Automatic Sleep Staging Using BiRNN with Data Augmentation and Label Redirection

Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation factors. In this paper, we propose an automatic sleep stage classification model based on the Bidirectional Recurrent Neural Network (BiRNN) with data bundling augmentation and label redirection for accurate sleep staging. Through extensive analysis, we discovered that the incorrect classification labels are primarily concentrated in the transition and nonrapid eye movement stage I (N1). Therefore, our model utilizes a sliding window input to enhance data bundling and an attention mechanism to improve feature enhancement after label redirection. This approach focuses on mining latent features during the N1 and transition periods, which can further improve the network model's classification performance. We evaluated on multiple public datasets and achieved an overall accuracy rate of 87.3%, with the highest accuracy rate reaching 93.5%. Additionally, the network model's macro F1 score reached 82.5%. Finally, we used the optimal network model to study the impact of different EEG channels on the accuracy of each sleep stage.

Computational Intelligence and Soft Computing Paradigm for Cheating Detection in Online Examinations

Covid-19 has been a life-changer in the sphere of online education. With complete lockdown in various countries, there has been a tumultuous increase in the need for providing online education, and hence, it has become mandatory for examiners to ensure that a fair methodology is followed for evaluation, and academic integrity is met. A plethora of literature is available related to methods to mitigate cheating during online examinations. A systematic literature review (SLR) has been followed in our article which aims at introducing the research gap in terms of the usage of soft computing techniques to combat cheating during online examinations. We have also presented state-of-the-art methods followed, which are capable of mitigating online cheating, namely, face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and detection of IP spoofing. A discussion on improvement of existing online cheating detection systems has also been presented.

Where to Look? Alcohol, Affect, and Gaze Behavior During a Virtual Social Interaction.

COVID-19 forced social interactions to move online. Yet researchers have little understanding of the mental health consequences of this shift. Given pandemic-related surges in emotional disorders and problematic drinking, it becomes imperative to understand the cognitive and affective processes involved in virtual interactions and the impact of alcohol in virtual social spaces. Participants (N=246) engaged in an online video call while their gaze behavior was tracked. Prior to the interaction, participants were randomly assigned to receive an alcoholic or control beverage. Participants' affect was repeatedly assessed. Results indicated that a proportionally larger amount of time spent gazing at oneself (vs. one's interaction partner) predicted significantly higher negative affect after the exchange. Further, alcohol independently increased self-directed attention, failing to demonstrate its typically potent social-affective enhancement in this virtual context. Results carry potential implications for understanding factors that increase risk for hazardous drinking and negative affect in our increasingly virtual world.

Development of Personalized Sleep Induction System based on Mental States

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7% and stop auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants. © 2023 IEEE.

Infantile spasms: guidance for pediatricians

In a cross-sectional study of 100 parents of children with infantile spasms, the median time from spasm onset to first visit with any health care provider was 5 days, but the median time from onset to first visit with an "effective provider" (one who provided both accurate diagnosis and prescription for appropriate first-line treatment) was 24 days,5 a delay attributed at least in part to poor awareness of the condition among providers.5 Given that worse outcomes may be associated with even a 1-week delay in treatment from onset, it is critical that pediatric health care providers are proficient in recognizing this condition.67 Clinical features Infantile spasms An infantile spasm is brief and abrupt, generally 1 to 3 seconds, with muscle contraction that can include the head, neck, trunk, and/or extremities. Home video recording, first advised by the Child Neurology Society to streamline IESS management at the onset of the COVID-19 pandemic, has since been endorsed as a continued recommendation toward timely intervention.4 In preparing to evaluate a patient with possible IESS, pediatricians should ask caregivers to record suspected events. The EEG pattern during the spasm itself is a high-amplitude sharp or slow wave followed by a relative electrodecrement. Because the interictal EEG is generally abnormal, it is not necessary to capture a spasm during the EEG recording to support the diagnosis. Workup for an underlying etiology if not known is important not only because some are associated with other health concerns requiring monitoring and intervention, but also because it can guide management, as some etiologies may respond better to different treatment approaches.9 Identification may also guide appropriate counseling of families, including prognostication and possible genetic counseling.

