Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices.

The use of voice recordings in both research and industry practice has increased dramatically in recent years-from diagnosing a COVID-19 infection based on patients' self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants' natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker's true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals.

Self-Care for Rheumatoid Arthritis during the Covid-19 Pandemic: Transferability of Canadian and Uk Experiences

Background/Aims The COVID-19 pandemic posed unique challenges for people worldwide in self-caring for their rheumatoid arthritis (RA). COVID-19 also prompted global changes in public health (e.g., vaccination programs and mask wearing) and rheumatology services (e.g., integrating telehealth with in-person healthcare). To facilitate cross-country learning of how to support people with RA to self-care during and post-pandemic, better understanding of individuals' experiences of self-care in the context of changes in public health and 'integrated' healthcare is needed. Our study aimed to explore transferability in experiences of public health measures and telehealth during COVID- 19 among individuals with RA in Canada and the UK. Methods Between July and October 2022, online focus groups (90 mins) took place with participants living with RA in the UK. Participants were recruited via social media and professional networks (including Versus Arthritis). Each participant received a report >=7days before each focus group, with a request to review in advance. The report contained preliminary findings identified through reflexive thematic analysis of interviews (30-70 mins) with thirty-nine participants with RA in British Columbia, Canada (26-86 years;36 females) between December 2020 and 2021. Nine preliminary themes were identified across three topics: accessing telehealth and in-person healthcare;decision-making around COVID-19 vaccinations and public health measures;and renegotiating 'the self'. The themes guided the focus groups, wherein UK participants shared their perspectives on each theme arising in the Canadian context. Audio-visual recordings were transcribed verbatim, and transcripts were de-identified. Ongoing directed content analysis of focus group data involves a collaborative approach with patient partners. Results Four focus groups involving thirteen participants (44-81 years;11 females) living across the UK were conducted. Participants had lived with RA for between 3-36 years. Canadian experiences typically resonated with UK participants, with some feeling a sense of unity and sadness that challenges were also experienced by others living with RA beyond their national context. Many UK participants supported preferences expressed by Canadian participants for a 'hybrid' healthcare approach to maximise benefits and minimise downsides of telehealth, and in-person consultations post-pandemic. Benefits (e.g., avoiding risk of COVID-19 transmission with telehealth) and disadvantages (e.g., lacking sensitivity/accuracy of in-person assessments) described by Canadian participants also resonated with UKbased participants. Many described how their decision-making on adopting public health measures to maintain their self-care was supported and/or undermined in their local context/community. Conclusion Our findings offer novel insights into the challenges and opportunities experienced by people with RA in their decision-making around public health measures and telehealth during a global pandemic. They also demonstrate some transferability of experiences between the UK and Canada. Insights may serve to inform decision-making for policy and programmes to support self-care (e.g., by integrating telehealth into routine rheumatology practice) across countries during and postpandemic.

DenseNet201 Model for Robust Detection on Incorrect Use of Mask

In 2022, the COVID-19 pandemic is still occurring. One of the optimal prevention efforts is to wear a mask properly. Several previous studies have classified the use of masks incorrectly. However, the accuracy resulting from the classification process is not optimal. This research aims to use the transfer learning method to achieve optimal accuracy. In this research, we used three classes, namely without a mask, incorrect mask, and with a mask. The use of these three classes is expected to be more detailed in detecting violations of the use of masks on the face. The classification method used in this research uses transfer learning as feature extraction and Global Average Pooling and Dense layers as classification layers. The transfer learning models used in this research are MobileNetV2, InceptionV3, and DenseNet201. We evaluate the three models' accuracy and processing time when using video data. The experimental results show that the DenseNet201 model achieves an accuracy of 93%, but the processing time per video frame is 0.291 s. In contrast to the MobileNetV2 model, which produces an accuracy of 89% and the processing speed of each video frame is 0.106 s. This result is inversely proportional to accuracy and speed. The DenseNet201 model produces high accuracy but slow processing time, while the MobileNetV2 model is less accurate but has faster processing. This research can be applied in the crowd center to monitor health protocols in the use of masks in the hope of inhibiting the transmission of the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Teacher Evaluation in Primary and Secondary Schools: A Systematic Review of SSCI Journal Publications from 2012 to 2022

