IoT-Based Telemedicine Health Monitoring System with a Fuzzy Inference-Based Medical Decision Support Module for Clinical Risk Evaluation

This paper discusses the design and implementation of an Internet of Things (IoT)-based telemedicine health monitoring system (THMS) with an early warning scoring (EWS) function that reads, assesses, and logs physiological parameters of a patient such as body temperature, oxygen saturation level, systemic arterial pressure, breathing patterns, pulse (heart) rate, supplemental oxygen dependency, consciousness, and pain level using Particle Photon microcontrollers interfaced with biosensors and switches. The Mandami fuzzy inference-based medical decision support system (FI-MDSS) was also developed using MATLAB to assist medical professionals in evaluating a patient's health risk and deciding on the appropriate clinical intervention. The patient's physiological measurements, EWS, and health risk category are stored on the Particle cloud and Thing Speak cloud platforms and can be accessed remotely and in real-time via the Internet. Furthermore, a RESTful application programming interface (API) was developed using GO language and PostgreSQL database to enhance data presentation and accessibility. Based on the paired samples t-tests obtained from 6 sessions with 10 trials for each vital sign per session, there were no significant differences between the clinical data obtained from the designed prototype and the commercially sold medical equipment. The mean differences between the compared samples for each physiological data were not more than 0.40, the standard deviations were less than 2.3, and the p-values were greater than 0.05. With a 96.67% accuracy, the FI-MDSS predicted health risk levels that were comparable to conventional EWS techniques such as the Modified National Early Warning Score (m-NEWS) and NEWS2, which are used in the clinical decision-making process for managing patients with COVID-19 and other infectious illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Double-Network Protein Hydrogels as Flexible Pressure Sensors for Contactless Delivery

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.

IoT-based COVID-19 Realtime Monitoring and Alerting System in Brunei

A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, also known as COVID-19) is a major problem for many countries in the world. Brunei is also affected by this COVID-19 pandemic in many ways. To alleviate the burden on the health ministry, we developed a low-cost, reliable Internet of Things (IoT) based real-time health monitoring system to diagnose early COVID-19 symptoms for patients at home. This diagnosis includes the three important physiological parameters such as body temperature, heart rate and oxygen saturation level (SpO2) in the blood. This system comes with an OLED LCD to display the three parameters. Apart from that, these parameters are also displayed on a mobile dashboard using the Cayenne IoT platform for easy access. This system was evaluated against many people, and the results were compared against the industry-standard pulse oximeters which are remarkably close and dependable. © 2022 IEEE.

Towards depression monitoring and prevention in older populations using smart wearables: Quantitative Findings

Depression has become a growing concern over the recent years. Since the start of the COVID-19 pandemic, depression among all age groups has increased significantly. As mental health is often stigmatized among older aged people, it is less openly discussed or treated. We propose a mental health monitoring approach that limits explicit user interaction, using Fitbit smartwatch data to determine depressive tendencies in older-aged people. We analysed physiological user data extracted from a Fitbit Alta HR device and use this data to train a machine learning model to detect depressive tendencies. While this is not a diagnostic tool, the aim is to identify physiological signs early on and direct the user toward professional medical guidance and treatment. We trained 19 predictive models on our dataset, the gradient boosting regressor outperformed all other models. The best performing model achieved at R-square of 0.32 although most models were poorly performing. Due to the limited sample size, there is a risk of model overfitting. Although these preliminary results are promising for one model, they would need to be replicated in a larger sample of older people, who exhibit a wider range of depressive tendencies. © 2022 IEEE.

A Quantitative Approach and Preliminary Application in Healthy Subjects and Patients with Valvular Heart Disease for 24-h Breathing Patterns Analysis Using Wearable Devices

The 24-h breathing patterns may be closely related to health status as well as disease progression. However, there is no consistent and widely accepted approach for mining the potential value in 24-h respiratory signals based on wearable device monitoring. This study presented a reference approach including signal quality assessment, calibration of tidal volume, and breathing patterns parameters based on a wearable continuous physiological parameter monitoring system for 24-h breathing patterns analysis, including time domain, frequency domain and nonlinear domain. 70 healthy subjects and 76 patients undergoing heart valve surgery were enrolled in this study. The normal reference range of breathing patterns was calculated based on healthy subjects. A subgroup study was conducted based on whether patients developed postoperative pulmonary complications (PPCs). Compared with non-PPCs group, the coefficient of variation of breathing rate in the recumbent position was smaller in the PPCs group. During the daytime, the kurtosis of breathing rate and contribution of the abdomen was smaller in PPCs group. During the nighttime, the coefficient of variation of breathing rate and SD2 was smaller in the PPCs group. The quantitative method proposed in this study fills the gap in the field of quantifying 24-h breathing patterns which is effective in discriminating different populations and is expected to be used widely in the context of COVID-19 epidemic. © 2022 IEEE.