The flexibility of the intermediate vs. wholistic/analytic styles-an eye tracking study

In the aftermath of the COVID-19 pandemic, the educational system is increasingly incorporating twenty-first-century skills, such as online learning, that require learners to demonstrate cognitive flexibility. Cognitive flexibility is the ability to quickly reconfigure our minds to meet the task demands. This study investigates the degree of cognitive flexibility of the wholistic-intermediate-analytic dimensions, by classifying patterns of Eye Movements (EM) and behavioural data. Using the E-CSA-W/A test, 113 participants were classified based on their tendency towards a particular style (wholistic/intermediate/analytic). Results indicate that wholistics and intermediates demonstrated greater cognitive flexibility in adapting to the task requirements than the analytics. Analytics were slower at completing the test and made more transitions between Areas of Interest than the other groups. Finally, while the behavioural data demonstrate quantitative differences between the groups, EM provides qualitative information regarding the cognitive process that leads to the response. Theoretical, methodological, and practical contributions are discussed.Copyright © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Engagement Estimation using Time-series Facial and Body Features in an Unstable Dataset

The global education sector has been deeply shaken by COVID-19 and forced to shift to an online teaching model. However, the lack of face-to-face communication and interaction in online learning is critical to high-quality teaching and learning. Research on engagement is a crucial part of solving this problem. Because engagement is of time-series data with an ongoing change, research datasets used for engagement analysis need a certain preprocessing method to capture time-series related engagement features. This research proposed a novel deep learning preprocessing method for improving engagement estimation using time-series facial and body information to restore traditional scenes in online learning environments. Such information includes head pose, mouth shape, eye movement, and body distance from the screen. We conducted a preliminary experiment on the DAiSEE dataset for engagement estimation. We applied skipped moving average in data preprocessing to reduce the influence of the extracted noises and oversampled the low engagement level data to balance the engaged/unengaged data. Since engagement is continuous and cannot be captured at a particular instant in time or single images, temporal video classification generally performs better than static classifiers. Therefore, we adopted long short-term memory (LSTM) and Quasi-recurrent neural networks (QRNNs)sequence models to train models and achieved the correct rate of 55.7% (LSTM) and 51.1% (QRNN) using the original key points extracted from OpenPose. Finally, we proposed the optimization structure network achieved the engagement estimation correct rate of 68.5% in proposed LSTM models and 64.2% in QRNN models. The achieved correct rate is 10% higher than the baseline in the DAiSEE dataset. © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

Monitoring Engagement in Online Classes Through WiFi CSI

Due to the Covid-19 pandemic, people have been forced to move to online spaces to attend classes or meetings and so on. The effectiveness of online classes depends on the engagement level of students. A straightforward way to monitor the engagement is to observe students' facial expressions, eye gazes, head gesticulations, hand movements, and body movements through their video feed. However, video-based engagement detection has limitations, such as being influenced by video backgrounds, lighting conditions, camera angles, unwillingness to open the camera, etc. In this work, we propose a non-intrusive mechanism of estimating engagement level by monitoring the head gesticulations through channel state information (CSI) of WiFi signals. First, we conduct an anonymous survey to investigate whether the head gesticulation pattern is correlated with engagement. We then develop models to recognize head gesticulations through CSI. Later, we plan to correlate the head gesticulation pattern with the instructor's intent to estimate the students' engagement. © 2023 IEEE.

