Remote Healthcare Solutions Research Articles

Non-Terrestrial Networks for Energy-Efficient Connectivity of Remote IoT Devices in the 6G Era: A Survey.

The Internet of Things (IoT) is gaining popularity and market share, driven by its ability to connect devices and systems that were previously siloed, enabling new applications and services in a cost-efficient manner. Thus, the IoT fuels societal transformation and enables groundbreaking innovations like autonomous transport, robotic assistance, and remote healthcare solutions. However, when considering the Internet of Remote Things (IoRT), which refers to the expansion of IoT in remote and geographically isolated areas where neither terrestrial nor cellular networks are available, internet connectivity becomes a challenging issue. Non-Terrestrial Networks (NTNs) are increasingly gaining popularity as a solution to provide connectivity in remote areas due to the growing integration of satellites and Unmanned Aerial Vehicles (UAVs) with cellular networks. In this survey, we provide the technological framework for NTNs and Remote IoT, followed by a classification of the most recent scientific research on NTN-based IoRT systems. Therefore, we provide a comprehensive overview of the current state of research in IoRT and identify emerging research areas with high potential. In conclusion, we present and discuss 3GPP's roadmap for NTN standardization, which aims to establish an energy-efficient IoRT environment in the 6G era.

Snakebite envenomation: A comprehensive evaluation of severity, treatment, and outcomes in 100 patients correlating timing of ASV administration with complications

This study, conducted at the New Civil Hospital in Surat from June 2004 to November 2006, explored the clinical dynamics, complications, and outcomes of 100 cases of poisonous snakebites. The age group most affected was 21–40 years (58%), reflective of heightened outdoor activities. Males, often primary earners, exhibited a higher incidence, while rural areas dominated (75%) due to favorable snake habitats. Notable patterns emerged, such as a significant occurrence of bites between 6:00 p.m. and 5:59 a.m. attributed to nocturnal snake activity and rural sleeping habits. The monsoon season experienced a peak in snakebites (79%) as snakes sought refuge in human habitats. Intriguingly, 45% of patients lacked visible fang marks, challenging their reliability for identifying venomous bites. Neurotoxicity prevailed (54%), with local pain and swelling prominent. Effective treatment resulted in a 90% cure rate, highlighting the significance of early diagnosis. Respiratory failure, hypotension, and combined manifestations indicated poor prognosis, underscoring the critical need for early recognition. Mechanical ventilation notably reduced mortality in neurotoxic cases. Delayed admission correlated with heightened severity, complications, and poorer outcomes, emphasizing the urgency of early intervention. Severity grades 3 and 4 exhibited elevated mortality rates, calling for urgent and meticulous management. The duration of venom in the bloodstream before antivenom administration directly impacted severity and outcomes. Future research avenues include species-specific considerations, antivenom advancements, community education, remote healthcare solutions, and long-term follow-up. Limitations encompass single-center data, incomplete records, temporal constraints, limited species identification, and a need to delve into social and cultural factors influencing snakebites.

Shared Leadership and Sociomateriality in Design Projects: Integrating Objects and Values

Shared leadership has gained momentum in fields ranging from education and healthcare to information technology and sports. Given its collaborative or participatory nature, design practice should be a fertile ground for the emergence of shared leadership. Yet, previous literature on design management and leadership has primarily conceptualized leadership as an individual characteristic. To extend the existing literature, this article presents an autoethnographic exploration of shared leadership from a sociomaterial perspective. It focuses on a design project dealing with remote healthcare solutions and contributes to existing literature on shared leadership and sociomateriality. I clarify how values, individuals, and objects interact over time to evolve, stabilize, and destabilize shared leadership. This finding calls for a more granular understanding of objects. This suggests that objects influence shared leadership not just through their form and development but also by imbuing and reinforcing certain values. The findings also highlight the need to regard design leadership as sociomaterial configurations rather than solely as an individual characteristic. The article concludes by proposing further research avenues and discussing implications for design practice.

INVESTIGATING OLDER ADULTS’ PERSPECTIVES ON TELEHEALTH ROBOTICS

Abstract The current social distancing directives have heightened the need for older adults to seek remote healthcare solutions. Telehealth robots could support home health by carrying out on-demand tasks for diagnosis and treatment at the user’s home through remote teleoperation by the healthcare provider. Acceptability of such telehealth robots by older adults is critical to their successful deployment. The overall goal of this research was to investigate older adults’ perceptions and attitudes towards a telehealth robot supporting health checkups at home. Specific objectives were to explore potential healthcare use cases for the robot; identify facilitators and barriers to its use; and elicit design requirements for use in healthcare contexts. We interviewed 5 men and 5 women (ages 66–73) after they viewed a series of videos demonstrating the potential uses of the telehealth robot. The older adults had positive first impressions towards the telehealth robot and were generally open to the idea of using it for telehealth tasks. They conveyed a high level of trust with the robot, especially if it were controlled by a healthcare provider, yet expressed concerns with privacy and security of health information that would need to be addressed in the design of security protocols. They described added value of the robot’s healthcare support while suggesting potential improvements to the robot. This research provided insights into older adults’ perceptions and attitudes towards a telehealth robot as well as identified potential healthcare use cases that would inform the design requirements for telehealth robots in different home healthcare contexts.