This study revealed the current situation and developments in teacher evaluation in primary and secondary schools by reviewing 54 articles published in the recent decade (i.e., from January 2012 to October 2022). The coding scheme was developed based on the three components of effective teacher evaluation systems: "what”, "how”, and "who”. Specifically, we investigated the frameworks used for teacher evaluation, methods of evaluation, and participants in teacher evaluation. Based on our results, most studies evaluated teachers from the dimension of Instructional Support. Evaluation through video recording became popular due to technological advancement. Further, an increasing number of schools invited external experts to conduct teacher evaluations to ensure fairness. We also identified several crucial factors for teacher development: effective use of teaching resources and technology, high-quality feedback and communication, emotional support, classroom organization, and professional responsibilities. Due to COVID-19, many schools adopted distance learning, prompting the need to develop technological skills for teachers. Through the in-depth analysis of the current situation and development trends in the various dimensions of teacher evaluation in primary and secondary education, future research directions and issues were discussed and explored in this review.

Real Time Implementation of Robust Sound based Respiratory Disease Classification using Spectrogram and Deep Convolutional Neural Networks

Respiratory diseases become burden to affect health of the people and five lung related diseases namely COPD, Asthma, Tuberculosis, Lower respiratory tract infection and Lung cancer are leading causes of death worldwide. X-ray or CT scan images of lungs of patients are analysed for prediction of any lung related respiratory diseases clinically. Respiratory sounds also can be analysed to diagnose the respiratory illness prevailing among humans. Sound based respiratory disease classification against healthy subjects is done by extracting spectrogram from the respiratory sound signal and Convolutional neural network (CNN) templates are created by applying the extracted features on the layered CNN architecture. Test sound is classified to be associated with respiratory disease or healthy subjects by applying the testing procedure on the test feature frames of spectrogram. Evaluation of the respiratory disease binary classification is performed by considering 80% and 20% of the extracted spectrogram features for training and testing. An automated system is developed to classify the respiratory diseases namely upper respiratory tract infection (URTI), pneumonia, bronchitis, bronchiectasis, and coronary obstructive pulmonary disease (COPD) against healthy subjects from breathing & wheezing sounds. Decision level fusion of spectrogram, Melspectrogram and Gammatone gram features with CNN for modelling & classification is done and the system has deliberated the accuracy of 98%. Combination of Gammatone gram and CNN has provided very good results for binary classification of pulmonary diseases against healthy subjects. This system is realized in real time by using Raspberry Pi hardware and this system provides the validation error of 14%. This automated system would be useful for COVID testing using breathing sounds if respiratory sound database with breathing sound recordings from COVID patients would be available.Copyright © 2023 North Atlantic University Union NAUN. All rights reserved.

Saturday Night Seder by Erich Bergen et al., and: Mishkan HaSeder, A Passover Haggadah ed. by Rabbi Hara E. Person and Jessica Greenbaum (review)

In the wake of 9/11, for instance, the Union for Reform Judaism rapidly posted a "Survivor Tree Planting Ceremony," a memorial service for religious schools, an interfaith dialogue guide, and readings and prayers for congregations and individuals. [...]for many Jewish Americans, the virtual Passover of 2020 was the gateway experience for so many other online forms of Judaism that followed: live-streamed prayer services attended by thousands across time zones, Zoom gatherings for weddings, brisses, baby namings, funerals, and shivas. [...]first I copied down the last stanza of "Passover Love Poem" (147), a poem by Rabbi Person, knowing it was just right to contribute to my second (and hopefully last) Zoom Seder: "This is more than a recipe for nostalgia.

Telemedicine technologies in improving the effectiveness of followup monitoring of patients with lower extremity peripheral artery disease during the COVID19 pandemic.

