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This paper describes a case study of an online physics laboratory course implemented for 254 engineering students at Walailak University in Thailand in response to the COVID-19 pandemic. The laboratory sequence was designed based on the United Kingdom Professional Standards Framework and covered eight experiments on fundamental concepts of motion, electricity, waves, and fluid mechanics. The experiments were conducted at students' homes using physics education technology simulations, Tracker for video analysis, smartphones, and home-based apparatuses. During the experiments, students shared their progress with classmates and lecturers through screen sharing, which facilitated exchanges of ideas and difficulties. The paper discusses the successful implementation of the online laboratory course and the challenges encountered, such as student engagement, resource limitations, and practical skill development. The insights gained from this case study may be useful for online laboratory education beyond the pandemic. [ FROM AUTHOR] Copyright of Distance Education is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The COVID-19 pandemic and associated lockdowns have brought to researchers’ attention the need to adapt user research methods to remote settings. This paper builds on a video dataset of remote user research with older adults, implemented in the context of a longitudinal study impacted by the lockdown. In the analysis of the dataset as a case study, we found and categorised general barriers, strategies and specificities of communication of user research sessions conducted remotely. Our findings inform researchers planning and implementing similar methods, particularly in studies involving older user groups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Background Purpose Methods Findings Conclusion Female learning of movement in elite combat sports has not been studied enough to date. Literature on movement learning and teaching of complex skills has not, to date, focused on karate, and the scarce literature on the learning of elite karate practitioners mostly does not focus on women. Nevertheless, women fighters participated in karate as an Olympic sport, even if such status was temporary, limited to Tokyo 2020 (2021).In an analogy with Iris Marion Young's (1980) publication ‘throwing like a girl’, our aim in this study was to investigate what it means to learn ‘to fight like a girl’ and if there is a feminine learned fighting style.We have carried out an ethnographic project focusing, due to the unexpected impact of COVID-19, mainly on interviewing the female Spanish karate team in preparation for the Olympic Games. We interviewed 14 women athletes of the team and their four male coaches twice each and analysed 28 videos of women athletes displaying their best athletic performances, according to themselves. In this article we are focusing on the analysis of 20 videos of the kumite modality, then reflecting on this analysis with data from interviews of the four coaches and ten of the athletes, the kumite athletes. We developed a series of criteria in order to carry out the task of observation and analysis of the gendered martial-sportive movement supported by the literature, coaches’ perspectives and athletes’ views. The main concepts derived from the video analysis were the use of space, restricted movements;absence of melee work;and difficulty in carrying out projections/sweeps.Sports karate is still configured as a (hetero)normative environment, supported in tradition, be that of martial art with its pedagogy and of sport as a male preserve. This context leads to a view of women’s learning to perform as inferior to men’s, perpetually comparing them, in the hierarchical structure established within the field. Concepts of equality and equity are undermined, and by performing differently, or not matching the male model, women have their performance of some complex movements qualified as a natural inability.We conclude that there is a feminine way of learning to fight, but only with generalized characteristics since there is a rich plurality of styles among these elite sporting women. For the field, to fight like a girl means inferior performance in comparison with men’s performance, however, for us, it means really ‘to fight’, not just on the mat, and we see such comparison as untenable. [ FROM AUTHOR] Copyright of Physical Education & Sport Pedagogy is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Intimate contact recognition has gained more attention in academia field in recent years due to the outbreak of Covid-19. However, state of the art solutions suffer from either inefficient accuracy or high cost. In this paper, we propose a novel method for COVID-19 intimate contact recognition in public spaces through video camera networks (CCTV). This method leverages distance detection and re-Identification algorithms, so pedestrians in close contact are re-identified, their identity information is obtained and stored in a database to realize contact tracing. We compare different social distance detection algorithms and the Faster-RCNN model outperforms other al-ternatives in terms of running speed. We also evaluate our Re-Identification model on two types of indicators in the PETS2009 dataset: mAP reaches 85.1%;rank-1, rank-5, and rank-10 reach 97.8%, 98.9%, and 98.9%, respectively. Experimental results demonstrate that our solution can be effectively applied in public places to realize fast and accurate automatic contact tracing. © 2022 IEEE.
