COVID-19 (Corona Virus Disease 2019) is a pandemic, which has been spreading exponentially around the globe. Many countries adopted stay-at-home or lockdown policies to control its spreading. However, prolonged stay-at-home may cause worse effects like economical crises, unemployment, food scarcity, and mental health problems of individuals. This article presents a smart consumer electronics solution to facilitate safe and gradual opening after stay-at-home restrictions are lifted. An Internet of Medical Things enabled wearable called EasyBand is introduced to limit the growth of new positive cases by autocontact tracing and by encouraging essential social distancing.

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EasyBand: A Wearable for Safety-Aware Mobility During Pandemic Outbreak

DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression

Background The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects. Purpose The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.

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The Recent Progress and Applications of Digital Technologies in Healthcare: A Review

Sensors for internet of medical things: State-of-the-art, security and privacy issues, challenges and future directions

People around the globe rely on their blood samples for their glucose level measurement. There is a demand for non-invasive, precise and cost-effective solutions to monitor blood glucose level and control of diabetes. Serum glucose is an accurate blood glucose measurement method in comparison to capillary glucose measurement. Presently, the serum glucose is measured through laboratory setup with an invasive approach. The invasive method is painful and is not suitable for continuous glucose measurement. In this paper, we propose a novel wearable non-invasive consumer device (called iGLU 2.0) which can be used by consumers for accurate continuous blood glucose monitoring. This device uses a novel short near infrared (NIR) spectroscopy developed by us. It is incorporated with Internet-of-Medical-Things (IoMT) for smart healthcare where the healthcare data is stored on the cloud and is accessible to the users and caregivers. Analysis of the optimized regression model is performed and the system is calibrated and validated through healthy, prediabetic and diabetic patients. The robust regression models of serum glucose level is then deployed as the mechanism for precise measurement in iGLU 2.0. The performance of iGLU 2.0 is validated with the prediction of capillary blood glucose using Average Error (AvgE) and Mean Absolute Relative Difference (mARD) which are calculated as 6.09% and 6.07%, respectively, whereas for serum glucose, AvgE and mARD are estimated as 4.88% and 4.86%, respectively.

iGLU 2.0: A New Wearable for Accurate Non-Invasive Continuous Serum Glucose Measurement in IoMT Framework

In the case of diabetes, fingertip pricking for blood sample is inconvenient for glucose measurement. Invasive approaches like laboratory tests and one touch glucometers enhance the risk of blood related infections. To mitigate this important issue, this article introduces a novel Internet-of-Medical-Things (IoMT) enabled edge-device for precise, noninvasive blood glucose measurement. The device called “Intelligent Glucose Meter” (i.e., iGLU) is based on near-infrared (NIR) spectroscopy and a machine learning model of high accuracy. iGLU has been validated in a hospital and blood glucose values are stored in an IoMT platform for remote monitoring by endocrinologists.

iGLU: An Intelligent Device for Accurate Noninvasive Blood Glucose-Level Monitoring in Smart Healthcare

Diabetes is one of the prominent diseases around the world. Presently, invasive techniques need a finger prick blood sample . A repetitively painful procedure that produces the chance of infection. To resolve this issue, non-invasive measurement approach is proposed. In this paper, an efficient NIR wave based optical detection system is proposed with optimized post-processing regression model. After real-time data analysis, it has been found that the coefficient of determination (
 $$R^{2}$$
 ) is improved with the value of 0.9084 using proposed regression model. Mean absolute derivative is also increased with 3.87 mg/dl corresponding to predicted blood glucose concentration. Mean absolute relative difference has exceeded to 3.25%, and average error is improved with 3.77% using proposed regression model. Average accuaracy has been analyzed 94–95% for predicted blood glucose concentration.

A precise non-invasive blood glucose measurement system using NIR spectroscopy and Huber’s regression model

To best of the authors knowledge, this article presents the first-ever non-invasive glucometer that takes into account serum glucose for high accuracy. In case of blood glucose measurement, serum glucose value has always been considered precise blood glucose value during prandial modes. Serum glucose can be measured in laboratory and more stable glucose level compare to capillary glucose. However, this invasive approach is not convenient for frequent measurement. Sometimes, Conventional invasive blood glucose measurement may be responsible for cause of trauma and chance of blood related infections. To overcome this issue, in the current paper, we propose a novel Internet-of-Medical (IoMT) enabled glucometer for non-invasive precise serum glucose measurement. In this work, a near-infrared (NIR) spectroscopic technique has been used for glucose measurement. The novel device called iGLU 2.0 is based on optical detection and precise machine learning (ML) regression models. The optimal multiple polynomial regression and deep neural network models have been presented to analyze the precise measurement. The glucose values of serum are saved on cloud through open IoT platform for endocrinologist at remote location. To validate iGLU 2.0, Mean Absolute Relative Difference (mARD) and Average Error (AvgE) are obtained 6.07% and 6.09%, respectively from predicted blood glucose values for capillary glucose. For serum glucose, mARD and AvgE are found 4.86% and 4.88%, respectively. These results represent that the proposed non-invasive glucose measurement device is more precise for serum glucose compared to capillary glucose.

iGLU 2.0: A new non-invasive, accurate serum glucometer for smart healthcare

In the case of diabetes, fingertip pricking for a blood sample is inconvenient for glucose measurement. Invasive approaches like laboratory test and one-touch glucometer enhance the risk of blood-related infections. To mitigate this important issue, in the current paper, we propose a novel Internet-of-Medical-Things (IoMT) enabled edge-device for precise, non-invasive blood glucose measurement. In this work, a near-infrared (NIR) spectroscopic technique using two wavelengths (940 nm, 1300 nm) is taken to detect the glucose molecule from human blood. The novel device called iGLU is based on NIR spectroscopy and machine learning (ML) models of high accuracy. An optimal multiple polynomial regression model and deep neural network (DNN) model have been presented for precise measurement. The proposed device is validated and blood glucose values are stored on the cloud using open IoT platform for remote monitoring by an endocrinologist. For device validation, the estimated blood glucose values have been compared with blood glucose values obtained from the invasive device. It has been observed that mean absolute relative difference (MARD) and average error (AvgE) are found 4.66 % and 4.61 % respectively from predicted blood glucose concentration values. The regression coefficient is found 0.81. The proposed spectroscopic non-invasive device provides accurate and cost-effective solution for smart healthcare.

Prateek Jain

Papers

iGLU 2.0: A New Wearable for Accurate Non-Invasive Continuous Serum Glucose Measurement in IoMT Framework

iGLU: An Intelligent Device for Accurate Noninvasive Blood Glucose-Level Monitoring in Smart Healthcare

A precise non-invasive blood glucose measurement system using NIR spectroscopy and Huber’s regression model

iGLU 2.0: A new non-invasive, accurate serum glucometer for smart healthcare

iGLU 1.0: An Accurate Non-Invasive Near-Infrared Dual Short Wavelengths Spectroscopy based Glucometer for Smart Healthcare