Aim. To evaluate the effectiveness of a novel approach to followup monitoring of patients with lower extremity peripheral artery disease (PAD) using telemedicine technologies. Material and methods. The study included 175 patients (mean age, 68, 1+/-7, 7 years). Two following groups of patients were formed: the main group (n=86), which used an optimized monitoring program using telemedicine techniques, and the control group (n=89), which assumed traditional monitoring by a cardiologist and a vascular surgeon. The mean followup period was 11, 77+/-1, 5 months. The optimized monitoring program included the implementation of audio communication with patients by an employee with a secondary medical education with an assessment of the current health status according to original unified questionnaire, with the definition of personalized management tactics. At the primary and final stages, the patient underwent an assessment of clinical and anamnestic data, mental and cognitive status, and compliance. Results. At the final stage, uncompensated hypertension was revealed in 36, 0% and 49, 4% (p=0, 0001), smoking - in 30, 6% and 42, 9% (p=0, 05) in the main and control group, respectively. In the main group, a greater painfree walking distance was revealed - 625, 8+/-395, 3 m (control group - 443+/-417 m (p=0, 013)). The average systolic blood pressure was 125, 2+/-10, 2 mm Hg and 138, 8+/-15, 8 mm Hg (p=0, 0001) in the main and control group, respectively. In the control group, a greater number of patients with a high level of personal and situational anxiety were revealed (p=0, 05). In the main group, a higher level of adherence to therapy was established at the final study stage (p=0, 001). Conclusion. The optimized monitoring program for patients with limited mobility is effective and can be implemented in practical healthcare for patients with lower extremity PAD.Copyright © 2023 Vserossiiskoe Obshchestvo Kardiologov. All rights reserved.

Bringing together podcasting, social work field education and learning about practice with Aboriginal peoples and communities

Social work content podcasting has increased exponentially in recent years, playing a new role in the emerging social work education debate surrounding online and remote delivery of social work content. Although podcasting itself is not now a new digital innovation, how and why social work educators and academics would embrace the use of podcasting is still debated and is often positioned as inferior to face-to-face classroom teaching. In the Australian context this is particularly important when non-Aboriginal students are engaging with Aboriginal understandings of place and ways of relating to Country, a challenging reflexive exercise without the added complexity that remote educational delivery can provide. The brief history of podcasting and its relationship to social work education provides a context for re-imagining the pedagogy of critical thinking, with a case example provided of a remote field placement with The Social Work Stories Podcast during the Covid-19 pandemic in 2020.

Questioning Potentials of the Electrorganic aFrame in Music Therapy: Two Client Case Studies with a Single Music Therapist

A multi-phase investigation was conducted to question potentials within music therapy of a new electrorganic frame drum musical instrument from Japan titled the ‘aFrame'. Two professional music therapists collaborated in this third phase of testing under the work in progress. One of the two music therapists tested the aFrame within numerous sessions with two profoundly disabled clients across generations i.e., an adolescent male and an adult woman. Observations including video recordings as baseline analysis. A goal of the study was to identify strengths and weaknesses of the new instrument in the field of (re)habilitation, especially across spectrums of those with profound dysfunction, special needs situations, and across ages. A goal of the overall work of some four decades, titled SoundScapes, is to achieve an ultimate compendium of tools for human performance to create specific interactive environments to support therapists, caregivers, and for own self-training through engaged and motivated creativity, self-expression, and play. Such environments as created by the first author have been used in his stage performances and installations (e.g., at Museums of Modern Art). The tools are thus considered transdisciplinarity forming a new holistic approach aligned to his six patents. Results from the investigation question the contextual potential of the aFrame due to a typical lack of motoric control aligned to the fragility and expense of the instrument – challenges were evident for those with diminished or lack of physical limb control. To optimize use, add-on footswitches and pedals are recommended with the aFrame instrument. These give added options including remote switching and an audio streaming interface mixer for optimal Online streaming of instrument (and voice) that would have been especially useful during the Coronavirus pandemic so that the music therapists could have continued their interactions with clients remotely (i.e., beyond video conferencing quality). Alternatives to the aFrame are posit and selected from the new generation of instruments and pedals controlling digital media as presented at the end of the text. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Experience of Pharmacy students participating in an Objective Structured Clinical Exam: a survey

