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The adoption of the Internet of Things (IoT) has revolutionized the way the health care industry works. IoT en-abled smart and connected solutions like smart sensors, wearable devices, and smart health monitoring systems are used to unleash the potential growth of the health care industry. IoT based health care solutions are on greater priority among IoT service providers since the disruptions caused by the COVID-19. According to experts, there still exist white spots in research studies on the Internet of Things (IoT) and health care Systems. The study conducted in this paper aims to explore emerging global research trends and topical focus in the field of IoT in health care System. Bibliometric analysis is used to analyze the research articles on 'Internet of Things' and 'Health care Systems' extracted from SCOPUS and WoS database using VoS Viewer tool;the analysis used to assess the growth and research trends of different research fields over a period of time. The parameters considered during analysis include year-wise citations, year-wise publications, keyword clustering analysis, author-wise analysis, country-wise research trends and publication trend over the years. The results showcased that there has been significant change in utilization of IoT in healthcare systems continuously during the period under study conducted. © 2022 IEEE.
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Body temperature is a significant vital sign that can provide great insight as to the state of health of a person. Nowadays, body temperatures are monitored as often as a precaution for the COVID-19 virus. This can be achieved with the use of wearables, which can be non-invasive and convenient for anybody to use. This study aims to design and construct a wearable that can accurately detect the body temperature of a person using the MLX90614 sensor as well as an I2C enabled LCD to allow the user to monitor their temperature at a moment's notice. © 2023 IEEE.
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The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system's fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population's aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask's breathing zone in order to use the patient's breath as a fingerprint for various diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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Wearable sensor technologies have improved people's daily lives through their applications in almost every field. Sensor technologies of inventive kinds are used in an extensive variety of applications in lifestyle, healthcare, fitness, manufacturing, etc. There have also been crucial issues in making significant improvements to the actual mechanical, electrical, and optical sensing methods mainly in upgrading the precision of identification of wearable sensors to various stimuli. With an extensive study of the basic demands in wearable device technology as of now, the road map becomes clearer for creating greater innovations in the future. This is a review that gives an outline of types of wearable sensors by the score that is utilized in daily life. © 2023 IEEE.
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Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly used for remote patient monitoring, especially during the COVID-19 pandemic, for the management of chronic diseases and neurological disorders. There is an urgent need to evaluate the accuracy of these devices. This scoping review considers the latest applications of wearable PPG sensors with a focus on studies that used wearable PPG sensors to monitor various health parameters. The primary objective is to report the accuracy of the PPG sensors in both real-world and clinical settings. Methods: This scoping review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Studies were identified by querying the Medline, Embase, IEEE, and CINAHL databases. The goal was to capture eligible studies that used PPG sensors to monitor various health parameters for populations with a minimum of 30 participants, with at least some of the population having relevant health issues. A total of 2,996 articles were screened and 28 are included in this review. Results: The health parameters and disorders identified and investigated in this study include heart rate and heart rate variability, atrial fibrillation, blood pressure (BP), obstructive sleep apnea, blood glucose, heart failure, and respiratory rate. An overview of the algorithms used, and their limitations is provided. Conclusion: Some of the barriers identified in evaluating the accuracy of multiple types of wearable devices include the absence of reporting standard accuracy metrics and a general paucity of studies with large subject size in real-world settings, especially for parameters such as BP.
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The deadly virus COVID-19 has heavily impacted all countries and brought a dramatic loss of human life. It is an unprecedented scenario and poses an extreme challenge to the healthcare sector. The disruption to society and the economy is devastating, causing millions of people to live in poverty. Most citizens live in exceptional hardship and are exposed to the contagious virus while being vulnerable due to the inaccessibility of quality healthcare services. This study introduces ubiquitous computing as a state-of-The-Art method to mitigate the spread of COVID-19 and spare more ICU beds for those truly needed. Ubiquitous computing offers a great solution with the concept of being accessible anywhere and anytime. As COVID-19 is highly complicated and unpredictable, people infected with COVID-19 may be unaware and still live on with their life. This resulted in the spread of COVID-19 being uncontrollable. Therefore, it is essential to identify the COVID-19 infection early, not only because of the mitigation of spread but also for optimal treatment. This way, the concept of wearable sensors to collect health information and use it as an input to feed into machine learning to determine COVID-19 infection or COVID-19 status monitoring is introduced in this study. © 2023 IEEE.