How Usable Is Coursera? A Usability Analysis through Eye-Tracking and Authentic Tasks

Massive Open Online Courses (MOOCs) are considered learning environments that eliminate many learning barriers. Online courses in MOOCs have become an opportunity for everyone during the lockdown of the COVID-19 outbreak. However, usability issues may cause problems such as high dropout rates and lack of learner's motivation. Therefore, in this study, the usability of Coursera, one of the most known MOOCs in the world, was evaluated. The evaluation was performed with ISO 9241-11 standard. The environment's effectiveness, efficiency, and satisfaction were evaluated with the authentic tasks requested to be done in Coursera. Additionally, the findings were supported by eye-tracking metrics such as fixation duration, fixation counts, heat maps, and gaze plots. Twelve individuals (six females, six males) participated in the authentic tasks, and three individuals (two females, one male) participated in the eye-tracking phase. Results of the study revealed that most participants successfully performed the authentic tasks and are generally satisfied with the usability of the environment. However, considering eye-tracking findings and Coursera Usage Satisfaction Survey, some usability problems such as inadequate language support and the difficulty of using the search feature emerged. In the end, possible reasons were discussed, and the suggestions were presented for usability improvements.

E-commerce Website User Engagement Analysis: An Eye Tracking Viewpoint

E-Commerce is the current day lifeline for many. After the COVID-19 pandemic the popularity of the E-Commerce increased rapidly people are now more accustomed to it. It is very important to analyze the impact of a E-Commerce web page on the customer in order to understand the customer behaviour. Where the person looks on a E commerce web page means a lot. It is challenging to analyze visual events while tracking them. Additionally, interaction patterns in significant regions of an interactive platform can be found using eye tracking data. Attention modelling for applications has become a new area of study in computer vision. Without temporal information, existing models are unable to capture the dynamic aspects of the actual attention process in free-viewing applications.To solve this problem, a solution based on an application-based saccadic model to simulate human visual dynamics while viewing applications is proposed. Eye fixes and saccades can be used to interpret a person's intention or goal in a certain circumstance. To track the user's vision, we have used pupil saccade fixation mechanism in the proposed methodology. We collect the pupil imprints based on the view, analyze the data from view samples, and recommend the optimum location to display an advertisement in any e-commerce websites. The system proves to be a better option to traditional methods as it gives 94% accuracy. © 2022 IEEE.

The flexibility of the intermediate vs. wholistic/analytic styles - an eye tracking study

In the aftermath of the COVID-19 pandemic, the educational system is increasingly incorporating twenty-first-century skills, such as online learning, that require learners to demonstrate cognitive flexibility. Cognitive flexibility is the ability to quickly reconfigure our minds to meet the task demands. This study investigates the degree of cognitive flexibility of the wholistic-intermediate-analytic dimensions, by classifying patterns of Eye Movements (EM) and behavioural data. Using the E-CSA-W/A test, 113 participants were classified based on their tendency towards a particular style (wholistic/intermediate/analytic). Results indicate that wholistics and intermediates demonstrated greater cognitive flexibility in adapting to the task requirements than the analytics. Analytics were slower at completing the test and made more transitions between Areas of Interest than the other groups. Finally, while the behavioural data demonstrate quantitative differences between the groups, EM provides qualitative information regarding the cognitive process that leads to the response. Theoretical, methodological, and practical contributions are discussed.

Mental States and Cognitive Performance Monitoring for User-Centered e-Learning System: A Case Study

The unprecedented long-term online learning caused by COVID-19 has increased stress symptoms among students. The e-learning system reduces communications between teachers and students, making it difficult to observe student's mental issues and learning performance. This study aims to develop a non-intrusive method that can simultaneously monitor stress states and cognitive performance of student in the scenario of online education. Forty-three participants were recruited to perform a computer-based reading task under stressful and non-stressful conditions, and their eye-movement data were recorded. A tree ensemble machine learning model, named LightGBM (Light Gradient Boosting Machine), was utilized to predict stress states and reading performance of students with an accuracy of 0.825 and 0.793, respectively. An interpretable model, SHAP (SHapley Additive exPlanation), was used to identify the most important eye-movement indicators and their effects on stress and reading performance. The proposed model can serve as a foundation for further development of user-centred services in e-learning system. © 2022 The authors and IOS Press.

