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1 |
Internet of Things-enabled Passive Contact Tracing in Smart Cities
ردیابی تماس غیرفعال با قابلیت اینترنت اشیا در شهرهای هوشمند-2022 Contact tracing has been proven an essential practice during pandemic outbreaks and is a
critical non-pharmaceutical intervention to reduce mortality rates. While traditional con-
tact tracing approaches are gradually being replaced by peer-to-peer smartphone-based
systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is
steadily growing in smart city environments. This work presents a contact tracing frame-
work that logs smart space users’ co-existence using IoT devices as reference anchors. The
design is non-intrusive as it relies on passive wireless interactions between each user’s
carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by uti-
lizing received signal strength indicators (RSSI). The proposed framework can log the iden-
tities for the interacting pair, their estimated distance, and the overlapping time duration.
Also, we propose a machine learning-based infection risk classification method to char-
acterize each interaction that relies on RSSI-based attributes and contact details. Finally,
the proposed contact tracing framework’s performance is evaluated through a real-world
case study of actual wireless interactions between users and IoT devices through Bluetooth
Low Energy advertising. The results demonstrate the system’s capability to accurately cap-
ture contact between mobile users and assess their infection risk provided adequate model
training over time.
© 2021 Elsevier B.V. All rights reserved. keywords: بلوتوث کم انرژی | ردیابی تماس | اینترنت اشیا | طبقه بندی خطر عفونت | Bluetooth Low Energy | Contact Tracing | Internet of Things | Infection Risk Classification |
مقاله انگلیسی |
2 |
Moving towards intelligent telemedicine: Computer vision measurement of human movement
حرکت به سمت پزشکی از راه دور هوشمند: اندازه گیری بینایی کامپیوتری حرکت انسان-2022 Background: Telemedicine video consultations are rapidly increasing globally, accelerated by the COVID-
19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual
judgement because video cameras are so ubiquitous in personal devices and new techniques, such as
DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy
of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has
never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations.
Objectives: To determine the validity and reliability of DLC applied to laptop videos to measure finger tapping,
a validated test of human movement.
Method: Sixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand
movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak,
a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101,
ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the
laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking.
Results: Over 96% (529/552) of DLC measures were within +∕−0.5 Hz of the Optotrak measures. At tapping
frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with
the laptop camera’s low FPS. Computer vision methods hold potential for moving us towards intelligent
telemedicine by providing human movement analysis during consultations. However, further developments
are required to accurately measure the fastest movements.
keywords: پزشکی از راه دور | ضربه زدن با انگشت | موتور کنترل | کامپیوتری | Telemedicine | DeepLabCut | Finger tapping | Motor control | Computer vision |
مقاله انگلیسی |
3 |
Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision
Cov-Net: یک روش تشخیصی به کمک رایانه برای تشخیص COVID-19 از تصاویر اشعه ایکس قفسه سینه از طریق بینایی ماشین-2022 In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human
beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this
paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19
from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust
feature learning ability. In particular, a modified residual network with asymmetric convolution and attention
mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated
convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic
and low-level detailed information. Experimental results on two public COVID-19 radiography databases have
demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966
and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms
other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of
Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed
that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios.
Consequently, one can conclude that this work has both practical value in providing reliable reference to the
radiologist and theoretical significance in developing methods to build robust features with strong presentation
ability.
keywords: COVID-19 | Computer aided diagnosis (CAD) | Feature learning | Image recognition | Machine vision |
مقاله انگلیسی |
4 |
آموزش آسیب شناسی از راه دور تحت همه گیری COVID-19: برداشت های دانشجویان پزشکی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 12 زمینه: همهگیری COVID-19 آموزش سنتی را مجبور کرده است که دوباره ساختار یافته و به صورت آنلاین ارائه شود. هدف: تجزیه و تحلیل ادراک دانشجویان پزشکی در مورد مزایا و مشکلات آموزش از راه دور پاتولوژی در طول همه گیری COVID-19.