Investigating the effect of wearing masks on office work in indoor environments during a pandemic using physiological sensing

The spread of pandemics has adverse effects on the lives of people in various ways. For people who need to work in the office and other indoor environments, wearing a mask has become an essential precaution to reduce the spread of the virus and thereby the risk of disease transmission. Therefore, it is important to understand how wearing a mask will affect people while they are performing daily office work. This paper aims to investigate the effect of wearing a mask on the physiological responses and task performance of those who work in office environments during the pandemic period. The two most commonly used masks (i.e., cloth and surgical masks) are chosen for evaluation. The work engagement, mental workload, skin conductance level (SCL), heart rate (HR), as well as the overall performance of 20 subjects while they are completing simulated office tasks are collected and analyzed. Although the results vary across different individuals, they reveal that wearing a mask during a pandemic period will potentially reduce the mental workload and SCL of people for specific types of tasks. In addition, the task performance (correct number and correct rate) of the subjects is worse when wearing a mask, which is highly correlated to the results of the mental workload and SCL. However, there is no one-size-fits-all pattern to conclude the effect of wearing masks on work engagement and HR. This study provides a valuable reference for those who need to wear a mask while working. © 2022 Elsevier Ltd

Design and Implementation of an Interactive Photoplethysmography and Galvanic Skin Response Based Gamepad

Mental disorders in the young adult population are becoming more frequent, largely due to the COVID-19 pandemic. This has led to the need to find new ways to adapt to therapeutic methods, offering greater attractiveness for this age range, and in many studies, it has been reported that this can be achieved thanks to video games. In this work, a controller design for video games that allows to obtain some of the most relevant biological signals of the relationship between the physiological state and the mental state of the user is proposed. An accessible and non-invasive instrument was built, in the form of a video game controller, to make measurements of heart rate and the galvanic response of the skin, two physiological variables that play a vital role in determining a person’s emotional state, that allows, in turn, to play video games that are designed to be able to perform actions based on the measurements of biosignals, such as modifying the difficulty, improving the user experience, etc. Making use of two biosignal sensors (photoplethysmography and galvanic skin response), the controller is developed to offer non-invasive biofeedback while playing computer video games, which provides an effective approach to developing interactive and customizable diagnostic and therapeutic psychological tools. This work, which involves the unification of various ideas and fields, could mean an advance in the field of the development of digital alternatives for therapies related to mental health, as well as a tool that allows a greater approach on the part of the community to which it is focused. This may mean that, in future developments, there is greater cohesion and a greater boom in treatments for people considered young adults. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

EVALUATING LEARNING EXPERIENCE AND EMOTIONAL TRIGGERS OF VIRTUAL LEARNING ENVIRONMENTS (VLES) USING PSYCHOGALVANIC REFLEXES AND BEHAVIOURAL ANALYSIS

Virtual Environments (VEs) are on the rise as an instrument in various sectors involving emotional states and educational research. Studies till date have tried to explore the effectiveness of VR in a variety of emotional health interventions, treatment of learning phobias, and providing virtual support to students worldwide. Research has demonstrated that VR immersive environments and VR experiences create a significant impact on the users' psyche. A learning experience is related to the emotional state of the person (O'Regan, K. (2003). Therefore, it would be interesting to study the influence of VR experience on the emotional states of the learners. Students around the globe were already struggling with emotional crises even before the pre-covid situation as reported by multiple agencies but now the situation has turned more grievous. Here comes the need for magnified learning experiences in virtual learning environments (VLEs). This study investigates the impact of two different VR-3D learning environments. It draws a comparison between students' emotional states, VR experience, and VR design elements using neurophysiological tools like Galvanic Skin Response (GSR) and self-reporting questionnaires. In the experiment, participants were asked to go through two different VR learning simulations and their physiological responses were recorded for analysis. The two simulations were differentiated based on space and interaction design elements. The study suggests that well-designed Virtual 3D-Environments in an educational setup can help students in reducing stress levels and ways how we can elicit positive emotions and facilitate a better learning experience. © Proceedings of the 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022. All rights reserved.

