BASS: Safe Deep Tissue Optical Sensing for Wearable Embedded Systems

In wearable optical sensing applications whose target tissue is not superficial, such as deep tissue oximetry, the task of embedded system design has to strike a balance between two competing factors. On one hand, the sensing task is assisted by increasing the radiated energy into the body, which in turn, improves the signal-to-noise ratio (SNR) of the deep tissue at the sensor. On the other hand, patient safety consideration imposes a constraint on the amount of radiated energy into the body. In this paper, we study the trade-offs between the two factors by exploring the design space of the light source activation pulse. Furthermore, we propose BASS, an algorithm that leverages the activation pulse design space exploration, which further optimizes deep tissue SNR via spectral averaging, while ensuring the radiated energy into the body meets a safe upper bound. The effectiveness of the proposed technique is demonstrated via analytical derivations, simulations, and in vivo measurements in both pregnant sheep models and human subjects.

Metaverse: Evolving role in healthcare delivery and implications

Metaverse, or the immersive internet, is touted as the next considerable technology-led disruption on the horizon, which can potentially disrupt clinician-patient interactions, patient experience, innovation and research and development processes. Metaverse is still in the evolution phase, and its firm definition framework is a work in progress. Surgical training allows the trainees to mimic procedures and simulate cross-collaborations within time and space, shortening the learning cycles. Similarly, patient experiences can be built by creating unique experiences replicating the real world without constraints. At the same time, the care providers can foster greater empathy while serving the patients with regular or special specific needs per disease conditions.

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Impact of Using the Internet of Medical Things on e-Healthcare Performance: Blockchain Assist in Improving Smart Contract

Background

This paper explores the use of blockchain technology and smart contracts in the Internet of Medical Things (IoMT). It aims to identify the challenges and benefits of implementing smart contracts based on blockchain technology in the IoMT. It provides solutions and evaluates the IoMT uses in e-healthcare performance.

Methods

A quantitative approach used an online survey from public and private hospital administrative departments in Dubai, United Arab Emirates (UAE). ANOVA, t-test, correlation, and regression analysis were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).

Patients and Methods

A mixed method was used in this research, a quantitative approach for data analysis utilizing online surveys from public and private hospitals' administrative departments in Dubai, UAE. A correlation, regression through ANOVA, and independent two-sample t-test were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).

Results

Blockchain application in smart contracts has proven to be significant in the healthcare sector. Results highlight the importance of integrating smart contracts and blockchain technology in the IoMT infrastructure to improve efficiency, transparency, and security. The study provides empirical evidence to support the implementation of smart contracts in the e-healthcare sector and suggests improved e-healthcare performance through this transition.

Conclusion

The emergence of e-healthcare systems with upgraded smart contracts and blockchain technology brings continuous health monitoring, time-effective operations, and cost-effectiveness to the healthcare sector.

Tailoring the Electron Trapping Effect of a Biocompatible Triboelectric Hydrogel by Graphene Oxide Incorporation towards Self-Powered Medical Electronics

Triboelectric nanogenerators (TENGs) are associated with several drawbacks that limit their application in the biomedical field, including toxicity, thrombogenicity, and poor performance in the presence of fluids. By proposing the use of a hemo/biocompatible hydrogel, poly(2-hydroxyethyl methacrylate) (pHEMA), this study bypasses these barriers. In contact-separation mode, using polytetrafluoroethylene (PTFE) as a reference, pHEMA generates an output of 100.0 V, under an open circuit, 4.7 μA, and 0.68 W/m² for an internal resistance of 10 MΩ. Our findings unveil that graphene oxide (GO) can be used to tune pHEMA's triboelectric properties in a concentration-dependent manner. At the lowest measured concentration (0.2% GO), the generated outputs increase to 194.5 V, 5.3 μA, and 1.28 W/m² due to the observed increase in pHEMA's surface roughness, which expands the contact area. Triboelectric performance starts to decrease as GO concentration increases, plateauing at 11% volumetric, where the output is 51 V, 1.76 μA, and 0.17 W/m² less than pHEMA's. Increases in internal resistance, from 14 ΩM to greater than 470 ΩM, ζ-potential, from -7.3 to -0.4 mV, and open-circuit characteristic charge decay periods, from 90 to 120 ms, are all observed in conjunction with this phenomenon, which points to GO function as an electron trapping site in pHEMA's matrix. All of the composites can charge a 10 μF capacitor in 200 s, producing a voltage between 0.25 and 3.5 V and allowing the operation of at least 20 LEDs. The triboelectric output was largely steady throughout the 3.33 h durability test. Voltage decreases by 38% due to contact-separation frequency, whereas current increases by 77%. In terms of pressure, it appears to have little effect on voltage but boosts current output by 42%. Finally, pHEMA and pHEMA/GO extracts were cytocompatible toward fibroblasts. According to these results, pHEMA has a significant potential to function as a biomaterial to create bio/hemocompatible TENGs and GO to precisely control its triboelectric outputs.