Proct-Xam - AI Based Proctoring

The COVID-19 pandemic has dramatically changed the education sector, which led to a rise in online learning. E-learning has exponentially increased and it has become difficult to conduct exams offline too. Need for online education and conducting online exams has led various institutes and educational sectors to develop a platform where exams can be proctored. The ability to proctor online examinations more effectively has become a crucial factor in the education sector. Presently, human proctoring is the most common approach of evaluation, by monitoring them during exams via a webcam. It has become challenging for supervisors/teachers to examine and conduct online exams. In addition to that, studies say that there has been a significant rise in cheating during online exams. The main objective of the project is to conduct honest, faithful examination by developing an AI based online proctoring system which will help teachers to check the examinee's authenticity, eliminate suspicious behaviour during the exam and keep track of students throughout the exam time. In addition to that, conducting smooth and honest examinations will increase everyone's trust in the online education sector. Proposed project is a website developed using AI algorithms such as CNN/RNN algorithm which monitors students during the exam, allowing students to attend the exam from any location, which will help in detecting any malicious activities via webcam and guarantee fair evaluation of exams. Vision based tracking consists of Eye ball tracking, Lip movement, additional member detection in frame and more. The website will also ensure that the candidate is sharing his/her screen. Conclusion, the proposed project provides an online platform with features like view students screen anytime, send warning messages, terminate someone's exam, eye tracker, lip tracker for conducting effective exams. © 2022 IEEE.

Oculomotor apraxia. Sequelae of COVID or not?

A 65-year-old man, victim of COVID in March 2020, escaped with severe functional sequelae, notably oculomotor and vestibular. The etiology remains difficult. The diagnostic approach is complicated by the context. Copyright © 2022 Elsevier Masson SAS

Tracking Students? Visual Attention During E-Learning in the Time of COVID-19

The COVID-19 virus pandemic has forced many educational institutions to use various platforms to conduct online classes. Online learning can hardly replace the classic form of teaching, and the involvement of students is often not at a satisfactory level. This paper researches the method of detecting students' attention during online learning based on monitoring eyes and face tracking. The analysis of the face, eye, and the probability of opening the right and left eye, enable the detection of the level of students' visual attention. The paper proposes a system that detects the visual attention of students by using a smartphones' camera and presents the experimental results obtained by using this system during the Covid-19 pandemic.

Reviews of articles on medicinal herbs

Preliminary studies have been conducted on the effect of lavender on people's stress;however, there have been inconsistencies in the results. [...]the current study aimed to estimate the pooled effect of lavender on stress using systematic review and meta-analysis. [...]of combining the studies, stress scores after using lavender in the intervention group showed a significant decrease compared to the control group. Acute vertigo can significantly impact the quality of life. [...]research is targeting therapeutics that can improve symptoms and improve wellbeing. EGb has several actions which may directly or indirectly be beneficial for vertigo, including antiinflammatory, antioxidant, neuroprotective, circulatory stimulating, and platelet-activating factor (PAF) inhibitory effects. [...]the current study aimed to evaluate the efficacy of EGb in addition to vestibular exercises in central vestibular vertigo caused by vertebrobasilar ischaemia.

Investigating the perceived effectiveness of eye movement desensitisation reprocessing (EMDR) treatment in adult survivors of childhood sexual abuse and the impact on neuropsychological, emotional and behavioural functioning and quality of life: A case series analysis