Monitoring and processing of physiological and domotics parameters in an Internet of Things (IoT) assistive living environment

In relation to an ever changing epidemiological world context, a category of people that is more subject to be impacted consists of the elderly. Certain steps can be taken in order to improve their quality of life especially in case of illness. One way of achieving this is to have a smart assistive living environment, which includes home automation and medical monitoring. The proposed system expands on an IoT solution for assisted living and introduces a highly flexible rules engine for processing physiological and domotics data obtained from the home environment, and for interacting with the system actuators. As proof-of-concept, there are several use-cases that are discussed depending on the type of patient: diabetic, cardiac, hypertensive, obese, COVID or Alzheimer. These scenarios emphasize the efficiency of the proposed solution and offer an insight on the high degree of ion and extensibility of the system. © 2022 IEEE.

Contactless Diagnosis using Internet of Things (IoT) Technology for Covid-19 Suspect Patient

Coronavirus causes Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), sometimes referred to Covid-19. The virus has a high duplication power to spreads and infects peoples. WHO has declared this illness an extraordinary pandemic. The symptoms of Covid-19 suffer within 14 day incubation period are as follows: fever, cough, shortness of breath or difficulty breathing, headache, runny nose, and sore throat. The government of Indonesia has instructed that the hospital is reserved only for severe Covid-19 patients, while patients with suspected Covid-19 are advised to be treated by the community. On the other hand, they need to know about patients' health data and monitor them without direct contact. Telehealthcare has been utilized for diagnosis, treatment, and avoidance of illness. IoT could be implemented in the healthcare area, and sensors could be used to measure the patient's physiological parameters to the server. The systems read vital signs such as body temperature, measurement of breathing, coughing, and heart rate. In this research, we propose an assistive technology that roled and played within the community services for the suspect patient. © 2022 IEEE.

Rehabilitation of Post-COVID Patients: A Virtual Reality Home-Based Intervention Including Cardio-Respiratory Fitness Training

The post-COVID syndrome is emerging as a new chronic condition, characterized by symptoms of breathlessness, fatigue, and decline of neurocognitive functions. Rehabilitation programs that include physical training seem to be beneficial to reduce such symptoms and improve patients’ quality of life. Given this, and considering the limitations imposed by the pandemic on rehabilitation services, it emerged the need to integrate telerehabilitation programs into clinical practice. Some telerehabilitation solutions, also based on virtual reality (VR), are available in the market. Still, they mainly focus on rehabilitation of upper limbs, balance, and cognitive training, while exercises like cycling or walking are usually not considered. The presented work aims to fill this gap by integrating a VR application to provide cardio-respiratory fitness training to post-COVID patients in an existing telerehabilitation platform. The ARTEDIA application allows patients to perform a cycling exercise and a concurrent cognitive task. Patients can cycle in a virtual park while performing a “go/no-go” task by selecting only specific targets appearing along the way. The difficulty of the practice can be adjusted by the therapists, while the physiological response is continuously monitored through wearable sensors to ensure safety. The application has been integrated into the VRRS system by Khymeia. In the next months, a study to assess the feasibility of a complete telerehabilitation program based on physical and cognitive training will take place. Such a program will combine the existing VRRS exercises and the cardio-respiratory fitness exercise provided by the ARTEDIA application. Feasibility, acceptance, and usability will be assessed from both the patients’ and the therapists’ sides. © 2022, Springer Nature Switzerland AG.