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AIM: To investigate the reliability and educational value of YouTube videos of minimally invasive complete mesocolic excision with right hemicolectomy procedures. MATERIALS AND METHODS: We searched YouTube with the terms "Laparoscopic and Robotic Complete Mesocolic Excision with Right Hemicolectomy" on January 12, 2021. To assess the reliability of the videos, we evaluated nine steps in each video and scored the videos based on the key steps they contained. The videos were divided into three groups according to the source of the upload. The total number of views, length, time since upload, and the number of likes, dislikes, and comments were recorded for each video. Narration, the use of descriptive subtitles, and the upload status by an expert surgeon were also examined. RESULTS: Sixty-eight videos were included in the study. A positive significant correlation was identified between the comprehensiveness score (CS) and the number of views (p=0.025). The CSs of the videos accessed from academic channels, as well as those accessed from journals, congress, and association channels, recorded higher CSs than those obtained from the personal channels of consultants (p=0.003). It was also found that CSs were higher in the videos of expert surgeons (p<0.001) and narrated videos (p<0.001). CONCLUSION: Not all YouTube videos on this subject have reliability and educational value. Surgical videos on YouTube may be evaluated by a video review commission formed by academic institutions, surgical associations, or expert surgeons, and videos suitable for education could be brought together and published via a free channel.
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Video analysis is carried out for one-dimensional motion and collision experiments using Tracker. Physical variables, i.e. displacement, time, and velocity, can be deducted from the analysis and allows one to connect them to the equation of motion and energy conservation. Corresponding equations of motions are given without neglecting the effect of friction and air resistance. Their solutions are used as models for data-fitting purposes. The fitting results allow one to find the surface friction coefficient and the air resistance parameter for the first experiment and friction for the second experiment. These experiments and analyses can be implemented for at least sophomore-level physics in a university. However, if simplified by removing all resistant parts, they can be used for high-school or first-year students in a university. The video files can be distributed to students for online courses that are prevalently ongoing worldwide due to the COVID pandemic. Students can observe the experiments and analyse data at home. With scheduled online consulting sessions, students can conduct the analysis with helps from instructors. [ FROM AUTHOR] Copyright of Physics Education is the property of IOP Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The world communities have suffered from the COVID-19 pandemic for the last two years. Even though many countries have started to normalise the situation, the COVID-19 still becomes a severe threat in the future. Healthy habits, such as complete and frequent handwashing, still need to be practised. These habits can minimise the transmission risks. The paper proposed a single-board computer system that aims to assess the handwashing steps. The standardised handwashing procedure is used to validate the acquired video of hand movement. The system is installed in a Raspberry Pi and receives video data from the connected mini camera. The deep learning model is implemented to provide classification capabilities. The assessment result is summarised according to the movement completeness and total duration. The testing stages found that the proposed system can provide accuracy and F1-score values of 82.55% and 86.66%, respectively. © 2021 ACM.
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Online exams are the most preferred mode of exams in online learning environment. This mode of exam has been even more prevalent and a necessity in the event of a forced closure of face-to-face teaching such as the recent Covid-19 pandemic. Naturally, conducting online exams poses much greater challenge to preserving academic integrity compared to conducting on-site face-to-face exams. As there is no human proctor for policing the examinee on site, the chances of cheating are high. Various online exam proctoring tools are being used by educational institutes worldwide, which offer different solutions to reduce the chances of cheating. The most common technique followed by these tools is recording of video and audio of the examinee during the whole duration of exam. These videos can be analyzed later by human examiner to detect possible cheating case. However, viewing hours of exam videos for each student can be impractical for a large class and thus detecting cheating would be next to impossible. Although some AI-based tools are being used by some proctoring software to raise flags, they are not always very useful. In this paper we propose a cheating detection technique that analyzes an exam video to extract four types of event data, which are then fed to a pre-trained classification model for detecting cheating activity. We formulate the cheating detection problem as a multivariate time-series classification problem by transforming each video into a multivariate time-series representing the time-varying event data extracted from each frame of the video. We have developed a real dataset of cheating videos and conduct extensive experiments with varying video lengths, different deep learning and traditional machine learning models and feature sets, achieving prediction accuracy as high as 97.7%.
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The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a system is devised that can track and monitor the crowd flow in Kaaba. The crowd in these videos is sparse due to the pandemic, and we have developed a technique to track the maximum crowd flow and detect any object (person) moving in the direction unlikely of the major flow. We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow. Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity to maintain a smooth crowd flow in Kaaba during the pandemic. © 2022 Tech Science Press. All rights reserved.
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The presence of B-line artefacts, the main artefact reflecting lung abnormalities in dengue patients, is often assessed using lung ultrasound (LUS) imaging. Inspired by human visual attention that enables us to process videos efficiently by paying attention to where and when it is required, we propose a spatiotemporal attention mechanism for B-line detection in LUS videos. The spatial attention allows the model to focus on the most task relevant parts of the image by learning a saliency map. The temporal attention generates an attention score for each attended frame to identify the most relevant frames from an input video. Our model not only identifies videos where B-lines show, but also localizes, within those videos, B-line related features both spatially and temporally, despite being trained in a weakly-supervised manner. We evaluate our approach on a LUS video dataset collected from severe dengue patients in a resource-limited hospital, assessing the B-line detection rate and the model’s ability to localize discriminative B-line regions spatially and B-line frames temporally. Experimental results demonstrate the efficacy of our approach for classifying B-line videos with an F1 score of up to 83.2% and localizing the most salient B-line regions both spatially and temporally with a correlation coefficient of 0.67 and an IoU of 69.7%, respectively.
