived quality of life, we utilized the Stroke Impact Scale along with participant feedback. Results: Results from our pilot data suggest that 30 sessions of remote training can induce modest changes on clinical and functional assessments, showing a statistically significant improvement of active wrist ranges of motion at the group level, changes that could allow some people with severe stroke to be eligible for other therapeutic approaches, such as RTP. Additionally, changes in motor control were correlated with the perceived impact of stroke on participation and impairment after training. We also report changes in corticomuscular coherence, which showed a laterality change from the ipsilesional motor cortex towards the contralesional hemisphere during wrist extension attempts. Finally, all participants showed high adherence to the protocol and reported enjoying using the system. Conclusion: Overall, Tele-REINVENT represents a promising telerehabilitation intervention that might improve sensorimotor outcomes in severe chronic stroke, and that improving sensorimotor abilities even modestly may improve quality of life. We propose that Tele-REINVENT may be used as a precursor to help participants gain enough active movement to participate other occupational therapy interventions, such as RTP. Future work is needed to examine if home-based telerehabilitation to provide feedback of individuated muscle activity could increase meaningful rehabilitation accessibility and outcomes for underserved populations.

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With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students' major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

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Background and aims: The present study aimed to synthesize existing quantitative evidence on the relationship between problematic smartphone use (PSU) and academic achievement with a focus on quantifying its magnitude and examining its potential moderators. Methods: Eligible studies were searched for up to February 10, 2023 in six different databases (i.e., MEDLINE, Current Contents Connect, PsycINFO, Web of Science, SciELO, and Dissertations & Theses Global). Studies were considered eligible if they provided information derived from self-report instruments that allowed statistical calculation of the relationship between PSU and academic achievement. Pooled effect sizes (r) were computed using a random-effects model. Meta-regressions were conducted to test the influence of study-level moderators on the relationship of interest. Influence analyses and a three-parameter selection model (3PSM) were conducted to examine the robustness of the results and publication bias, respectively. Results: A total of 33 effect sizes from 29 studies (n = 48,490) were retrieved. Results showed a small effect size (r = -0.110), which tended to be larger in samples consisting of students from elementary and middle schools. Discussion and Conclusions: Findings from the present study contribute to the understanding of a potential determinant of decreased academic achievement by providing evidence that PSU may be one of them.

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This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.

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The cognitive load experienced by humans is an important factor affecting their performance. Cognitive overload or underload may result in suboptimal human performance and may compromise safety in emerging human-in-the-loop systems. In driving, cognitive overload, due to various secondary tasks, such as texting, results in driver distraction. On the other hand, cognitive underload may result in fatigue. In automated manufacturing systems, a distracted operator may be prone to muscle injuries. Similar outcomes are possible in many other fields of human performance such as aviation, healthcare, and learning environments. The challenge with such human-centred applications is that the cognitive load is not directly measurable. Only the change in cognitive load is measured indirectly through various physiological, behavioural, performance-based and subjective means. A method to objectively assess the performance of such diverse measures of cognitive load is lacking in the literature. In this paper, a performance metric for the comparison of different measures to determine the cognitive workload is proposed in terms of the signal-to-noise ratio. Using this performance metric, several measures of cognitive load, that fall under the four broad groups were compared on the same scale for their ability to measure changes in cognitive load. Using the proposed metrics, the cognitive load measures were compared based on data collected from 28 participants while they underwent n-back tasks of varying difficulty. The results show that the proposed performance evaluation method can be useful to individually assess different measures of cognitive load.

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Objective: Wordbot, a chatbot designed for gamified education, transforms the process of memorizing complex medical terminology into an engaging and enjoyable activity for medical students. Taking inspiration from the "guessing words" game, Wordbot aims to improve medical students' learning outcomes by making the vocabulary memorization process more memorable. Materials and Methods: Wordbot, which can be implemented on the LINE platform, was created for this research, specifically to improve medical terminology learning. Wordbot incorporated mobile devices and personal computer-compatible flashcard games with features such as user ranking and personalization to enhance motivation and optimize learning outcomes. In the experimental research setting, half of a total of 48 nursing students were randomly assigned to use Wordbot for 4 months, and the other half were assigned to a control group relying on self-study without the help of Wordbot. Both groups received pretest and post-test to assess their respective learning of medical terminology. In this study, a statistical t-test was used to analyze the results between the two groups. In addition, user usability testing was conducted to evaluate the usability of Wordbot and gather feedback on user experience. Results: The results of this study have demonstrated that Wordbot is effective in facilitating students learning of medical terminology. Students experienced a significant improvement in their knowledge of medical terminology. An average user usability test score of 83.25 indicated that users' satisfaction with Wordbot is high. Conclusion: Incorporating gamification and personalization elements in Wordbot can significantly improve the overall enjoyment of the learning process. By participating in diverse interactive activities, users can effectively enhance their proficiency in spelling, recognition, and speaking. Wordbot utilizes sophisticated algorithms to generate customized questions based on identified mistakes, which facilitate error identification and correction. The robust findings of this study overwhelmingly support Wordbot's role as a convenient and easily accessible tool for learning medical terminology. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Chang Gung Medical Foundation (Protocol code: 202201586B0, date of approval: 8 November 2022). We obtained informed consent from all of our study participants regarding their willingness to participate in this study.