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The replication of biological systems by mechanical and electronic devices is referred to as bionics. The bionics industry has grown along four primary application areas, in addition to hearing, vision, orthopedics, and a small, dispersed group of implants that enhance cardiac and neurological functions. The SARS-CoV-2 virus is the infectious disease known as coronavirus disease (COVID-19). The virus-infected people require assistance to better understand the situation caused by COVID-19 and to bring some easy, efficient, and effective solutions. One of the solutions mentioned for the early stages involves wearable sensors with temperature sensors for early Covid-19 identification and photos delivered to an AI-enabled smartphone, robotic sensor, or robot itself. In severe situations, lung X-ray images are captured by robotic and remote sensors, and the lungs are given the right medication to finish off the virus. The paper presents the study on the overview, applications of artificial intelligence, and deep learning from the bionics point of view. Deep learning and machine learning will be used for reducing the Covid-19 outbreak. Wearable sensors provide important data by having temperature-embedded sensors in several physical devices that reveal details about the environment and body that are connected. Covid-19 probability prediction is aided by smartphones with artificial intelligence and machine learning capabilities. Case history, doctor notes, chest X-ray reports, details on the sites of breakouts, and other criteria can help forecast the severity of Covid-19 when it is in its severe phases and direct the administration of medication to a specific area of the lungs.
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Yoga has been a great form of physical activity and one of the promising applications in personal health care. Several studies prove that yoga is used as one of the physical treatments for cancer, musculoskeletal disorder, depression, Parkinson's disease, and respiratory heart diseases. In yoga, the body should be mechanically aligned with some effort on the muscles, ligaments, and joints for optimal posture. Postural-based yoga increases flexibility, energy, overall brain activity and reduces stress, blood pressure, and back pain. Body Postural Alignment is a very important aspect while performing yogic asanas. Many yogic asanas including uttanasana, kurmasana, ustrasana, and dhanurasana, require bending forward or backward, and if the asanas are performed incorrectly, strain in the joints, ligaments, and backbone can result, which can cause problems with the hip joints. Hence it is vital to monitor the correct yoga poses while performing different asanas. Yoga posture prediction and automatic movement analysis are now possible because of advancements in computer vision algorithms and sensors. This research investigates a thorough analysis of yoga posture identification systems using computer vision, machine learning, and deep learning techniques.
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In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson's correlation coefficient on WEKA for features' importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively).
Subject(s)
Wearable Electronic Devices , Humans , Machine Learning , Algorithms , Random Forest , Data CollectionABSTRACT
Thanks to a general multidisciplinary and interdisciplinary approach, during the last few decades there have been huge advances in the diagnostic field. In particular, the miniaturization and automation of several assays have led to the development of the so-called point-of-care tests (PoCT), which are devices capable to provide accurate and specific detection of analytes such as glucose, other clinically-relevant biomarkers, pathogens, and drugs. The detection with these devices typically takes place in a few minutes and without the need of specialized personnel. Here we discuss the key technologies and applications of PoCTs, as well as the major challenges in the clinical environment. © 2023 Elsevier Ltd. All rights reserved
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Patient sensing and data analytics provide information that plays an important role in the patient care process. Patterns identified from data and Machine Learning (ML) algorithms can identify risk/abnormal patients' data. Due to automatization this process can reduce workload of medical staff, as the algorithms alert for possible problems. We developed an integrated approach to monitor patients' temperature applied to COVID-19 elderly patients and an ML process to identify abnormal behavior with alerts to physicians. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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BACKGROUND: The COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions. METHODS: Twenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors (Clinical Trial: NCT04204493; https://clinicaltrials.gov/ct2/show/NCT04204993). This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semi-supervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the post-inoculation dataset. RESULTS: Inoculation with this challenge virus was well-tolerated with an infection rate of 85%. With the model classification threshold set so that no alarms were recorded in the 170 healthy days recorded, the algorithm correctly identified 16 of 17 (94%) positive presymptomatic and asymptomatic individuals, on average 58 hours post inoculation and 23 hrs before the symptom onset. CONCLUSION: The data processing and modeling methodology show promise for the early detection of respiratory illness. The detection algorithm is compatible with data collected from smartwatches using optical techniques but needs to be validated in large heterogeneous cohorts in normal living conditions.