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Educational experiments, demonstrations, and laboratory works are the basis for the study of the natural sciences. The drastic restriction of lessons in a real physics laboratory in 2020 - 2021 clearly demonstrated their negative effect on the quality of education. In terms of distance and blended learning, video analysis of real experiments, processes, and phenomena is a real competitor to the corresponding digital models. The free software Tracker toolkit allows measuring physical quantities on the basis of video material, processing the received data, and comparing them with mathematical models. Tracker usage expands methods of obtaining and practical application of information, shows how to identify the actions of laws, and, therefore, forms the scientific competency of students. The general rules for creating educational videos suitable for analysis using Tracker are given in the article. There are demonstrated different approaches to creating instructions for laboratory works based on video analysis on the examples of labs such as measuring the acceleration of free fall (mechanics), determining the surface tension of a liquid (molecular physics), the study of the rotational motion of the Sun (astronomy). Reference videos, didactic recommendations, a collection of video problems, and instructions on non-traditional methods of educational experiment adapted for video analysis using Tracker have been created. They are posted in the section “Instrumental digital didactics” of the resource www.stemua.science of the National Center “Junior Academy of Sciences of Ukraine”, which is popular among online users. Video analysis techniques are used for formal and non-formal education of pupils and students in the conditions of COVID-19;this has been verified during the training of educators and distance summer science school (2020). © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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BACKGROUND: Institutions, government departments, and healthcare professionals engage in social media because it facilitates reaching a large number of people simultaneously. YouTube provides a platform whereby anyone can upload videos and gain feedback on their content from other users. Many YouTube videos are related to health and science, and many people search YouTube for health-related information. YouTube has been acknowledged as a key public information source in recent crises caused by Zika, H1N1, swine flu, and most recently, COVID-19. METHODS: YouTube videos were collected from the YouTube Application Programming Interface (API) using the search terms COVID-19, coronavirus, COVID19, and corona. The search was conducted on April 4 and 5, 2020. The initial investigation found a total of 1084 videos. The second step involved identifying and verifying the videos for their relationship to COVID-19 information and excluding videos that did not relate to COVID-19 or were in a language other than English and Hindi. RESULTS: An analysis of YouTube videos covering COVID-19, uploaded in early 2020, in English and Hindi. The sample comprised 349 videos (n = 334 English). Videos were characterized by contributor, duration, content, and reception (views/likes/dislikes/comments). The majority contained general information, with only 4.01% focusing on symptoms and 11.17% on treatment and outcomes. Further, the majority (n = 229) were short videos of under 10 min duration. Videos provided by government and health care professionals comprised 6.87% and 5.74% % of the sample, respectively. News channels uploaded 71.63% of videos. CONCLUSIONS: YouTube may provide a significant resource for disseminating of information on public health issues like outbreaks of viral infections and should be utilized by healthcare agencies for this purpose. However, there is currently no way to determine whether a video has been produced or verified by authorized healthcare professionals. This limitation needs to be addressed so that the vital distribution services offered by platforms like YouTube can be fully utilized for increasing public understanding of healthcare science, particularly during a crisis such as a pandemic.
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COVID-19 , Influenza A Virus, H1N1 Subtype , Social Media , Zika Virus Infection , Zika Virus , Humans , Information Dissemination , Pandemics , SARS-CoV-2 , Video RecordingABSTRACT
Interest in sport cyberpsychology has become more popular over the last decade, primarily due to the increased use of technology and the online world, including social media, within sport settings (Hurley, 2018). In 2020, this became even more apparent for many athletes, their support teams and their sport organisations, when their professional and social worlds became cyber-dominated due to the COVID-19 pandemic. Many challenges were encountered by: (i) the athletes, in their efforts to remain active and well during this time when all competitions were cancelled and (ii) the healthcare professionals, working with these athletes, in their efforts to continue to support the athletes, when severe travel restrictions and social distancing were in place for considerable periods of time. The purpose of this paper, using a narrative review process, is to present and scrutinise an array of Internet interventions, remote psychological supports and applications (apps) that the athletes and their support personnel used to help them meet their physical, social, and emotional needs during the pandemic. The beneficial and restrictive features of these online options will be presented. Two main themes will be considered in order to focus this discussion, namely, (i) the potential physical and mental opportunities and challenges using the online world extensively during this time presented for the individuals working in sport and (ii) suggestions for how such online interventions used by the athletes, their coaches and sport science personnel during the pandemic may be maintained in some positive ways into the future, to help the athletes prepare for their upcoming competitions, their training and their future careers when they retire from elite sport.