طراحی: یک مطالعه مقطعی با یک نظرسنجی آنلاین برای دانشجویان سال سوم و چهارم فارغالتحصیلی پزشکی، که در آموزش از راه دور پاتولوژی در طول همهگیری COVID-19 شرکت کردند، انجام شد. روشهای تدریس آنلاین شامل فعالیتهای همزمان با سخنرانیهای تعاملی زنده، بحثهای مبتنی بر مورد و فعالیتهای ناهمزمان با سخنرانیهای ضبطشده، آموزشها و متون موجود در پلت فرم آموزش آنلاین است. ادراک دانشجویان در مورد آموزش از راه دور آسیب شناسی از طریق نظرسنجی آنلاین مورد ارزیابی قرار گرفت. یافتهها: 90 دانشجو (47%) از 190 شرکتکننده پرسشنامه را تکمیل کردند که 45 نفر مرد و 52 نفر در سال سوم فارغالتحصیلی پزشکی بودند. شرایط درک شده ای که یادگیری آسیب شناسی را تسهیل می کرد شامل استفاده از پلت فرم آموزش آنلاین و انعطاف پذیری زمانی برای مطالعه بود. دانشجویان سخنرانی های زنده تعاملی را برتر از سخنرانی های سنتی سنتی می دانستند. شرایط درک شده ای که مانع اجرای آموزش آنلاین شد، شامل دشواری جداسازی مطالعه از فعالیت های خانگی، بی انگیزگی و بدتر شدن کیفیت زندگی به دلیل دوری فیزیکی از همکاران و اساتید بود. به طور کلی، آموزش از راه دور آسیب شناسی توسط 80٪ از دانشجویان ارزش مثبت داشت. نتیجهگیری: ابزارهای آنلاین اجازه میدهند تا محتوای پاتولوژی با موفقیت در طول همهگیری COVID-19 به دانشآموزان ارائه شود. این تجربه می تواند الگویی برای فعالیت های آموزشی آتی آسیب شناسی در آموزش علوم بهداشت باشد. کلید واژه ها: پاتولوژی | آموزش از راه دور | کووید -19 | آموزش پزشکی |
مقاله ترجمه شده |
5 |
Smart mask – Wearable IoT solution for improved protection and personal health
ماسک هوشمند – راه حل پوشیدنی اینترنت اشیا برای بهبود حفاظت و سلامت شخصی-2022 The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we
envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control
the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials,
AI, wireless, IoT, and software will help the gathering of health data and health-related event
detection in real time from the user as well as from their environment. In the larger scale, with the
help of AI-based analysis for health data it is possible to predict and decrease medical costs with
accurate diagnoses and treatment plans, where the comparison of personal data to large-scale
public data enables drawing up a personal health trajectory, for example. Key research prob-
lems for smart respiratory protective equipment are identified in addition to future research di-
rections. A Smart Mask prototype was developed with accompanying user application, backend
and heath AI to study the concept. keywords: کووید-۱۹ | محاسبات لبه | اینترنت اشیا | سلامت شخصی | پوشیدنی | COVID-19 | Edge computing | IoT | Personal health | Wearable |
مقاله انگلیسی |
6 |
5G network slice for digital real-time healthcare system powered by network data analytics
برش شبکه 5G برای سیستم دیجیتال مراقبت بهداشتی بلادرنگ طراحی شده توسط تجزیه و تحلیل داده های شبکه-2022 In the wake of the COVID-19 pandemic, where almost the entire global healthcare ecosystem struggled to handle
patients, it’s evident that the healthcare segment needs a virtual real-time digital support system. The recent
advancements in technology have enabled machine-to-machine communication, enhanced mobile broadband,
and real-time biometric data analytics. These could potentially fulfill the requirements of an end-to-end digital
healthcare system. For building such a system, there is also a need for a dedicated and specialized communication
network. Such a system will not only support dynamic throughput, latency and payload but also provide guaranteed QoS (Quality of Service) at every instant. The motive of our study was to define an implementable lowlevel architecture for the digital healthcare system by using the 5G Network Slice that incorporates all these
features. Best-in-class wearable devices will collect the biometric data and transmit it via the 5G network slice.
Data analytics is then applied to the collected data to build a knowledge graph used for quick predictions and
prescriptions. The architecture also keeps in mind the security and integrity aspects of healthcare data.