In the Pharmacy master program at Utrecht University students are trained for patient centred pharmaceutical consultations. After the training, students participate in a formative Objective Structured Clinical Exam (OSCE) to experience what an OSCE is like and to gauge their ability. Students receive written feedback and a videorecording of their consultations. Students who perform well on a station get an exemption for that station in the summative OSCE. In the fall of 2020, the organisation of the OSCE was altered because of COVID-19. A survey was held to find out if these changes should be kept in the future. Immediately after the OSCE students were asked to fill a semi-structured survey. The survey contained multiple choice questions with room for elaboration about the organisation, their performance, and their experience of the OSCE. Students were asked to characterize the OSCE with four words. From 26 October – 2 November 2020 82 students participated in the formative OSCE;69 (84%) filled the survey. Students agreed that the OSCE was well organised (77%) and the assessors were friendly and put them at ease (87%). However, 80% of the students felt stressed. To characterize the OSCE students used 95 different words (total 196 words) which related to experiencing stress (22%), being an instructive experience (16%), difficult (12%), a pleasant atmosphere (8%) and general positive remarks (19%). The survey offered in-depth insight in students experience of the OSCE. Assessors experience that OSCEs are stressful and difficult for students. It is encouraging that students also mention positive aspects such as being educational and a pleasant atmosphere. The experience of the students and assessors have led to some changes in the organisation of the OSCE (more time per station, students prepare per station). In the future the survey could be repeated for comparison. [ FROM AUTHOR] Copyright of Patient Education & Counseling is the property of Elsevier B.V. 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.)

Convolutional Neural Network Model based Students' Engagement Detection in Imbalanced DAiSEE Dataset

The COVID-19 pandemic has significantly changed learning processes. Learning, which had generally been carried out face-to-face, has now turned online. This learning strategy has both advantages and challenges. On the bright side, online learning is unbound by space and time, allowing it to take place anywhere and anytime. On the other side, it faces a common challenge in the lack of direct interaction between educators and students, making it difficult to assess students' engagement during an online learning process. Therefore, it is necessary to conduct research with the aim of automatically detecting students' engagement during online learning. The data used in this research were derived from the DAiSEE dataset (Dataset for Affective States in E-Environments), which comprises ten-second video recordings of students. This dataset classifies engagement levels into four categories: low, very low, high, and very high. However, the issue of imbalanced data found in the DAiSEE dataset has yet to be addressed in previous research. This data imbalance can cause errors in the classification model, resulting in overfitting and underfitting of the model. In this study, Convolutional Neural Network, a deep learning model, was utilized for feature extraction on the DAiSEE dataset. The OpenFace library was used to perform facial landmark detection, head pose estimation, facial expression unit recognition, and eye gaze estimation. The pre-processing stages included data selection, dimensional reduction, and normalization. The PCA and SVD techniques were used for dimensional reduction. The data were later oversampled using the SMOTE algorithm. The training and testing data were distributed at an 80:20 ratio. The results obtained from this experiment exceeded the benchmark evaluation values on the DAiSEE dataset, achieving the best accuracy of 77.97% using the SVD dimensional reduction technique. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Research on two-dimensional code scanning and automatic recording device for human body temperature measurement based on stm32

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places. © 2022 IEEE.

Video Compression using Deep Neural Networks

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards. © 2022 IEEE.

SMALL: Open Surgery versus Minimally invasive vacuum-Assisted excision for smaLL screendetected breast cancer - a UK phase III randomised multicentre trial