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19.

Understanding IoHT and Edge/Fog Computing Solutions for Smart In-Home Remote Healthcare

Healthcare information systems have been dominated by cloud technology and the Internet of Things (IoT) for decades today. In some urgent scenarios, we reveal a prevailing architectural framework that is based on fog/edge optimal computing approaches smart in-home remote healthcare solutions and architectures and recognize the challenges and requirements of IoHT devices for diverse utilization instances. Even with these upsides, conventional centralized access constraint confronts privacy problems and patient health data security. This study likewise constructs a “blockchain-enabled edge that computes” mechanism, through which smart contracts with the consensus protocol produced by Edge Intelligent Server are deployed to secure privacy topics and balance scalability in trustless surroundings. We expect this paper to be a significant guideline for the subsequent elaboration of fog/edge-based systems that compute solutions for smart in-home remote healthcare IoT applications. There will be a change of paradigm from “hospital-based” to “distributed patient in-home healthcare”.

Millimeter‐wave planar antennas for 5G‐based remote monitoring and treatment of COVID‐19 patients

AbstractSpreading of COVID‐19 intensely affected the social life, economics, and health care system globally. Remote treatment of COVID‐19 is required to prevent healthcare professionals from being infected and solve the shortage of health resources which is generated due to large number of COVID patients. Tele‐health services require low latency, high data rate connections between various sensors, and Internet of Things devices with reliable and uninterrupted networks, which can support massive number of devices. 5G‐based remote health care solutions are provided by various researchers to solve the aforementioned problems. Various technologies such as millimeter‐wave (mm‐wave), beamforming, and multiple‐input‐multiple‐output (MIMO) are used to fulfill the requirements. mm‐Wave spectrum provides wide available bandwidth, high speed, low latency, and compact antenna size but range of communication is limited due to high link losses and environmental attenuation. High‐gain, narrow beam‐steerable radiation properties of antenna are required to mitigate these losses. This article first presents the design of 16 elements mm‐Wave beam‐steerable planar array for 5G‐based COVID‐19 tracking and health care management. The proposed antenna provides 1.83 GHz of bandwidth with gain of 19.3 dB and side‐lobe levels of less than −24 dB at 28.67 GHz. Narrow main beam having 0.9° beamwidth can be steered from −50° to +45° with less than 5 dB reduction in gain. MIMO configurations of the proposed antenna are also discussed. MIMO performance parameters are under acceptable limit. The proposed antenna is potential cost efficient solution for 5G‐based COVID‐19 tracking and treatment applications.

Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application.

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

Interactive telemedicine solution based on a secure mHealth application.

In dynamic healthcare environments, caregivers and patients are constantly moving. To increase the healthcare quality when it is necessary, caregivers need the ability to reach each other and securely access medical information and services from wherever they happened to be. This paper presents an Interactive Telemedicine Solution (ITS) to facilitate and automate the communication within a healthcare facility via Voice over Internet Protocol (VOIP), regular mobile phones, and Wi-Fi connectivity. Our system has the capability to exchange/provide securely healthcare information/services across geographic barriers through 3G/4G wireless communication network. Our system assumes the availability of an Electronic Health Record (EHR) system locally in the healthcare organization and/or on the cloud network such as a nation-wide EHR system. This paper demonstrate the potential of our system to provide effectively and securely remote healthcare solution.

EHealth Service Support in Future IPv6 Vehicular Networks

Recent vehicular networking activities include novel automotive applications, such as public vehicle to vehicle/infrastructure (V2X), large scale deployments, machine-to-machine (M2M) integration scenarios, and more. The platform described in this paper focuses on the integration of eHealth in a V2I setting. This is to allow the use of Internet from a vehicular setting to disseminate health-related information. From an eHealth viewpoint, the use of remote healthcare solutions to record and transmit a patient’s vital signs is a special telemedicine application that helps hospital resident health professionals to optimally prepare the patient’s admittance. From the automotive perspective, this is a typical vehicle-to-infrastructure (V2I) communication scenario. This proposal provides an IPv6 vehicular platform, which integrates eHealth devices and allows sending captured health-related data to a personal health record (PHR) application server in the IPv6 Internet. The collected data is viewed remotely by a doctor and supports his diagnostic decision. In particular, our work introduces the integration of vehicular and eHealth testbeds, describes related work and presents a lightweight auto-configuration method based on a DHCPv6 extension to provide IPv6 connectivity with a few numbers of messages.