Adopting MQTT for a multi protocols IoMT system

<span>Coronavirus disease (COVID-19) altered the way of caregiving and the new pandemic forced the health systems to adopt new treatment protocols in which remote follow-up is essential. This paper introduces a proposed system to link a remote healthcare unit as it is inside the hospital. Two different network protocols; a global system for mobile communication (GSM) and Wi-Fi were used to simulate the heath data transfer from the two different geographical locations, using Raspberry Pi development board and Microcontroller units. Message queuing telemetry transport (MQTT) protocol was employed to transfer the measured data from the healthcare unit to the hospital’s Gateway. The gateway is used to route the aggregated health data from healthcare units to the hospital server, doctors’ dashboards, and the further processing. The system was successfully implemented and tested, where the experimental tests show that the remote healthcare units using a GSM network consumed about 900 mWh. A high percentage of success data packets transfer was recorded within the network framework as it reaches 99.89% with an average round trip time (RTT) of 7.5 milliseconds and a data transfer rate up to 12.3 kbps.</span>

A Secure Three Factor Based Authentication Scheme for Telecare Medicine Information System with Privacy Preservation

Telecare Medicine Information System (TMIS) is now attracting field for remote healthcare, diagnosis and emergency health services etc. The major objective of this type of system is to provide medical facilities to patients who are critically ill and unable to attend hospitals or put in isolation for observations. A major challenge of such systems is to securely transmit patients' health related information to the medical server through an insecure channel. This collected sensitive data is further used by medical practitioners for diagnosis and treatment purposes. Therefore, security and privacy are essential for healthcare data. In this paper, a robust authentication protocol based on Chebyshev Chaotic map has been proposed for adequate security while transmitting data. The privacy preservation is maintained by a rule set which mainly controls the views. A detailed security analysis was performed for the proposed scheme.

Advances in Medical Wearable Biosensors: Design, Fabrication and Materials Strategies in Healthcare Monitoring

In the past decade, wearable biosensors have radically changed our outlook on contemporary medical healthcare monitoring systems. These smart, multiplexed devices allow us to quantify dynamic biological signals in real time through highly sensitive, miniaturized sensing platforms, thereby decentralizing the concept of regular clinical check-ups and diagnosis towards more versatile, remote, and personalized healthcare monitoring. This paradigm shift in healthcare delivery can be attributed to the development of nanomaterials and improvements made to non-invasive biosignal detection systems alongside integrated approaches for multifaceted data acquisition and interpretation. The discovery of new biomarkers and the use of bioaffinity recognition elements like aptamers and peptide arrays combined with the use of newly developed, flexible, and conductive materials that interact with skin surfaces has led to the widespread application of biosensors in the biomedical field. This review focuses on the recent advances made in wearable technology for remote healthcare monitoring. It classifies their development and application in terms of electrochemical, mechanical, and optical modes of transduction and type of material used and discusses the shortcomings accompanying their large-scale fabrication and commercialization. A brief note on the most widely used materials and their improvements in wearable sensor development is outlined along with instructions for the future of medical wearables.

Caregiving During COVID-19: A Multi-State Qualitative Study of Family Caregiver Experiences and Decision Making

COVID-19 poses unique challenges to family caregivers. This study explores how family caregivers for older adults with cognitive impairments experience and make decisions about caregiving during a global pandemic. Using purposive sampling, 63 family caregivers across eight states participated in open-ended qualitative interviews (2019-2020), until thematic saturation was reached. Questions broadly examined caregivers’ experiences and decisions, focusing on decisions made around type of care setting. Questions about responses to the Pandemic were added as events unfolded. States were selected to represent variation in Home and Community Based Service (HCBS) expenditures as a percentage of total Medicaid long-term services and supports expenditures. Family caregivers experienced significant concern about COVID-19 itself, and about the indirect consequences of caregiving caused by the pandemic. Caregivers also displayed flexibility and adaptability in ceasing selected services, contingently continuing services, and utilizing telemedicine and other remote healthcare interventions to protect their loved ones. Many family caregivers utilized remote health care tools such telemedicine, no-contact prescription and grocery delivery. Such measures improved service access and reduced caregiver workload. Given the persistent challenges posed by COVID-19, long-term service organizations have an opportunity to enhance their policies to meet the needs of caregivers and those they care for. There is a need to expand telemedicine and other remote healthcare tools, while adapting these technologies to the needs of families. Also, procedures are needed for safe pathways to utilize HCBS and nursing care during a pandemic including communication supports, sufficient PPE, increased staffing, and utilization of evidence-based protocols.

Improving Patient Outreach by Defining Telehealth Suitability in a Tertiary Huntington’s Disease Clinic

Background: The COVID-19 pandemic has increased the need for remote healthcare options among patients with Huntington’s disease (HD). However, since not every HD patient is suitable for telehealth, it is important to differentiate who can be seen virtually from who should remain as in-person. Unfortunately, there are no clinical guidelines on how to evaluate HD patients for telehealth eligibility. Objective: To standardize the teleneurology selection process in HD by implementing a screening tool that accounts for patient-specific factors. Methods: We organized various indications and contraindications to teleneurology into a flowchart. If any indications or contraindications were met, patients were assigned to telehealth or maintained as in-person, respectively. If no indications or contraindications were met, patients were given the option of telehealth or in-person for their upcoming appointments. In two implementation cycles, we tested this screening tool among all HD patients scheduled for clinic visits, aided by chart review and phone interview. Results: In a cohort of 81 patients, telehealth acceptance among eligible patients increased from 45.0%to 83.3%. Frequency of telehealth visits increased from a pre-intervention baseline of 12.8%to 28.2%. Conclusion: Teleneurology utilization among HD patients more than doubled across our study. Our intervention promotes consistency and patient-centeredness in HD clinical care and streamlines the overall telehealth selection process. Future studies can seek to reduce telehealth no-shows and also evaluate the utility of the motor and psychiatric criteria included in our screening tool.

Remote Gait Type Classification System Using Markerless 2D Video

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.