Model-Driven Dementia Prevention and Intervention Platform

Most types of dementia, including Alzheimer's disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems).

Octopus: A Novel Approach for Health Data Masking and Retrieving Using Physical Unclonable Functions and Machine Learning

Health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because devices are now linked to high-speed internet and mobile phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this paper, Octopus and Physically Unclonable Functions (PUFs) are used to provide privacy to the healthcare device by masking the data, and machine learning (ML) techniques are used to retrieve the health data back and reduce security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.

Swarm Intelligence in Internet of Medical Things: A Review

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

Prioritization-Driven Congestion Control in Networks for the Internet of Medical Things: A Cross-Layer Proposal

Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices' transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet's network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency.

Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform

The aim of this study was to introduce the implemented MEDBIZ platform based on the internet of medical things (IoMT) supporting real-time digital health services for precision medicine. In addition, we demonstrated four empirical studies of the digital health ecosystem that could provide real-time healthcare services based on IoMT using real-world data from in-hospital and out-hospital patients. Implemented MEDBIZ platform based on the IoMT devices and big data to provide digital healthcare services to the enterprise and users. The big data platform is consisting of four main components: IoMT, core, analytics, and services. Among the implemented MEDBIZ platform, we performed four clinical trials that designed monitoring services related to chronic obstructive pulmonary disease, metabolic syndrome, arrhythmia, and diabetes mellitus. Of the four empirical studies on monitoring services, two had been completed and the rest were still in progress. In the metabolic syndrome monitoring service, two studies were reported. One was reported that intervention components, especially wearable devices and mobile apps, made systolic blood pressure, diastolic blood pressure, waist circumference, and glycosylated hemoglobin decrease after 6 months. Another one was presented that increasing high-density lipoprotein cholesterol and triglyceride levels were prevented in participants with the pre-metabolic syndrome. Also, self-care using healthcare devices might help prevent and manage metabolic syndrome. In the arrhythmia monitoring service, during the real-time monitoring of vital signs remotely at the monitoring center, 318 (15.9%) general hikers found abnormal signals, and 296 (93.1%) people were recommended for treatment. We demonstrated the implemented MEDBIZ platform based on IoMT supporting digital healthcare services by acquiring real-world data for getting real-world evidence. And then through this platform, we were developing software as a medical device, digital therapeutics, and digital healthcare services, and contributing to the development of the digital health ecosystem.

Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review

Bioelectrical impedance analysis and bioelectrical impedance spectroscopy (BIA/BIS) of tissues reveal important information on molecular composition and physical structure that is useful in diagnostics and prognostics. The heterogeneity in structural elements of cells, tissues, organs, and the whole human body, the variability in molecular composition arising from the dynamics of biochemical reactions, and the contributions of inherently electroresponsive components, such as ions, proteins, and polarized membranes, have rendered bioimpedance challenging to interpret but also a powerful evaluation and monitoring technique in biomedicine. BIA/BIS has thus become the basis for a wide range of diagnostic and monitoring systems such as plethysmography and tomography. The use of BIA/BIS arises from (i) being a noninvasive and safe measurement modality, (ii) its ease of miniaturization, and (iii) multiple technological formats for its biomedical implementation. Considering the dependency of the absolute and relative values of impedance on frequency, and the uniqueness of the origins of the α-, β-, δ-, and γ-dispersions, this targeted review discusses biological events and underlying principles that are employed to analyze the impedance data based on the frequency range. The emergence of BIA/BIS in wearable devices and its relevance to the Internet of Medical Things (IoMT) are introduced and discussed.

