ABSTRACT
The Covid-19 pandemic has increased the pressure on organizations to ensure health and safety in the workplace. An increasing number of organizations are considering wearables and physiolytics devices as part of their safe return to work programs so as to comply with governments' accountability rules. As with other technologies with ambivalent use (i.e., simultaneously beneficial and harmful), the introduction of these devices in work settings is met with skepticism. In this context, nudging strategies as a way of using design, information, and other ways to manipulate behaviors (system 1 nudge) and choices (system 2 nudge) has gained traction and is often applied alongside the introduction of ambivalent technologies with the aim to "nudge” their use. While the feasibility of different nudge strategies is often studied from only a managerial perspective, where employees' volitional autonomy and dignity is often treated as secondary, we explore which nudges are acceptable from the perspectives of ordinary workers. Using Q-methodology as a more evolutionary and participatory way to design nudges, we describe five basic strategies that are (to varying degrees) acceptable to them: (a) positive reinforcement and fun, (b) controlling the organizational environment, (c) self-responsibility, (d) collective responsibility, and (e) adapting the individual environment. Our findings show that there is a wide range of viewpoints on what is being considered an acceptable nudge and stress the importance of a transparent, equal dialogue between those who design nudges and potential nudgees. © Association for Information Technology Trust 2023.
ABSTRACT
With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%.
ABSTRACT
Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.
ABSTRACT
Wireless body area networks (WBANs) are helpful for remote health monitoring, especially during the COVID-19 pandemic. Due to the limited batteries of bio-sensors, energy-efficient routing is vital to achieve load-balancing and prolong the network's lifetime. Although many routing techniques have been presented for WBANs, they were designed for an application, and their performance may be degraded in other applications. In this paper, an ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP) is introduced as an adaptive real-time remote health monitoring in WBANs. The motivation behind this technique is to utilize the superior route optimization solutions offered by metaheuristics and to integrate them with the real-time routing capability of machine learning. The proposed method involves two phases: offline model tuning and online routing. During the offline pre-processing step, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is used to optimize routes across various WBAN configurations. By applying WOA for multiple WBANs, a comprehensive dataset is generated. This dataset is then used to train and test a machine learning regressor that is based on support vector regression (SVR). Next, the optimized MDML-RP model is applied as an adaptive real-time protocol, which can efficiently respond to just-in-time requests in new, previously unseen WBANs. Simulation results in various WBANs demonstrate the superiority of the MDML-RP model in terms of application-specific performance measures when compared with the existing heuristic, metaheuristic, and machine learning protocols. The findings indicate that the proposed MDML-RP model achieves noteworthy improvement rates across various performance metrics when compared to the existing techniques, with an average improvement of 42.3% for the network lifetime, 15.4% for reliability, 31.3% for path loss, and 31.7% for hot-spot temperature.
ABSTRACT
This work focuses on the development of a portable physiological monitoring framework that can continuously monitor the patient's heartbeat, oxygen levels, temperature, ECG measurement, blood pressure, and other fundamental patient's data. As a result of this, the workload and the chances of being infected by COVID-19 of the health workers will be reduced and an efficient patient monitoring system can be maintained. In this paper, an IoT based continuous monitoring system has been developed to monitor all COVID-19 patient conditions and store patient data in the cloud server using Wi-Fi Module-based remote communication. In this monitoring system, data stored on IoT platform can be accessed by an authorized individual and ailments can be examined by the doctors from a distance based on the values obtained. If a patient's physical condition deteriorates, the doctor will immediately receive the emergency alert notification. This model proposed in this research work would be extremely important in dealing with the Corona epidemic around the world. © 2022 IEEE.
ABSTRACT
The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help. © 2023 SCPE.
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This paper describes how an IoT -based health monitoring system was conceived and built (IoT). With the proliferation of new technologies, doctors nowadays are constantly on the lookout for cutting-edge electronic tools that will make it simpler to detect abnormalities in the human body. The Internet of Things makes it possible to create cutting-edge, non-intrusive healthcare assistance systems. In this article, we introduce the Comprehensive Health Monitoring System, or CHMS. Normal people can't afford to buy separate devices or make frequent trips to hospitals. Our CHMS will monitor a patient's vitals, including temperature, heart rate, and oxygen saturation (OS), and relay that information to a portable device. To make sense of the information gathered by the physical layer's sensors, the logical layer must analyses it. The application layer then makes judgments based on the processed data from the logical layer. The primary goal is to reduce costs for average consumers. Patients will have simple access to individual healthcare, in addition to financial sustainability. This study introduces an IoT -based system that would streamline the operation of a complex medical gadget while reducing its associated cost, allowing its users to do so from the comfort of home. The public's adoption of these gadgets as aids in a given setting might have significant effects on their own lives. © 2023 IEEE.
