A Novel Real-Time Non-invasive Hemoglobin Level Detection Using Video Images from Smartphone Camera

Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory gold standard. We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works

Vital Sensory Kit For Use With Telemedicine In Developing Countries

In many developing countries, a large percentage of the population lacks access to adequate healthcare. This is especially true in India where close to 70% of the population lives in rural areas and has little to no access to hospitals or clinics. People living in rural India often times cannot afford to pay to see a doctor should they need to make the journey to a hospital. Telemedicine, a breakthrough in the past couple decades, has broken down the barrier between the patient and the physician. It has slowly been implemented in India to make doctors more available to patients through the use of video conferences and other forms of communication. A compact and affordable kit has been developed that will be used to take a patient’s blood pressure, heart rate, blood glucose concentration and oxygen saturation. Our most novel contribution is the non-invasive glucose sensor that will use a near-infrared LED and photodiode in the patient’s earlobe. Currently millions of diabetics do this by pricking their finger. By wirelessly sending data results from the vital sign kit, the first essential part of a treatment can be carried out via wireless communication, saving the doctor and patient time and money

e-CoVig: a novel mHealth system for remote monitoring of symptoms in COVID-19

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).In 2019, a new virus, SARS-CoV-2, responsible for the COVID-19 disease, was discovered. Asymptomatic and mildly symptomatic patients were forced to quarantine and closely monitor their symptoms and vital signs, most of the time at home. This paper describes e-CoVig, a novel mHealth application, developed as an alternative to the current monitoring paradigm, where the patients are followed up by direct phone contact. The e-CoVig provides a set of functionalities for remote reporting of symptoms, vital signs, and other clinical information to the health services taking care of these patients. The application is designed to register and transmit the heart rate, blood oxygen saturation (SpO2), body temperature, respiration, and cough. The system features a mobile application, a web/cloud platform, and a low-cost specific device to acquire the temperature and SpO2. The architecture of the system is flexible and can be configured for different operation conditions. Current commercial devices, such as oximeters and thermometers, can also be used and read using the optical character recognition (OCR) functionality of the system. The data acquired at the mobile application are sent automatically to the web/cloud application and made available in real-time to the medical staff, enabling the follow-up of several users simultaneously without the need for time consuming phone call interactions. The system was already tested for its feasibility and a preliminary deployment was performed on a nursing home showing promising results.This work was funded by Fundação para a Ciência e Tecnologia (FCT) under the grants e-CoVig—Project 255_596880547, and LARSyS—Project UIDB/50009/2020, by FCT/MCTES through national funds and, when applicable, co-funded EU funds under the grant NICE-HOME—Project UIDB/50008/2020, and by the IT—Instituto de Telecomunicações under grant BI/No. 13—19 May 2020 “AIMHealth”, which is gratefully acknowledged.info:eu-repo/semantics/publishedVersio

IoT Platform for COVID-19 Prevention and Control: A Survey

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.Comment: 12 pages; Submitted to IEEE Internet of Things Journa

Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones

Nowadays, due to the widespread use of smartphones in everyday life and the improvement of computational capabilities of these devices, many complex tasks can now be deployed on them. Concerning the need for continuous monitoring of vital signs, especially for the elderly or those with certain types of diseases, the development of algorithms that can estimate vital signs using smartphones has attracted researchers worldwide. Such algorithms estimate vital signs (heart rate and oxygen saturation level) by processing an input PPG signal. These methods often apply multiple pre-processing steps to the input signal before the prediction step. This can increase the computational complexity of these methods, meaning only a limited number of mobile devices can run them. Furthermore, multiple pre-processing steps also require the design of a couple of hand-crafted stages to obtain an optimal result. This research proposes a novel end-to-end solution to mobile-based vital sign estimation by deep learning. The proposed method does not require any pre-processing. Due to the use of fully convolutional architecture, the parameter count of our proposed model is, on average, a quarter of the ordinary architectures that use fully-connected layers as the prediction heads. As a result, the proposed model has less over-fitting chance and computational complexity. A public dataset for vital sign estimation, including 62 videos collected from 35 men and 27 women, is also provided. The experimental results demonstrate state-of-the-art estimation accuracy.Comment: 6 pages, 9 figures, 4 table

A smartphone-based chemosensor to evaluate antioxidants in agri-food matrices by in situ AuNP formation

In recent years, there has been a continuously growing interest in antioxidants by both customers and food industry. The beneficial health effects of antioxidants led to their widespread use in fortified functional foods, as dietary supplements and as preservatives. A variety of analytical methods are available to evaluate the total antioxidant capacity (TAC) of food extracts and beverages. However, most of them are expensive, time-consuming, and require laboratory instrumentation. Therefore, simple, cheap, and fast portable sensors for point-of-need measurement of antioxidants in food samples are needed. Here, we describe a smartphone-based chemosensor for on-site assessment of TAC of aqueous matrices, relying on the antioxidant-induced formation of gold nanoparticles. The reaction takes place in ready-to-use analytical cartridges containing an hydrogel reaction medium preloaded with Au(III) and is monitored by using the smartphone’s CMOS camera. An analytical device including an LED-based lighting system was developed to ensure uniform and reproducible illumination of the analytical cartridge. The chemosensor permitted rapid TAC measurements of aqueous samples, including teas, herbal infusions, beverages, and extra virgin olive oil extracts, providing results that correlated with those of the reference methods for TAC assessment, e.g., oxygen radical absorbance capacity (ORAC)

Employment of artificial intelligence mechanisms for e-Health systems in order to obtain vital signs improving the processes of online consultations and diagnosis

A large number of web-based e-Health applications have been developed through time, allowing doctors to access different types of functionalities, like knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person. The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time and allowing the visualization of all historical data of a patient. It aims to defend the concept of promoting online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.info:eu-repo/semantics/acceptedVersio