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1 |
IoT architecture for continuous long term monitoring: Parkinson’s Disease case study
معماری اینترنت اشیا برای نظارت طولانی مدت مداوم: مطالعه موردی بیماری پارکینسون-2022 In recent years, technological advancements and the strengthening of the Internet of Things
concepts have led to significant improvements in the technology infrastructures for remote
monitoring. This includes telemedicine which is the ensemble of technologies and tools involved
in medical services, from consultations, to diagnosis, prescriptions, treatment and patient
monitoring, all done remotely via an Internet connection.
Developing a telemedicine framework capable of monitoring patients over a continuous long-term monitoring window may encounter various issues related to the battery life of the device or the accuracy of the retrieved data. Moreover, it is crucial to develop an IoT architecture that is adaptable to various scenarios and the ongoing changes of the application scenario under analysis. In this work, we present an IoT architecture for continuous long-term monitoring of patients. Furthermore, as a real scenario case study, we adapt our IoT architecture for Parkinson’s Disease management, building up the PDRMA (Parkinson’s disease remote monitoring architecture). Performance analysis for optimal operation with respect to temperature and daily battery life is conducted. Finally, a multi-parameter app for the continuous monitoring of Parkinson’s patients is presented. keywords: IoT | Telemedicine | Continuous long term monitoring | Parkinson’s disease | e-Health |
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 |
A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022 Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is
one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm
the presence of this virus, some radiological investigations find some important features from the
CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This
article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful
to automatically process the CT scan images without any manual annotation and helpful in the easy
interpretation. The proposed approach is based on artificial cell swarm optimization and will be
known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented
in the Matlab environment. The proposed approach uses a novel superpixel computation method
which is helpful to effectively represent the pixel intensity information which is beneficial for the
optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm
optimization approach. So, a twofold contribution can be observed in this work which is helpful
to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be
isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical
impact of this work. Both qualitative and quantitative experimental results show the effectiveness of
the proposed approach and also establish it as an effective computer-aided tool to fight against the
COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β
index are used to quantify the segmented results and it is observed that the proposed approach not
only performs well but also outperforms some of the standard approaches. On average, the proposed
approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and
9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art
approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a
contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization |
مقاله انگلیسی |
5 |
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 |
6 |
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 |
7 |
An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches
یک معماری تعاملی مبتنی بر اینترنت اشیا برای نظارت بیسیم بیماران با پچ های حسگر-2022 The alliance between the Internet of Things (IoT) and healthcare has the potential to improve
healthcare assistance at different stages of care through distributed vital sign sensing, paving
the way for domiciliary hospitalization. In this work, we propose an innovative design for
an IoT-based interoperable healthcare system to wirelessly monitor and classify patient status.
To support our research, we identify gaps, and discuss standards, protocols and technologies
based on works that use relevant IoT applications in healthcare. The proposed architecture is
centered on several low-energy unobtrusive sensors attached to the patients’ bodies, as well as
their beds, which encompass data acquisition nodes linked to a smart gateway that aggregates
data. The smart gateway is integrated with an existing hospital information system through the
exchange of Electronic Health Records (EHR), making relevant patient data easily available to
health professionals on systems which are familiar to them. A use case scenario is presented in
order to fulfill functional and non-functional requirements and provide a better understanding
of connection and communication between the distinct entities of the proposed architecture,
which is based on Bluetooth Low Energy (BLE) technology at the data acquisition level, the
Message Queuing Telemetry Transport (MQTT) protocol at the internal level, and on the Fast
Healthcare Interoperability Resources (FHIR) standard at the higher level.
keywords: Internet of Things | Digital healthcare | Wireless patient biomonitoring | System architecture | Interoperability |
مقاله انگلیسی |
8 |
HealthCloud: A system for monitoring health status of heart patients using machine learning and cloud computing
HealthCloud: سیستمی برای نظارت بر وضعیت سلامت بیماران قلبی با استفاده از یادگیری ماشین و محاسبات ابری-2022 In the context of the global health crisis of 2020, the tendency of many people to self-diagnose at
home virtually, prior to any physical interaction with medical professionals, has been increased.
Existing self-diagnosis systems include those accessible via the Internet, which involve entering
one’s symptoms. Several other methods do exist, for example, people read medical blogs or
notes, which are often wrongly interpreted by them and they arrive at a completely different
assumption regarding the cause of their symptoms. In this paper, a system called HealthCloud
is proposed, for monitoring health status of heart patients using machine learning and cloud
computing. This study aims to offer the ‘best of both worlds’, by combining the information
required for the person to understand the disease in sufficient detail, with an accurate prediction
as to whether they may have (in this case) heart disease or not. The presence of heart disease
is predicted using machine learning algorithms such as Support Vector Machine, K-Nearest
Neighbours, Neural Networks, Logistic Regression and Gradient Boosting Trees. This paper
evaluates these machine learning algorithms to obtain the most accurate model, in compliance
with Quality of Service (QoS) parameters. The performance of these machine learning models
is measured and compared using the metrics such as Accuracy, Sensitivity (Recall), Specificity,
AUC scores, Execution Time, Latency, and Memory Usage. For better establishment of the
results, these machine learning algorithms have been cross validated with 5-fold cross validation
technique. With an accuracy rate of 85.96%, it has been found that Logistic Regression is the
most responsive and accurate model amongst those models assessed. The Precision, Recall,
Cross Validation mean and AUC Score for this model were 95.83%, 76.67%, 81.68% and 96%
respectively. The algorithm and the mobile application were tested on Google Cloud Firebase
with existing user inputs from the dataset, as well as with unseen new data. The use of this
system can assist patients, both in reaching self-diagnosis decisions and in monitoring their
health.
keywords: Machine learning | Smart healthcare | Heart disease prediction | Cloud computing |
مقاله انگلیسی |
9 |
Implementation of a teleimmersion homecoming support system for supporting inpatients
پیاده سازی سیستم پشتیبانی از راه دور بازگشت به خانه برای حمایت از بیماران بستری-2022 This research has developed a teleimmersion homecoming support system for inpatients and their
families by combining a head-mounted display, a 360-degree camera, and a remote control robot.
This system realizes real-time remote communication between inpatients and their families and
provides inpatients with a “homecoming” feeling. Furthermore, inpatients can experience a
realistic home space by operating the remote control robot with a 360-degree camera installed at
home and enjoying family conversations with the voice call function. To evaluate this system, we
conducted a questionnaire survey on the operability, necessity, functionality, and presence of this
system. More than 80% of the subjects answered positively in the evaluation of the necessity,
functionality, and presence of this system. However, in the operability evaluation, 30% of the
subjects answered negatively, so it is necessary to improve the operability in the future. keywords: غوطه وری از راه دور | ربات کنترل از راه دور | سیستم پشتیبانی بازگشت به خانه | نمایشگر روی سر | واقعیت مجازی | Teleimmersion | Remote control robot | Homecoming support system | Head mounted display | Virtual reality |
مقاله انگلیسی |
10 |
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 |