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COVID 19 has had a major effect on society. In order to keep people's spacing, new requirements have been placed in place regarding the amount of users authorized in individual rooms in offices, shops, etc. Along with social distance, regular temperature verification at mall entrances are indeed permitted. An excellent embedded machine learning system is proposed in this work to identify face masks automatically and detect the body's temperature in a real-time application. The proposed system, in particular, utilizes a raspberry pi camera to capture real-time video simultaneously by identifying face masks with the help of a classification technique. The face mask detector is constructed by utilizing mobilenetv2 and imaging net pre-trained weights to consider three scenarios: wearing a mask correctly, wearing a mask incorrectly, and not wearing any at all. By placing a temperature gauge on a Raspberry Pi, a framework has also been developed for determining a person's body temperature. The numerical outcomes show the feasibility and performance of our integrated devices in compared to many cutting-edge research. This temperature and facemask detection device monitors a person's body heat and detects whether or not that person is wearing a facemask. Consequently, any organization's entrance could contain this device. In this study, the door is only released if the temperature is below 99° F, which would be calculated by the Electro Selective Pattern-32 images, the MLX sensor, and the fact that a person's face is 80% protected by a facemask. © 2023 IEEE.
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Nowadays, health monitoring is crucial, especially monitoring the temperature and heartbeat of the patient in Covid / non-Covid situations. Continued monitoring of the patient is not a possible and tedious job. IoT plays a critical role in Hospitals where patients are in Intensive Care Units (ICU), and patients are treated at home (isolation points). The devices receive data continuously and monitor by the doctors remotely. This paper presents temperature and heartbeat monitoring using Internet Of Things (IoT) devices with an algorithm to capture data from devices and sends it to computer devices at a reasonable cost. Proof Of Concept has been created with the help of an Arduino board, Pulse Sensor, Temperature Sensor, Breadboard, ESP8266 Wi-Fi module, and Liquid Crystal Display (LCD). The IoT devices capture data from different devices (patient health data) in real time. The Health Care Monitoring (HCM) Application builds using microservices architecture, runs on top of the Thingspeak data, and sends notifications to the doctors if there is an emergency. The doctors can act according to rather than monitor continuously. This model eliminates manual intervention for taking the reading from time to time. © 2022 IEEE.
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The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation. Author
ABSTRACT
The application of machine learning in the medical field is still limited. The main reason behind the lack of use is the unavailability of an easy-to-use machine learning system that targets non-technical users. The objective of this paper is to propose an automated machine learning system to aid non-technical users. The proposed system provides the user with simple choices to provide suggestions to the system. The system uses the combination of the user's choices and performance evaluation to select the most suited model from available options. In this study, we employed the system on a Parkinson's disease dataset. The templates for support vector machine and random forest algorithms are provided to the system. Support vector machines and random forests were able to produce 80% and 75% accuracy, respectively. The system used performance parameters of the system and user choices to select the most suited models for each test case. The support vector machine was selected as the most suited model in three test cases, while random forest was selected as the most suited for one test case. The test cases also showed that the weighted time parameter impacted the results heavily.Copyright © 2022 The Author(s)
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This paper discusses an IoT -based smart wheelchair through which the elderly and those who are physically challenged i.e., those who cannot do the basic movement without the help of others, will be able to do their basic movement. This wheelchair will also allow COVID-19 patients to move from one place to another in a relatively contactless condition at the hospital or airport. This wheelchair comes with a smart band through which the basic physical condition of the body, such as body temperature, pulse rate, blood oxygen, etc. parameters can be known. If the level of any of these parameters is abnormal, the system will immediately send a notification to the user's family member or access person. Additionally, the system has location tracking through which family members can track the user's location whenever they want. NodeMCU, temperature sensors, pulse sensors, etc., have been used as hardware to build the system and a mobile application designed for remote monitoring. © 2023 IEEE.
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Globally, COVID-19 pandemic has influenced and changed norms and common health cultures. Different countries have implemented risk management and dealt with the condition based on the applicability of the international measures and some uniquely to their situations. As technology has become a key tool in daily lives and smart phones and connectivity has become a common necessity for most of the world's population, these can be used to help face the pandemic and the new normal it brings. Using one of the widely used software platforms, the research intends to design a framework for a health monitoring application for private institutions. © 2022 IEEE.
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A multifunctional medical device for the aid of COVID affected patients are scarce. This paper proposes an automated medical device which is incorporated with a feedback mechanism and a GSM base emergency alarm system. The combined sensors in the prototype can acquire readings of a patient's temperature, heart rate, oxygen saturation (SpO2), respiratory rate (RR), and heart condition noninvasively and can send these vitals easily via SMS in real time. Based on the patient's SpO2 level and RR, the system can control the oxygen flow through a nasal canola with the aid of a servo motor mechanism. The system derives information from the sensors to operate automatically based on the degrading vitals of a patient. Due to its nature of user friendliness the protype can be operated without much prior medical knowledge. © 2023 IEEE.