An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications

This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications. Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases. Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research. The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail.

A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students' Cardiac Signal and MSY

The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory.

Triboelectric and Piezoelectric Nanogenerators for Self-Powered Healthcare Monitoring Devices: Operating Principles, Challenges, and Perspectives

The internet of medical things (IoMT) is used for the acquisition, processing, transmission, and storage of medical data of patients. The medical information of each patient can be monitored by hospitals, family members, or medical centers, providing real-time data on the health condition of patients. However, the IoMT requires monitoring healthcare devices with features such as being lightweight, having a long lifetime, wearability, flexibility, safe behavior, and a stable electrical performance. For the continuous monitoring of the medical signals of patients, these devices need energy sources with a long lifetime and stable response. For this challenge, conventional batteries have disadvantages due to their limited-service time, considerable weight, and toxic materials. A replacement alternative to conventional batteries can be achieved for piezoelectric and triboelectric nanogenerators. These nanogenerators can convert green energy from various environmental sources (e.g., biomechanical energy, wind, and mechanical vibrations) into electrical energy. Generally, these nanogenerators have simple transduction mechanisms, uncomplicated manufacturing processes, are lightweight, have a long lifetime, and provide high output electrical performance. Thus, the piezoelectric and triboelectric nanogenerators could power future medical devices that monitor and process vital signs of patients. Herein, we review the working principle, materials, fabrication processes, and signal processing components of piezoelectric and triboelectric nanogenerators with potential medical applications. In addition, we discuss the main components and output electrical performance of various nanogenerators applied to the medical sector. Finally, the challenges and perspectives of the design, materials and fabrication process, signal processing, and reliability of nanogenerators are included.

The SSTeP-KiZ System-Secure Real-Time Communication Based on Open Web Standards for Multimodal Sensor-Assisted Tele-Psychotherapy

In this manuscript, we describe the soft- and hardware architecture as well as the implementation of a modern Internet of Medical Things (IoMT) system for sensor-assisted telepsychotherapy. It enables telepsychotherapy sessions in which the patient exercises therapy-relevant behaviors in their home environment under the remote supervision of the therapist. Wearable sensor information (electrocardiogram (ECG), movement sensors, and eye tracking) is streamed in real time to the therapist to deliver objective information about specific behavior-triggering situations and the stress level of the patients. We describe the IT infrastructure of the system which uses open standards such as WebRTC and OpenID Connect (OIDC). We also describe the system's security concept, its container-based deployment, and demonstrate performance analyses. The system is used in the ongoing study SSTeP-KiZ (smart sensor technology in telepsychotherapy for children and adolescents with obsessive-compulsive disorder) and shows sufficient technical performance.

Internet of medical things-enabled CRISPR diagnostics for rapid detection of SARS-CoV-2 variants of concern

Previous studies have highlighted CRISPR-based nucleic acid detection as rapid and sensitive diagnostic methods for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we reported an optimized CRISPR-Cas12a diagnostic platform for the safe and rapid detection of SARS-CoV-2 variants of concern (VOCs). This platform, which was referred to as CALIBURN-v2, could complete the diagnosis on extracted RNA samples within 25 min in a closed-lid reaction mode and had 100-fold increase in detection sensitivity in comparison with previous platforms. Most importantly, by integrating a portable device and smartphone user interface, CALIBURN-v2 allowed for cloud server-based data collection and management, thus transforming the point-of-care testing (POCT) platform to internet of medical things (IoMT) applications. It was found that IoMT-enabled CALIBURN-v2 could achieve 95.56% (172 out of 180) sensitivity for SARS-CoV-2 wild type and 94.38% (84 out of 89) overall sensitivity for SARS-CoV-2 variants including Delta and Omicron strains. Therefore, our study provides a feasible approach for IoMT-enabled CRISPR diagnostics for the detection of SARS-CoV-2 VOCs.