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The primary goals of behavior analysis are the prediction and influence of behavior. These goals are largely achieved through the identification of functional relations between behaviors and the stimulating environment. Behavior-behavior relations are insufficient to meet these goals. Although this environment-behavior approach has been highly successful when applied to public behaviors, extensions to private events have been limited. This article discusses technical and conceptual challenges to the study of private events. We introduce a neurobiological-behavioral approach which seeks to understand private behavior as environmentally controlled in part by private neurobiological stimuli. These stimuli may enter into functional relations with both public and private behaviors. The analysis builds upon several current approaches to private events, delineates private behaviors and private stimulation, and emphasizes the reciprocal interaction between the two. By doing so, this approach can improve treatment and assessment of behavior and advance understanding of concepts such as motivating operations. We then describe the array of stimulus functions that neurobiological stimuli may acquire, including eliciting, discriminative, motivating, reinforcing, and punishing effects, and describe how the overall approach expands the concept of contextual influence. Finally, we describe how advances in behavioral neuroscience that enable the measurement and analysis of private behaviors and stimuli are allowing these once private events to affect the public world. Applications in the area of human-computer interfaces are discussed.

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Virtual reality (VR) allows visuotactile interaction in a virtual environment. VR has several potential applications such as surgical training, phobia treatments, and gait rehabilitation. However, further interface development is required. Therefore, the objective of this study was to develop a noninvasive wearable device control to a VR gait training program. It consists of custom-made insoles with vibratory actuators, and plantar pressure sensor-based wireless interface with a VR game. System usability testing involved a habituation period and three gaming sessions. Significant gait improvement was associated with game scores (P < 0.05). This VR gait training system allowed real-time virtual immersive interaction with anticipatory stimulus and feedback during gait.

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Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user's body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have varied in medium, method, and application. Modern developments in the areas of machine perception have seen the rise of single-camera, skeletal model, hand-gesture identification algorithms, such as media pipe hands (MPH). This paper evaluates the applicability of these modern HGR algorithms within the context of alternative control. Specifically, this is achieved through the development of an HGR-based alternative-control system capable of controlling of a quad-rotor drone. The technical importance of this paper stems from the results produced during the novel and clinically sound evaluation of MPH, alongside the investigatory framework used to develop the final HGR algorithm. The evaluation of MPH highlighted the Z-axis instability of its modelling system which reduced the landmark accuracy of its output from 86.7% to 41.5%. The selection of an appropriate classifier complimented the computationally lightweight nature of MPH whilst compensating for its instability, achieving a classification accuracy of 96.25% for eight single-hand static gestures. The success of the developed HGR algorithm ensured that the proposed alternative-control system could facilitate intuitive, computationally inexpensive, and repeatable drone control without requiring specialised equipment.

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Introduction: Emotion plays a vital role in understanding activities and associations. Due to being non-invasive, many experts have employed EEG signals as a reliable technique for emotion recognition. Identifying emotions from multi-channel EEG signals is evolving into a crucial task for diagnosing emotional disorders in neuroscience. One challenge with automated emotion recognition in EEG signals is to extract and select the discriminating features to classify different emotions accurately. Methods: In this study, we proposed a novel Transformer model for identifying emotions from multi-channel EEG signals. Note that we directly fed the raw EEG signal into the proposed Transformer, which aims at eliminating the issues caused by the local receptive fields in the convolutional neural networks. The presented deep learning model consists of two separate channels to address the spatial and temporal information in the EEG signals, respectively. Results: In the experiments, we first collected the EEG recordings from 20 subjects during listening to music. Experimental results of the proposed approach for binary emotion classification (positive and negative) and ternary emotion classification (positive, negative, and neutral) indicated the accuracy of 97.3 and 97.1%, respectively. We conducted comparison experiments on the same dataset using the proposed method and state-of-the-art techniques. Moreover, we achieved a promising outcome in comparison with these approaches. Discussion: Due to the performance of the proposed approach, it can be a potentially valuable instrument for human-computer interface system.