Keywords: 5G network slice | Slice dimensioning | Digital healthcare | Network data analytics framework | IoMT |
مقاله انگلیسی |
7 |
A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home
یک معماری مفهومی هشدار اولیه مبتنی بر اینترنت اشیا برای نظارت از راه دور بیماران COVID-19 در بخش ها و در خانه-2022 Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person
contacts, enabling early-detection of severe cases, and remotely assessing patients’ status.
Internet of Things (IoT) technologies have been used for monitoring patients’ health with
wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning
scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results
in detecting the health deterioration of COVID-19 patients. Although the literature presents
several approaches for remote monitoring, none of these studies proposes a customized,
complete, and integrated architecture that uses an effective early-detection mechanism for
COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore,
this article’s objective is to present a comprehensive IoT-based conceptual architecture that
addresses the key requirements of scalability, interoperability, network dynamics, context
discovery, reliability, and privacy in the context of remote health monitoring of COVID-19
patients in hospitals and at home. Since remote monitoring of patients at home (essential
during a pandemic) can engender trust issues regarding secure and ethical data collection,
a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a
configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.
keywords: نظارت از راه دور | کووید-۱۹ | اخبار-2 | معماری | رضایت | اینترنت اشیا | Remote monitoring | COVID-19 | NEWS-2 | Architecture | Consent | IoT |
مقاله انگلیسی |
8 |
Evaluation of six commercial SARS-CoV-2 rapid antigen tests in nasopharyngeal swabs: Better knowledge for better patient management?
ارزیابی شش تست آنتی ژن سریع SARS-COV-2 در سواب های نازوفارنکس: دانش بهتر برای مدیریت بهتر بیمار؟-2021 Robust antigen point-of-care SARS-CoV-2 tests have been proposed as an efficient tool to address the COVID-19
pandemic. This requirement was raised after acknowledging the constraints that are brought by molecular
biology. However, worldwide markets have been flooded with cheap and potentially underperforming lateral
flow assays. Herein we retrospectively compared the overall performance of five qualitative rapid antigen SARS-
CoV-2 assays and one quantitative automated test on 239 clinical swabs. While the overall sensitivity and
specificity are relatively similar for all tests, concordance with molecular based methods varies, ranging from
75,7% to 83,3% among evaluated tests. Sensitivity is greatly improved when considering patients with higher
viral excretion (Ct≤33), proving that antigen tests accurately distinguish infectious patients from viral shedding.
These results should be taken into consideration by clinicians involved in patient triage and management, as well
as by national authorities in public health strategies and for mass campaign approaches. keywords: SARS-DONE-2 | تست های آنتی ژن سریع | rt-pcr | کووید -19 | SARS-CoV-2 | Rapid antigen tests | RT-PCR | COVID-19 |
مقاله انگلیسی |
9 |
An ecological critique of accounting: The circular economy and COVID-19
نقد زیست محیطی حسابداری: اقتصاد دایره ای و Covid-19-2021 Given the increasing participation of accounting technologies in purported solutions to
deal with the ecological crisis, we address two areas where a growing accounting
literature is emerging, the circular economy and the COVID-19 pandemic, testing some
ideas to inform an ecological critique of accounting that could help us ward off the
‘‘dreams of escaping” (Latour, 2018). We suggest that the conceptual separation between
nature and society renders accounting for the circular economy and the COVID-19
pandemic problematic. A critical account of the circular economy might problematize
things like the whole economic system’s physical scale, spatial and temporal system
boundaries, consumer culture, and the inherent politics of the circular economy. We also
suggest that a critical account of the COVID-19 pandemic needs to take on board the
participation of accounting representations in the construction of particular narratives
about the virus. In particular, calculations of the costs caused by COVID-19 need to be
connected to the ecological value of viruses to illustrate how the social and the
biological worlds are inextricably connected. In both cases, we suggest critical
accounting researchers need to be actively involved in discussions about how valuation
constructs narratives about resource or waste, with significant implications on how we
conceive the relationship between humanity and the environment.
keywords: حسابداری | انسان شناسی | اقتصاد دایره ای | کووید -19 | بحران زیست محیطی | Accounting | Anthropocene | Circular economy | COVID-19 | Environmental crisis |
مقاله انگلیسی |
10 |
Digital Livestock Farming
دامداری دیجیتال-2021 As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented. Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture |
مقاله انگلیسی |