A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.

Development of a Low-Power IoMT Portable Pillbox for Medication Adherence Improvement and Remote Treatment Adjustment

Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. Crucial drug-drug, food-drug, and supplement-drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment.

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis

Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.

A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things

Rapid advancements in the internet of things (IoT) are driving massive transformations of health care, which is one of the largest and critical global industries. Recent pandemics, such as coronavirus 2019 (COVID-19), include increasing demands for ubiquitous, preventive, and personalized health care to be provided to the public at reduced risks and costs with rapid care. Mobile crowdsourcing could potentially meet the future massive health care IoT (mH-IoT) demands by enabling anytime, anywhere sense and analyses of health-related data to tackle such a pandemic situation. However, data reliability and availability are among the many challenges for the realization of next-generation mH-IoT, especially in COVID-19 epidemics. Therefore, more intelligent and robust health care frameworks are required to tackle such pandemics. Recently, reinforcement learning (RL) has proven its strengths to provide intelligent data reliability and availability. The action-state learning procedure of RL-based frameworks enables the learning system to enhance the optimal use of the information as the time passes and data increases. In this article, we propose an RL-based crowd-to-machine (RLC2M) framework for mH-IoT, which leverages crowdsourcing and an RL model (Q-learning) to address the health care information processing challenges. The simulation results show that the proposed framework rapidly converges with accumulated rewards to reveal the sensing environment situation.

Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers

Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions-either leukemias or healthy-utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work.

Ethics for integrating emerging technologies to contain COVID-19 in Zimbabwe

Zimbabwe is among the countries affected with the coronavirus disease (COVID-19) and implemented several infection control and measures such as social distancing, contact tracing, regular temperature checking in strategic entry and exit points, face masking among others. The country also implemented recursive national lockdowns and curfews to reduce the virus transmission rate and its catastrophic impact. These large-scale measures are not easy to implement, adhere to and subsequently difficult to practice and maintain which lead to imperfect public compliance, especially if there is a significant impact on social and political norms, economy, and psychological wellbeing of the affected population. Also, emerging COVID-19 variants, porous borders, regular movement of informal traders and sale of fake vaccination certificates continue to threaten impressive progress made towards virus containment. Therefore, several emerging technologies have been adopted to strengthen the health system and health services delivery, improve compliance, adherence and maintain social distancing. These technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation, and quarantine compliance. However, the use of emerging technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which impact their efficacy, adoption and ultimately influence public trust. Therefore, the study proposes ethical framework for using emerging technologies to contain the COVID-19 pandemic. The framework is centered on ethical practices such as security, privacy, justice, human dignity, autonomy, solidarity, beneficence, and non-maleficence.

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

Sensors in the Autoclave-Modelling and Implementation of the IoT Steam Sterilization Procedure Counter

Surgical procedures involve major risks, as pathogens can enter the body unhindered. To prevent this, most surgical instruments and implants are sterilized. However, ensuring that this process is carried out safely and according to the normative requirements is not a trivial task. This study aims to develop a sensor system that can automatically detect successful steam sterilization on the basis of the measured temperature profiles. This can be achieved only when the relationship between the temperature on the surface of the tool and the temperature at the measurement point inside the tool is known. To find this relationship, the thermodynamic model of the system has been developed. Simulated results of thermal simulations were compared with the acquired temperature profiles to verify the correctness of the model. Simulated temperature profiles are in accordance with the measured temperature profiles, thus the developed model can be used in the process of further development of the system as well as for the development of algorithms for automated evaluation of the sterilization process. Although the developed sensor system proved that the detection of sterilization cycles can be automated, further studies that address the possibility of optimization of the system in terms of geometrical dimensions, used materials, and processing algorithms will be of significant importance for the potential commercialization of the presented solution.