A Framework for Infectious Disease Monitoring with Automated Contact Tracing - A Case Study of COVID-19

Throughout human history, deadly infectious diseases emerged occasionally. Even with the present-day advanced healthcare systems, the COVID-19 has caused more than six million deaths worldwide (as of 27 July 2022). Currently, researchers are working to develop tools for better and effective management of the pandemic. ’Contact tracing’is one such tool to monitor and control the spread of the disease. However, manual contact tracing is labor-intensive and time-consuming. Therefore, manually tracking all potentially infected individuals is a great challenge, especially for an infectious disease like COIVD-19. To date, many digital contact tracing applications were developed and used globally to restrain the spread of COVID-19. In this work, we perform a detailed review of the current digital contact tracing technologies. We mention some of their key limitations and propose a fully integrated system for contact tracing of infectious diseases using COVID-19 as a case study. Our system has four main modules -Case Maps, Exposure Detection, Screening, and Health Indicators that takes multiple inputs like users’self-reported information, measurement of physiological parameters, and information of the confirmed cases from the public health, and keeps a record of contact histories using Bluetooth technology. The system can potentially evaluate the users’risk of getting infected and generate notifications to alert them about the exposure events, risk of infection, or abnormal health indicators. The system further integrates the web-based information on confirmed Covid-19 cases and screening tools, which potentially increases the adoption rate of the system. IEEE

An IoT-enabled Electronic Textile-based Flexible Body Sensor Network for Real-time Health Monitoring in Assisted Living during Pandemic

This paper presents a cost-effective and flexible electronic textile sensor with high sensitivity and fast response and demonstrates its versatile applications, including real-time measurements of finger kinematics, phonation, cough patterns, as well as subtle muscle movements (i.e., eye reflex). The sensor can discriminate between speech and cough patterns, thereby expanding its applications to COVID-19 detection, speech rehabilitation training, and human/machine interactions. A combination of different sensor data is essential to acquire clinically significant information. Therefore, a sensor array is interfaced with the LoRa communication protocol to establish an Internet of Things (IoT)-based electronic textile framework. The IoT integration allows remote monitoring of body kinematics and physiological parameters. Therefore, the proposed IoT-based framework holds the potential to provide real-time and continuous health monitoring to allow immediate intervention during this pandemic. © 2022 IEEE.

A Low Cost Health Monitoring System by Maintaining Covid 19 Protocol

Social isolation and home quarantining have been standard procedures around the world, since the outbreak of the novel coronavirus (COVID-19) sickness pandemic. Due to the spread of the COVID-19 disease, patients' remote monitoring becomes even more important in this situation. There are two reasons for this: (i) They must be kept alive and their symptoms under control;(ii) they must not leave the quarantined region throughout the quarantine time. This work presents a low-cost method for sensing patients' the physiological characteristics and displaying them on an Android- based mobile application. The Arduino UNO, a DHT11 Humidity Temperature Sensor sensor device, and HC-05 Bluetooth module were carried out to make up the system. A sensor was included in the system to capture the physiological health parameters of patients automatically. As a result, the patients can be remotely observed using the suggested method from a safe distance, avoiding direct contact and adhering to social distancing procedures. It was developed using free available online platform for developing mobile application is MIT inventor-2. Thus, even at the most difficult stage of the COVID-19 epidemic, increased health and a comfortable lifestyle can be accomplished. © 2022 IEEE.

Physiological Behavior of COVID-19 Patients Using Wearable System and Markov Chains

This study seeks to continuously monitor key physiological parameters in progression and development in people infected with the COVID-19 virus. This will be developed through a wearable system where physiological and some hemodynamic data will be obtained. Based on this data collection, Markov chains will be applied in order to establish the probability of change in the progression of the disease. That is, the probability of complications from this condition or improvement of the infected patient. For the development of this study, a data collection system based mainly on MEMS-type sensors will be used, as well as the use of stochastic simulations to verify the development of Markov chains. It is important to mention that this is a study in development that seeks the rapid application for the monitoring of patients with this condition. © 2021 IEEE.

A Pilot study for Depression Detection during COVID-19 using Stroop Test

The Corona virus disease 2019 (COVID-19) pandemic has caused substantial increase in distress among people all over the world. This work aims to study depression during the COVID-19 among the educational sector and to develop a novel stroop test based depression detection system by analyzing the response time (RT) for normal stroop test (ST) and emotional stroop test (EST). The data for this work is collected from 44 participants. It is found that 66% of the participants have depression. The analysis of RT for ST and EST before and after showing video stimulus, indicates that there is a significant difference in change of response time (dRT) for both normal and depressive cases. Further this feature along with the physiological parameters (PhyP) of the participants are given to support vector machine (SVM) and extreme gradient boost (XGBoost) classifier to develop depression detection systems. The XGBoost provides highest accuracy of 85.71% with PhyP + ST dRT data and an accuracy of 71.43% with PhyP + EST dRT data. Therefore, the proposed systems may serve as a screening tool for depression during this pandemic situation. © 2021 IEEE