Childhood sexual abuse (CSA) is an ongoing issue (WHO, 2017) with 1 in 20 children annually reported as being abused in the UK (NSPCC, 2019;Radford et al, 2011). Many child survivors are of adolescent or young adult age before they request help from relevant services (HAVOCA, 2021;NSPCC, 2018;Noel, Dogaru, and Ellis, 2015;Flatley, 2017). The aim of this study was to investigate the perceived experiences of six young adult female survivors of CSA of their EMDR treatment as well as an assessment of the changes in the individual trauma stress response. This case series analysis explored (a) neuropsychological, emotional (namely low selfesteem, anxiety and depression), behavioural functioning and quality of life issues using descriptive statistics via outcome measures conducted before, during and after treatment and (b) client perspectives through qualitative interviewing at one-month follow-up to ensure adequate time was allowed to monitor changes using Thematic Analysis [TA]. The study setting was within the Improving Access to Psychological Therapies (IAPT) program framework, established to ensure service users accessing NHS treatment are presented with choice in their treatment.The triangulation of data in this study allowed for a deeper analysis of the experiences of adult CSA survivors undergoing EMDR treatment beyond an examination of differences in pre and post outcome measures. The descriptive statistics suggested overall positive changes in participant functioning in all measured domains (three positive, two moderate outcome cases and one sceptical no-improvement case;based on independent research rater feedback) however variable differences in neuropsychological processing from pre- to post-treatment. The descriptive statistics were limited in their generalisability because of certain limitations in data collection as inhibited by COVID-19 restrictions and because of the small sample size. Three key themes were identified in the qualitative analysis which contributed to the literature on treatment of adult CSA survivors by identifying which factors the clients identified as helpful and unhelpful to their treatment. These themes were identified as being an 'Unhelpful' process (service time restrictions, fear of the lack of confidentiality, fear of emotional reprocessing), 'Helpful' aspects of therapy (client choice in treatment, therapist interpersonal and professional skills, psychological resourcing, idiosyncratic approaches) and 'Mixed Responses' due to COVID-19 (face-to-face vs remote working). Overall, this study contributed to the literature about EMDR treatment for adult CSA survivors by shedding insight into the perceived experiences of clients and providing further evidence for the efficacy of this treatment.Importantly, further research could investigate a potentially larger sample, emphasis on neuropsychological functioning, and within differing settings, to understand deficits within the current study. A qualitative study of the perceptions and experiences of childhood sexual abuse survivors who opt for CBT over EMDR might lead to recommendations for changes in protocol that would make EMDR more acceptable. There is scope to further investigate EMDR as a reliable and valid treatment option within NHS IAPT settings, alongside essential service development in therapist training programmes to support the growing need for treatment of multiple-trauma and/or Complex-PTSD (ICD-11, International Classification of Diseases-11, 2018). (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Effective online learning management system to improve and enhance the online learning and student engagement experience

With the spread of Covid-19, there has been a shift towards distance learning and teachers find it difficult to keep track of students who are attentive during class. Unlike before, where the traditional classroom environment helped teachers keep track of the students who are not fully concentrating during the lessons. This shift to online learning has made teachers find it much more difficult to keep track of students who are idling during their lecture period. For this the following solution is proposed to introduce an extension to help teachers integrate with existing video conferencing platforms. This solution will help teachers to know whether the student has been attentive during class, by keeping track of their peripheral device movements, such as mouse movements or keystrokes. Previous studies have been conducted to keep track of student's eye movement and browser history, but no solution has been developed to easily plug and play' into an existing platform for teachers to get real time progress of a student's interaction to the lecture. The main objective of this research will be to help enhance the learning experience of a student by keeping the teacher aware of the student's progress just like in a traditional classroom environment. © 2022 ACM.

Trait anxiety affects attentional bias to emotional stimuli across time: A growth curve analysis.

Many studies have illustrated the close relationship between anxiety disorders and attentional functioning, but the relationship between trait anxiety and attentional bias remains controversial. This study examines the effect of trait anxiety on the time course of attention to emotional stimuli using materials from the International Affective Picture System. Participants with high vs. low trait anxiety (HTA vs. LTA) viewed four categories of pictures simultaneously: dysphoric, threatening, positive, and neutral. Their eye-movements for each emotional stimulus were recorded for static and dynamic analysis. Data were analyzed using a mixed linear model and growth curve analysis. Specifically, the HTA group showed a greater tendency to avoid threatening stimuli and more pupil diameter variation in the early period of stimulus presentation (0-7.9 s). The HTA group also showed a stronger attentional bias toward positive and dysphoric stimuli in the middle and late period of stimulus presentation (7.9-30 s). These results suggest that trait anxiety has a significant temporal effect on attention to emotional stimuli, and that this effect mainly manifests after 7 s. In finding stronger attentional avoidance of threatening stimuli and more changes in neural activity, as well as a stronger attentional bias toward positive stimuli, this study provides novel insights on the relationship between trait anxiety and selective attention.