Background: Mammographic screening programmes reduce breast cancer mortality, but detect many small tumours with favourable biological features which may not progress during a woman's lifetime. Screen-detected cancers are treated with standard surgery and adjuvant therapies, with associated morbidities. There is a need to reduce overtreatment of good prognosis tumours and numerous studies have evaluated the omission of radiotherapy in this context. However, there is little evidence to support surgical de-escalation, although percutaneous minimally invasive treatment approaches have been described. Vacuum-assisted excision (VAE) is in widespread use for management of benign lesions and lesions of uncertain malignant potential. SMALL (ISRCTN 12240119) is designed to determine the feasibility of using this approach for treatment of small invasive tumours detected within the UK NHS Breast Screening Programme (BSP). Method(s): SMALL is a phase III multicentre randomised trial comparing standard surgery with VAE for screendetected good prognosis cancers. The main eligibility criteria are age >=47 years, unifocal grade 1 tumours with maximum diameter 15mm, which are strongly ER/PR+ve and HER2-ve, with negative clinical/radiological axillary staging. Patients are randomised 2:1 in favour of VAE or surgery;with no axillary surgery in the VAE arm. Completeness of excision is assessed radiologically, and if excision is incomplete, patients undergo open surgery. Adjuvant radiotherapy and endocrine therapy are mandated in the VAE arm but may be omitted following surgery. Co-primary end-points are: 1. Noninferiority comparison of the requirement for a second procedure following excision 2. Single arm analysis of local recurrence (LR) at 5 years following VAE Recruitment of 800 patients will permit demonstration of 10% non-inferiority of VAE for requirement of a second procedure. This ensures sufficient patients for single arm analysis of LR rates, where expected LR free survival is 99% at 5 years, with an undesirable survival probability after VAE of 97%. To ensure that the trial as a whole only has 5% alpha, the significance level for each co-primary outcome is set at 2.5% with 90% power. The Data Monitoring Committee will monitor LR events to ensure these do not exceed 3% per year. Secondary outcome measures include time to ipsilateral recurrence, overall survival, complications, quality of life and health economic analysis. A novel feature of SMALL is the integration of a QuinteT Recruitment Intervention (QRI), which aims to optimise recruitment to the study. Recruitment challenges are identified by analysing recruiter/patient interviews and audiorecordings of trial discussions, and by review of trial screening logs, eligibility and recruitment data and study documentation. Solutions to address these are developed collaboratively, including individual/group recruiter feedback and recruitment tips documents. Result(s): SMALL opened in December 2019, but recruitment halted in 2020 for 5 months due to COVID-19. At 7st July 2022, 142 patients had been randomised from 26 centres, with a randomisation rate of approximately 45%, and a per site recruitment rate of 0.4-0.5 patients/month, approaching the feasibility recruitment target of 144 patients. Drawing from preliminary QRI findings and insights from patient representatives, a recruitment tips document has been circulated (on providing balanced information about treatments, encouraging recruiters to engage with patient preferences, and explaining randomisation). Individual recruiter feedback has commenced, with wider feedback delivered across sites via recruitment training workshops. Conclusion(s): Despite pandemic-related challenges, SMALL has an excellent recruitment rate to date and is expected to have a global impact on treatment of breast cancer within mammographic screening programmes.

'Masks and Social Distancing -You Still Want to Add That Human Element': Impact of Covid-19 Changes on Children and Families

Background Despite lower rates of illness, morbidity and mortality associated with SARS-CoV-2 infection in children during the pandemic, their health and wellbeing has been significantly impacted. Emerging evidence indicates that this includes experiences of hospital-based care for them and their families. As part of a series of multi-site research studies to undertake a rapid appraisal of healthcare workers' perceptions of working during the pandemic, our study focussed on clinical and non-clinical staff perceptions of the impact of COVID-19 on aspects of care delivery, preparedness and staffing which were specific to a specialist children's hospital. Methods This was a qualitative study. Hospital staff were invited to take part in a single telephone interview. Researchers used a qualitative rapid appraisal design. This included a semi-structured interview guide, RREAL Rapid Assessment Procedure (RAP) sheet to share data, audio recording and transcription of interviews, with a framework approach to analysis. Results Thirty-six staff participated representing a wide range of roles within the hospital: 19 (53%) nurses, seven (19%) medical staff, 10 (28%) other staff groups (including radiographers, managers, play staff, schoolteachers, domestic and portering staff, social workers). Three themes related to the impact on children and families were identified: Same Hospital but Different for Everyone, Families Paid the Price and The Digital World. Conclusion Providing care and treatment for children and families changed profoundly during the pandemic, particularly during lockdowns periods. Adaptations to deliver clinical care, play, schooling, and other therapies online were rapidly put into action, however benefits were not universal or always inclusive. The disruption to a central principle of children's hospital care-the presence and involvement of families-was of critical concern to staff. We present perceptions of staff on how changes to the organisation of care delivery within Great Ormond Street Hospital impacted upon children and families.

Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs

The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic. The demand for higher resolutions and quality, the need for immersive video formats, and the newest, more complex video codecs increase the energy consumption in data centers and display devices. In this paper, we explore and compare the energy consumption across optimized state-of-the-art video codecs, SVT-AV1, VVenC/VVdeC, VP9, and x.265. Furthermore, we align the energy usage with various objective quality metrics and the compression performance for a set of video sequences across different resolutions. The results indicate that from the tested codecs and configurations, SVTAV1 provides the best tradeoff between energy consumption and quality. The reported results aim to serve as a guide towards sustainable video streaming while not compromising the quality of experience of the end user. © 2022 IEEE.

'I Put That under My Work Blanket' - Children's Hospital Staff Experiences of Redeployment during the Covid-19 Pandemic

Background In March 2020 and January 2021 Great Ormond Street Hospital (GOSH) staff were redeployed to hospitals in North Central London, to support the care of adult Covid positive in-patients and paediatric services. In addition to providing care for children usually referred to GOSH, the hospital prepared for children who required hospital care who would usually have been admitted to other Paediatric Intensive Care Units across London - units repurposed to provide adult intensive care;and children who would normally receive their care in local hospital paediatric services, many of which were closed as staff were treating adults. Clinical skills training was offered to up-skill non-ward-based staff and provide an update on current techniques utilised in the care of general paediatric patients. Methods Within a wider study to understand healthcare workers' perceptions of care delivery in the context of the COVID-19 pandemic, GOSH staff were invited to take part in a single semi-structured interview by telephone. In our sampling strategy, we purposively recruited staff with experience of redeployment. We employed qualitative rapid appraisal design, RREAL Rapid Assessment Procedures (RAP) for early sharing, interpretation and analysis of data, audio recording and transcription of interviews and framework analysis. Results Recruitment and interviews took place between March and November 2021. Thirty-six GOSH staff were recruited, 18 (50%) participants had been redeployed outside the hospital and 4 (11%) within the hospital. We identified six themes which illustrated staff experiences of redeployment. These included (i) drivers and agency;(ii) preparation for redeployment;(iii) working reality;(iv) impact on family life;(v) professional disruption and (vi) personal challenges. Conclusion Redeployment was reported as both challenging and rewarding. More timely confirmation and bespoke training recognising individual skill sets was recommended. Support structures were available with the majority preferring those developed with close colleagues.

Social Distance Alerting System Using Machine Learning

The increase into the Corona virus pandemic led to a higher death rate globally. The best way to prevent getting sick is to keep yourself physically or socially far. Our project provides an approach for physical isolation revealing using machine knowledge toward indicate the necessary space to be maintained to decrease the collision of the corona virus contagious widespread spread. By analyzing a videotape provide for from the camera, the detect apparatus be fashioned in the direction of notify individuals toward maintain a out of harm's way aloofness on or after one an additional. The open-source person recognition pretrained model, YOLO3 algorithm, was utilized to recognize people using the video frame from the camera as input. YOLO3 has the benefit of mortal a lot quicker than further algorithms, at a halt maintain exactness and meets the real-time requirements for person detection. In order to calculate distance from the 2D plane, the video frames are afterwards transformed into top-down views. Estimated distance between individuals and any non-compliant pair of individuals within the display is indicate by means of a red colour edge and stripe, the moderate distance is represented with orange colour and the safe distance is represented by green colour frame. The suggested technique was examined lying on a pre record videotape as well as on the live video feed of persons walking on the road. Additionally an alarm sound is provided to notify the persons. The outcome show that the planned strategy is ready toward sees the societal separation trial among many populaces withinthe videotape. © 2022 IEEE.

Swimming × Online Coaching: Online Conditioning of Competitive Swimmers - Focusing on the Thorax - via Smartphones and Wearable Sensors

In response to the decline in motor function (centered on the thorax) caused by chronic muscle tension associated with strengthening exercises for competitive swimmers, we devised a training program that promotes awareness of the functional coordination of the thorax;spine, ribs, and core muscles, and restores natural and efficient body movement. This article presents the results of supporting athlete training during the novel coronavirus pandemic by providing regular coaching remotely using a web-conference system with smartphones, video recording, and a multi-sensor belt equipped with hitoe™ for measuring myoelectricity, respiration, and motion. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.