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Innovative, pedagogically informed instructional design is instrumental in increasing student engagement and improving learning outcomes in online learning environments. Interactive learning resources provide students with the opportunity to engage with content in a more personalised manner. H5P (HTML 5 Package) is a collaborative platform that allows developers to create interactive content and has been regularly used in education settings. Some evidence suggests using interactive H5P resources in online education courses could lead to greater student engagement. However, to date, there has been little investigation into whether H5P resources can improve student learning outcomes. The current study aimed to assess whether using interactive H5P resources improved assessed learning outcomes in an online undergraduate psychology course. A randomized cross-over design was utilized to test whether students exposed to H5P interactive videos had improved assessment results when compared to a control group. This study found no meaningful differences in assessment scores between students exposed to H5P versus those that were not. There was low overall engagement with the interactive content. However, students who did engage with the resources reported a positive experience and indicated a preference for more interactive elements in future courses. Future research should extend on the instructional design obstacles identified in this study, for example, by examining whether improved accessibility and education on the benefits of interactive resources would increase engagement and grades.

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The urgent shift to online distance teaching and learning during the Covid-19 pandemic presented teachers with unique pedagogical, technological, and psychological challenges. The aim of this study was to map the main positive and negative experiences of teachers during this transition, as well as to examine intra- and interpersonal factors that affected teachers' ability to cope effectively with the challenges of online distance teaching. We used a mixed-method approach that combined qualitative (interviews) and quantitative (questionnaires) analyses. The interviews were analyzed using a grounded theory approach, specifically a bottom-up analysis, which led to the identification of five primary categories reflecting teachers' main concerns in online distance teaching (i.e., social, emotional, cognitive, pedagogical, and system support. The two most prominent categories were pedagogy and emotions, illustrating their centrality in teachers' experiences. A regression analysis of the questionnaires' data revealed that the two main variables which predicted both positive and negative experiences in online distance teaching were self-efficacy and teachers' attitudes towards technology integration in teaching. Findings of this study allow formulation of guidelines to promote factors related to positive experiences in online distance teaching.

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BACKGROUND: The most suitable and reliable inference engines for Clinical Decision Support Systems in nursing clinical practice have rarely been explored. PURPOSE: This study examined the effect of Clinical Diagnostic Validity-based and Bayesian Decision-based Knowledge-Based Clinical Decision Support Systems on the diagnostic accuracy of nursing students during psychiatric or mental health nursing practicums. METHODS: A single-blinded, non-equivalent control group pretest-posttest design was adopted. The participants were 607 nursing students. In the quasi-experimental design, two intervention groups used either a Knowledge-Based Clinical Decision Support System with the Clinical Diagnostic Validity or a Knowledge-Based Clinical Decision Support System with the Bayesian Decision inference engine to complete their practicum tasks. Additionally, a control group used the psychiatric care planning system without guidance indicators to support their decision-making. SPSS, version 20.0 (IBM, Armonk, NY, USA) was used for data analysis. chi-square (χ2) test and one-way analysis of variance (ANOVA) used for categorical and continuous variables, respectively. Analysis of covariance was done to examine the PPV and sensitivity in the three groups. RESULTS: Results for the positive predictive value and sensitivity variables indicated that decision-making competency was highest in the Clinical Diagnostic Validity group, followed by the Bayesian and control groups. The Clinical Diagnostic Validity and Bayesian Decision groups significantly outperformed the control group in terms of scores on a 3Q model questionnaire and the modified Technology Acceptance Model 3. In terms of perceived usefulness and behavioral intention, the Clinical Diagnostic Validity group had significantly higher 3Q model and modified Technology Acceptance Model 3 scores than the Bayesian Decision group, which had significantly higher scores than the control group. CONCLUSION: Knowledge-Based Clinical Decision Support Systems can be adopted to provide patient-oriented information and assist nursing student in the rapid management of patient information and formulation of patient-centered care plans.

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Haptics plays a significant role not only in the rehabilitation of neurological disorders, such as stroke, by substituting necessary cognitive information but also in human-computer interfaces (HCIs), which are now an integral part of the recently launched metaverse. This study proposes a unique, soft, monolithic haptic feedback device (SoHapS) that was directly manufactured using a low-cost and open-source fused deposition modeling (FDM) 3D printer by employing a combination of soft conductive and nonconductive thermoplastic polyurethane (TPU) materials (NinjaTek, USA). SoHapS consists of a soft bellow actuator and a soft resistive force sensor, which are optimized using finite element modeling (FEM). SoHapS was characterized both mechanically and electrically to assess its performance, and a dynamic model was developed to predict its force output with given pressure inputs. We demonstrated the efficacy of SoHapS in substituting biofeedback with tactile feedback, such as gripping force, and proprioceptive feedback, such as finger flexion-extension positions, in the context of teleoperation. With its intrinsic properties, SoHapS can be integrated into rehabilitation robots and robotic prostheses, as well as augmented, virtual, and mixed reality (AR/VR/MR) systems, to induce various types of bio-mimicked feedback.