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This paper describes the design and software implementation of a wearable prototype that allows users to monitor the vital signs of COVID-19 patients in quarantine areas. This prototype consists of two parts, the bracelet, andthe Base control unit (BCU). The bracelet is built with ESP8266 and sensors as main components, as well as the battery and other parts needed to fulfill the system's purpose (monitoring the vital signs of COVID-19 patients). At the same time, the Raspberry Pi (SCB) single board computer and GSM/GPRS/HAT are the main components of the Basic Control Unit (BCU). The current work describes the main parts of the pseudocode, as well as the activity diagram for the microcontroller and Raspberry Pi. This paper describes the mechanism of sending alert messages, whereby the system's ability to configure two types of alert messages;(1) physician Messages (these Messages will be sent to the physicianassociated with the patient if one or more vital signs reach a critical value;these messages contain all measurements of a patient's vital signs);(2) Authorize messages (these messages will be sent if the quarantine rules are violated;the patient's location will be sent to the authorized person as a Google Mapslink). Also, this paper describes the graphical user interface for communication, management,. and interaction between the users of the system. © 2022 IEEE.
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Sensor technology has become an integral part of the diagnosis, monitoring, therapeutic and surgical areas of medical science. Various sensors like glucose biosensors for diagnosis of diabetes mellitus or fluorescent sensors for gene expression and protein localization have become a common part of the biomedical field. Due to their widespread applications, various advances and improvements have taken place in medical sensor technology which has led to an increase in the ease and accuracy of diagnosis as well as treatment of diseases. This review article aims at studying various novel and innovative developments in biosensors, fibre optic sensors, sensors used for microelectromechanical systems, flexible sensors and wearable sensors. This article also explores new sensing methodologies and techniques in different medical domains like dentistry, robotic surgery and diagnosis of severe life-threatening diseases like cancer and diabetes. Various sensors and systems used for rapid detection of the SARS-CoV-2 virus which is responsible for the COVID-19 pandemic have also been discussed in this article. Comparison of novel sensor-based systems for detection of various medical parameters with traditional techniques is included. Further research is necessary to develop low cost, highly accurate and easy-to-use medical devices with the help of these innovative sensor technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The COVID-19 pandemic has affected a variety of aspects of our everyday life. Most activities like entertainment, healthcare, education and businesses have been reshaped due to the safety guidelines. Proper monitoring in indoor areas is essential to limit the spread of COVID-19. This paper presents a low-cost prototype system that addresses the indoor safety issue by combining a mask detector and temperature measurement system with a smart wearable band which alerts people to maintain social distance in close vicinity. The focus is on ensuring safe distance, wearing a mask, and no entry for people with high temperatures. Firstly, the mask and temperature system has an Arduino NANO that works as the primary device. The Arduino is connected with an ESP32-Cam that sends the image to a client where we have trained and developed a machine learning model using thousands of masked and unmasked pictures. Following, the model uses an image classification algorithm with the tensorflow.js model and gives us the result with an accuracy percentage. Secondly, the temperature is measured with the help of an MLX90614 non-contact sensor. The temperature of a person is also shown on the monitor at of. Finally, a wearable device is presented with a NodeMCU 8266 Wi-Fi module. It uses Received Signal Strength Indicator (RSSI) value to detect another similar device and alerts through a vibrator and buzzer if the social distance rules are violated. We evaluated the system in real-life scenarios, and the mask detection system gives an average accuracy of 98.7%. We have presented an in-depth analysis of the Mask Detection System, showing different mask types, the accuracy of the machine learning algorithm, temperature measurements and results. Similarly, the distance measurement system is presented with several factors. © 2022 IEEE.