A Smart and Secure IoMT Tele-Neurorehabilitation Framework for Post-Stroke Patients

COVID-19 pandemic adversely challenged the healthcare system in an unprecedented way. Access to neurorehabilitation programme for patients with stroke and other neurological disability was severely restricted including shutting down of most community-based and outpatient facilities. There is hardly any organised virtual programme of exploring any potential of stretching and exercising of muscles needed in a rehabilitation programme. There is an impetus to innovate service developments, while the risks and fear of contracting the coronavirus remain prevalent. We propose a framework for developing a novel tele-neurorehabilitation system that will guide the patients to perform therapeutic exercises, as proposed by the clinicians, remotely. The system will allow patients to directly interact with doctors through a secure audio–video online portal. Wearable motion tracking sensors will be integrated within a hardware-based home setting for gathering performance data live from patients while they are performing exercises. The paper describes the design components of the framework justifying the tools, hardware, and protocols required to implement a secure online portal for tele-neurorehabilitation. Specifications of the core architectural layers have been reported. Some preliminary work demonstrates how the framework specifies capturing and analysing of physiological data using wearable sensors, as well as displaying of gait parameters on a software dashboard. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effect of Alpha Wave-Containing Music on the Psychological Stress Level of Hospital Staffs and their Cognitive Performance

This study aims to investigate the effect of alpha music therapy that is an affordable, easily implemented, and sustainable method on stress level, cognitive functions, and physiological response of hospital staff amidst the context of the COVID-19 pandemic. The testing group was required to listen to alpha music for two weeks. Stress questionnaires, cognitive tasks, and physiological data were collected before and after the intervention. Blood pressure and heart rate between the two groups do not differ significantly and change after intervention. The increase in PSS scores and fast response time in the Matrix Task of the Control group indicate increasing stress levels, reduced attention, and remembering ability. These results of the Control group explain the high workload at the year-end and COVID-19 outbreak in Vietnam occurring during the second data collection week. In contrast, both the PSS and Respond time measures suggest a positive effect of alpha music on the Testing group. © 2021 ECBIOS 2021. All rights reserved.

Physio6: A Sensor-Based Monitoring System for 6-Minute Walking Test in the Era of COVID-19

Coronavirus disease of 2019 (COVID-19) is still severe nowadays, and plentiful COVID-19 patients need careful rehabilitation. The 6-minute walking test (6MWT) is a common clinical trial that requires the patient to walk as far as possible in a corridor for 6 minutes, significantly indicating patients' cardiopulmonary disease conditions and rehabilitation. A traditional 6MWT provides the 6-minute walking distance (6MWD) as the primary result for clinical analysis. In this paper, we propose Physio6, a sensor-based monitoring system for 6MWT, which monitors one patient's various physiological signals and indicates her/his condition during the test. The system also provides the functions of early warning based on physiological signal monitoring and automatically or manually recording the adverse events, such as hypoxia or dyspnea. Moreover, Physio6 is able to communicate with the existing systems in hospitals, and to generate a comprehensive report that summarizes the performance of the patient in the current 6MWT and even in the past ones. Our system has been deployed in four hospitals. Compared with the conventional distance-based measurement, our preliminary validation reveals that the extracted physiological parameters are promisingly valuable for clinical decision-making. System quality and device comfort are also confirmed by questionnaires. The potential of leveraging this system to perform the remote 6MWT at home/in communities as a solution of COVID-19 patient rehabilitation monitoring is also discussed. © 2021 IEEE.

CheckING: An IoT Device for COVID-19 Monitoring and Prevention

The COVID-19 pandemic has spread rapidly around the world forcing people to isolate at home and collapsing hospitals causing millions of deaths. The continuous and efficient monitoring of those who showed symptoms jointly with the analysis of the environment conditions to avoid the spread of the virus gave rise to the development of different technological alternatives. In the present work, a comprehensive device with multi-parameter sensing has been designed, emphasizing the integration of physiological and environmental parameters with remote monitoring, of the interest in the current pandemic context. © 2021 IEEE.