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Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor - patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (N = 12) were able to develop an understanding of how AI and chatbots will affect their future daily work. Although basic attitudes toward the use of AI were positive, the students also expressed concerns. There were high levels of agreement regarding the use of AI in administrative settings (83.3%) and research with health-related data (91.7%). However, participants expressed concerns that data protection may be insufficiently guaranteed (33.3%) and that they might be increasingly monitored at work in the future (58.3%). The evaluations indicated that future physicians want to engage more intensively with AI in medicine. In view of future developments, AI and data competencies should be taught in a structured way during the medical curriculum and integrated into curricular teaching.

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Gamification is mainly used to increase user engagement and motivation, hence increasing the user base and user activity. Defined by applying game elements to non-gaming contexts, gamification is mostly integrated with software applications in order to provide a gameful experience for users. Education has been one of the areas where gamification studies have focused a lot during the last decade. Young students with the age range of 7-12 years old (K-6) require different teaching methods to use their full potential. However, the methods and principles presented in studies on gamification and its application in education are not dedicated to K-6 students. Furthermore, the evolution of video games has brought new opportunities to develop new gamification elements and principles. In this research, the easter egg element has been implemented as a gamification element. Easter eggs can trigger children's curiosity by encouraging them to find all the Easter eggs, promising special rewards and perks. Additionally, a gamified approach is proposed for implementing a gamified software application for K-6 students. Based on the proposed approach, Science Island is implemented as an online gamified web application for K-6 students. In order to assess the proposed approach, a group of 47 sixth-grade students was selected to use the application for an observation period of 2 months. Feedbacks from students showed that more than 82% of the students agreed with the effectiveness of gamification in their educational performance. Additionally, the results from the data analysis revealed that students' learning performance was improved significantly after applying gamification elements; showing an increase of 0.63 in average quiz score from the second month compared to the first month. Furthermore, the user activity rate at the end of the observation period showed increased motivation among students for using the software application.

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This study investigated the effects of smartphone use on the perceived academic performance of elementary school students. Following the derivation of four hypotheses from the literature, descriptive analysis, t testing, one-way analysis of variance (ANOVA), Pearson correlation analysis, and one-way multivariate ANOVA (MANOVA) were performed to characterize the relationship between smartphone behavior and academic performance with regard to learning effectiveness. All coefficients were positive and significant, supporting all four hypotheses. We also used structural equation modeling (SEM) to determine whether smartphone behavior is a mediator of academic performance. The MANOVA results revealed that the students in the high smartphone use group academically outperformed those in the low smartphone use group. The results indicate that smartphone use constitutes a potential inequality in learning opportunities among elementary school students. Finally, in a discussion of whether smartphone behavior is a mediator of academic performance, it is proved that smartphone behavior is the mediating variable impacting academic performance. Fewer smartphone access opportunities may adversely affect learning effectiveness and academic performance. Elementary school teachers must be aware of this issue, especially during the ongoing COVID-19 pandemic. The findings serve as a reference for policymakers and educators on how smartphone use in learning activities affects academic performance.

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The current study examined the use of electronic textbooks designed as mobile applications for learning vocabulary in English among Iranian university students. To this end, 95 university students in an experimental (N = 50) and a control group (N = 45) participated in the study. An explanatory sequential mixed methods design was employed and over an academic semester, the participants used either traditional materials or mobile-based electronic textbooks for learning 600 words in English. To assess the outcomes from different learning conditions, receptive knowledge of the target vocabulary items was tested in three junctures of time (i.e. pre-, post-, and delayed post-test). Additionally, open-ended questionnaires and interviews were used to collect qualitative data from the experimental group to further investigate their perceptions of using mobile-based electronic textbooks for vocabulary learning. The findings revealed a significant main effect for time and both groups significantly improved their vocabulary knowledge from pre-test to post-test. Moreover, a significant main effect was found for using electronic textbooks on mobile devices, and the experimental group outperformed the control group on the post- and delayed post-tests. The qualitative findings revealed three perceived benefits, namely episodic learning, easy access to materials, and enhanced enjoyment for mobile assisted vocabulary learning through electronic textbooks. The perceived challenges were related to health concerns, distractions associated with mobile environments, and external pressure resulting from excessive mobile use among the participants. In general, the findings of the study shed light on the potential offered by mobile-based textbooks for learning English vocabulary, with implications for teachers and materials developers in language teaching programs.