Framework for ethical and acceptable use of social distancing tools and smart devices during COVID-19 pandemic in Zimbabwe

Despite the successful development of vaccines, coronavirus disease (COVID-19) continues to present unprecedented challenges. Besides the ongoing vaccination activities, many countries still rely on measures including social distancing, contact tracing, mandatory face masking among others. Several digital technologies such as smart devices, social distancing tools, smart applications have been adopted to enhance public adherence to reduce secondary transmission. Such technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self-isolation and quarantine compliance. The use of digital technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over-tracking, over-surveillance and lack of proper development and implementation guidelines which subsequently impact their efficacy and adoption. Also, the aggressive and mandatory use of large-scale digital technologies is not easy to implement, adhere to and subsequently difficult to practice which ultimately lead to imperfect public compliance. To alleviate these impediments, we analysed the available literature and propose an ethical framework for the use of digital technologies centred on ethical practices. The proposed framework highlights the trade-offs, potential roles and coordination of different stakeholders involved in the development and implementation of digital technologies, from various social and political contexts in Zimbabwe. We suggest that transparency, regular engagement and participation of potential users are likely to boost public trust. However, the potential violation of ethical values, poor communication, hasty implementation of digital technologies will likely undermine public trust, and as such, risk their adoption and efficacy.

A Study on CP-ABE-based Medical Data Sharing System with Key Abuse Prevention and Verifiable Outsourcing in the IoMT Environment

Recent developments in cloud computing allow data to be securely shared between users. This can be used to improve the quality of life of patients and medical staff in the Internet of Medical Things (IoMT) environment. However, in the IoMT cloud environment, there are various security threats to the patient's medical data. As a result, security features such as encryption of collected data and access control by legitimate users are essential. Many studies have been conducted on access control techniques using ciphertext-policy attribute-based encryption (CP-ABE), a form of attribute-based encryption, among various security technologies and studies are underway to apply them to the medical field. However, several problems persist. First, as the secret key does not identify the user, the user may maliciously distribute the secret key and such users cannot be tracked. Second, Attribute-Based Encryption (ABE) increases the size of the ciphertext depending on the number of attributes specified. This wastes cloud storage, and computational times are high when users decrypt. Such users must employ outsourcing servers. Third, a verification process is needed to prove that the results computed on the outsourcing server are properly computed. This paper focuses on the IoMT environment for a study of a CP-ABE-based medical data sharing system with key abuse prevention and verifiable outsourcing in a cloud environment. The proposed scheme can protect the privacy of user data stored in a cloud environment in the IoMT field, and if there is a problem with the secret key delegated by the user, it can trace a user who first delegated the key. This can prevent the key abuse problem. In addition, this scheme reduces the user's burden when decoding ciphertext and calculates accurate results through a server that supports constant-sized ciphertext output and verifiable outsourcing technology. The goal of this paper is to propose a system that enables patients and medical staff to share medical data safely and efficiently in an IoMT environment.

Security in IoMT Communications: A Survey

The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing

This study aims to explore the impact of healthcare digitalization on smart hospital project financing (PF) fostered by pay-for-performance (P4P) incentives. Digital platforms are a technology-enabled business model that facilitates exchanges between interacting agents. They represent a bridging link among disconnected nodes, improving the scalable value of networks. Application to healthcare public-private partnerships (PPPs) is significant due to the consistency of digital platforms with health issues and the complexity of the stakeholder's interaction. In infrastructural PPPs, public and private players cooperate, usually following PF patterns. This relationship is complemented by digitized supply chains and is increasingly patient-centric. This paper reviews the literature, analyzes some supply chain bottlenecks, addresses solutions concerning the networking effects of platforms to improve PPP interactions, and investigates the cost-benefit analysis of digital health with an empirical case. Whereas diagnostic or infrastructural technology is an expensive investment with long-term payback, leapfrogging digital applications reduce contingent costs. "Digital" savings can be shared by key stakeholders with P4P schemes, incentivizing value co-creation patterns. Efficient sharing may apply network theory to a comprehensive PPP ecosystem where stakeholding nodes are digitally connected. This innovative approach improves stakeholder relationships, which are re-engineered around digital platforms that enhance patient-centered satisfaction and sustainability. Digital technologies are useful even for infectious disease surveillance, like that of the coronavirus pandemic, for supporting massive healthcare intervention, decongesting hospitals, and providing timely big data.