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BACKGROUND: People with intellectual disabilities (ID) are at high risk of developing respiratory health issues. The COVID-19 pandemic has compounded this, with serious consequences, and for some, death. Despite home-based oxygen saturation monitoring being recommended for people with ID, there is a stark lack of evidence in the literature on its feasibility. METHOD: We conducted 3-day baseline home-based oxygen saturation monitoring, using pulse oximeters, with eight parents of nine adults with ID in Scotland. Two eligible parents also completed a further 2 weeks of monitoring, and returned an evaluation questionnaire on its feasibility. RESULTS: Baseline mean readings for eight adults with ID were within the normal range (%Sp02 ≥ 95), and for another one 94%. Fluctuations over the 3-day assessment period were experienced by six of these individuals. However, these variations were within limits which are not dangerous (lowest reading 92%), implying that parental home-based pulse oximetry monitoring is likely to be safe for adults with ID. The two parents who completed the evaluation found home-based pulse oximetry monitoring to be easy/very easy to do, and effective/very effective. CONCLUSIONS: This is the first research study, albeit with a very small sample, to report on the potential feasibility of parental home-based pulse oximetry monitoring for adults with ID. Home-based pulse oximetry monitoring appears to be safe in adults with ID at risk of developing serious respiratory problems, and not difficult for their parents to do. There is an urgent need to replicate this work, using a larger sample, to promote home-based respiratory health monitoring more widely for people with ID.
Subject(s)
COVID-19 , Intellectual Disability , Humans , Adult , Intellectual Disability/diagnosis , Pandemics , Oximetry , OxygenABSTRACT
The entire world witnessed the covid-19pandemicinthe year 2020. The actual outbreak of this corona virus was first reported in Wuhan, China and later declared to be epidemic by (WHO) World Health Organization. The whole world was under tremendous pressure in monitoring health, managing, and maintaining hospitals and inventing new drugs. Initially, India was very much worried because of the huge population. The pandemic posed a critical challenge for healthcare sectors, since doctors and nursing professionals were among the most severely affected and it's clear that India must adopt new measures to increase healthcare proportional ratio and adoption of new technologies to manage large population groups. Robotics is one area which may largely always support the segment. The proposed research project emphasized on developing robotic devices with robotic vision, sensors-based motion planning, dynamic obstacle detection, and autonomous navigation in a hospital environment and supported the medical and nursing teams in reducing their workload and improving patient health monitoring, also the research explored multi-robot exploration and integration. © 2022 IEEE.
ABSTRACT
This paper presents an IoT-based heart monitoring system using 8266 NodeMCU. According to the Malaysian Department of Statistics, ischemic heart disease is the leading cause of death, accounting for 15.0% of the 109,164 medically certified deaths in 2019. The coronary heart is a vital organ that pumps oxygen and blood across the body. Meanwhile, if the heart is not getting sufficient oxygen, the patient will experience chest pain, typically on the left side of the body, which can be mistaken for a heart problem. During the Covid-19 pandemic, a patient cannot attend regular treatment at the hospital as it is operating at full capacity. During this phase, the hospital can only focus on the critical and high-risk patient. The proposed heart monitoring system monitors the patient by measuring the heart rate and oxygen level in the comforts of home. Therefore, the patient can provide his current health record for the doctor's evaluation. The idea behind this proposed system is to construct an IOT-based system that automatically monitors the health condition in terms of heartbeat and oxygen detection. The prototype provides data to the Blynk for the patient and the I-Heart web-based application for the medical practitioner. © 2022 IEEE.
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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.
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ZigBee technology is preferably been used for health monitoring as it consumes very less power, high reliability, and low expenses. In this paper, mobile-based medical alert system for COVID-19 detection system using ZigBee technology is proposed. The health report of the user will be sent to the caretaker or doctor via cloud computing network so that they can analyze the problem. The real-time monitoring of health temperature and symptoms of COVID-19 and data transmission via remote sensing is also realized. © 2022 Scrivener Publishing LLC.
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Due to the outbreak of the COVID-19 novel coronavirus, the restrictions on population entry and exit have resulted in most elderly people staying at home alone, causing them a lot of inconvenience. Aiming at the problem that the elderly living alone at home may cause various diseases due to negative emotions but cannot be detected and solved in time, a method of detecting the facial expressions of the elderly is proposed to determine whether the elderly need timely care. YOLOX is the latest generation of YOLO series target detectors released by Megvii Technology in July 2021. It adopts the latest technology in the industry in recent years and surpasses existing similar products in performance and accuracy. If the YOLOX detector can be applied to the health monitoring of the elderly living alone under the current epidemic situation, it will be of great significance to improve detection rate and accuracy of detection and reduce labor costs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.