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To meet the growing demand for sustainable development and ecofriendliness, hydrogels based on biopolymers have attracted widespread attention for developing flexible pressure sensors. Natural globular proteins exhibit great potential for developing biobased pressure sensors owing to their advantages of high water solubility, easy gelation, biocompatibility, and low production cost. However, realizing globular protein hydrogel-based sensors with interfacial and bulk toughness for pressure sensing and use in wearable devices remains a challenge. This study focuses on developing a high-performance flexible pressure sensor based on a biobased protein hydrogel. Consequently, a flexible protein/polyacrylamide (PAM) hydrogel with a featured double-network (DN) structure linked covalently with hydrogen bonds was first synthesized via a one-pot method based on natural ovalbumin (OVA). The unique DN structure of the as-synthesized OVA/PAM hydrogel affords excellent mechanical performance, flexibility, and adhesion properties. The mechanical properties of the DN hydrogel were enhanced after further cross-linking with Fe3+ and treatment with glycerol. Subsequently, the flexible pressure sensor was constructed by sandwiching a microstructured OVA/PAM dielectric layer between two flexible silver nanowire electrodes. The obtained sensor exhibits a high sensitivity of 2.9 kPa-1 and a short response time of 18 ms, ensuring the ability to monitor physiological signals. Based on its excellent performance, the fabricated sensor was used for monitoring the signals obtained using practical applications such as wrist bending, finger knocking, stretching, international Morse code, and pressure distribution. Particularly, we implemented a contactless delivery system using the fabricated OVA-based pressure sensors linked to unmanned vehicles and global positioning systems, providing a solution for low-risk commodity distribution during Coronavirus disease 2019 (COVID-19). © 2023 American Chemical Society.
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Physical fitness is the prime priority of people these days as everyone wants to see himself as healthy. There are numbers of wearable devices available that help human to monitor their vital body signs through which one can get an average idea of their health. Advancements in the efficiency of healthcare systems have fueled the research and development of high-performance wearable devices. There is significant potential for portable healthcare systems to lower healthcare costs and provide continuous health monitoring of critical patients from remote locations. The most pressing need in this field is developing a safe, effective, and trustworthy medical device that can be used to reliably monitor vital signs from various human organs or the environment within or outside the body through flexible sensors. Still, the patient should be able to go about their normal day while sporting a wearable or implanted medical device. This article highlights the current scenario of wearable devices and sensors for healthcare applications. Specifically, it focuses on some widely used commercially available wearable devices for continuously gauging patient's vital parameters and discusses the major factors influencing the surge in the demand for medical devices. Furthermore, this paper addresses the challenges and countermeasures of wearable devices in smart healthcare technology. © 2023 IEEE.
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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.
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"Human Activity Recognition" (HAR) refers to the ability to recognise human physical movements using wearable devices or IoT sensors. In this epidemic, the majority of patients, particularly the elderly and those who are extremely ill, are placedin isolation units. Because of the quick development of COVID, it's tough for caregivers or others to keepan eye on them when they're in the same room. People are fitted with wearable gadgets to monitor them and take required precautions, and IoT-based video capturing equipment is installed in the isolation ward. The existing systems are designed to record and categorise six common actions, including walking, jogging, going upstairs, downstairs, sitting, and standing, using multi-class classification algorithms. This paper discussed the advantages and limitations associated with developing the model using deep learning approaches on the live streaming data through sensors using different publicly available datasets. © 2022 IEEE
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Health awareness has increased worldwide since the COVID 2019 pandemic, creating a strong demand for wearable electronics. Wearable sensors for monitoring a patient's health are prevalent to reduce medical costs and decrease in-person clinic visits. Integrating electronics into clothes is challenging because most fabrics are porous and incompatible with the existing manufacturing methods, such as screen printing. The indirect printing method was employed to fabricate electrical circuitry on a textile substrate by printing it on a heat transfer polymer (HTP) and attaching it to the target cloths by stitching or glueing. Such a fabrication process has the potential to lead the way in developing new intelligent clothes. However, the durability of the printed circuitry in this manufacturing process on a cloth is still unknown and requires investigation. Therefore, this paper's objective is to study the durability of printed circuitries on fabric by applying constant cyclic loading. The test vehicle is a printed conductive silver interdigitating circuitry on fabric. Another test vehicle on a polyethylene terephthalate (PET) substrate was fabricated for a benchmark. A constant cyclic loading at 1Hz at a 50% duty cycle was applied to the test vehicles 100,000 times. The printed circuitry was monitored by logging the voltage in an electrical voltage divider configuration while the sensor was pressed and released. The result indicates that the fabric test vehicle can still function after the 100,000 cycles of the cyclic loading test and is comparable to that on the PET substrate. The recorded voltage-to-force values of the printed sensor on the fabric drifted upward and downward up to 3% over the loading cycles. The optical microscope observation on the cyclic loading samples showed signs of shear stresses on the printed silver and electrically conductive films, which could cause the tips of the silver interdigitating fingers to shatter. The study indicates that the properly manufactured circuits on fabric can be reliable and utilized for wearable applications. © 2022 IEEE.