A Scoping Review of Virtual Focus Group Methods Used in Rehabilitation Sciences

Virtual methods for conducting focus group studies are increasingly being used in many fields, including rehabilitation sciences. This is partly due to the current pandemic, and the need for social distancing, however, may also relate to factors such as convenience and practicality. Virtual research methods enable investigators to collect data at a distance from the participant(s) through the use of technology-mediated data collection methods incorporating new tools and technologies. The aim of this scoping review was to identify, synthesize, and present current evidence related to the methods for conducting virtual focus groups. A comparison of asynchronous and synchronous data collection methods was conducted. The objectives, inclusion criteria, and scoping review methods were specified in advance and documented in a protocol. The 40 articles in this review included virtual focus group research conducted in rehabilitation sciences including data collection conducted using both synchronous (22.5%) and asynchronous (77.5%) models and using a defined moderation method. Three modes of focus group discussion were reported including email, chat-based, and videoconferencing; these were facilitated through the various technology platforms reported in the review. Reported barriers and facilitators to conducting virtual focus group research were extracted and summarized. Commonly reported facilitators to virtual focus group research included the ability to recruit participants from diverse geographical locations and the participants’ ability to engage at times convenient to them. Both computer literacy and access to technology were reported as common barriers. This review highlighted the need for further research and guidance around virtual focus groups conducted using face-to-face synchronous methods and with younger participants groups

Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

Harnessing the Power of Smart and Connected Health to Tackle COVID-19:IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) — including Machine Learning (ML) and Big Data analytics — as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19

Smart and collaborative industrial IoT: A federated learning and data space approach

Industry 4.0 has become a reality by fusing the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI), providing huge opportunities in the way manufacturing companies operate. However, the adoption of this paradigm shift, particularly in the field of smart factories and production, is still in its infancy, suffering from various issues, such as the lack of high-quality data, data with high-class imbalance, or poor diversity leading to inaccurate AI models. However, data is severely fragmented across different silos owned by several parties for a range of reasons, such as compliance and legal concerns, preventing discovery and insight-driven IIoT innovation. Notably, valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security. This adversely influences inter- and intra-organization collaborative use of IIoT data. To tackle these challenges, this article leverages emerging multi-party technologies, privacy-enhancing techniques (e.g., Federated Learning), and AI approaches to present a holistic, decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape. Moreover, to evaluate the efficiency of the proposed reference model, a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture. Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable, Accessible, Interoperable, and Reusable (FAIR) principles

Somaclonal variation of tissue culture regenerated plants of Aloe barbadensis Mill.

Aloe barbadensis is perennial, monocotyledonous, fleshy plant belongs to Aloaceae family. In this study, somoclonal variations of regenerated A. barbadensis plants were investigated. The plantlets of forth subculture transferred to the soil for further study. The genomic DNAs of 40 regenerated plantlets were extracted and genetic variations were studied using SPAR markers including RAPD and ISSR primers. The amounts of Aloe gel also were extracted from regenerated A. vera plants. Average percentage of polymorphism, Shannon index, Nei's genetic diversity and number of effective alleles based on RAPD data were higher than genetic parameters obtained from ISSR data. NJ cluster and STRUCTURE plot based on molecular markers grouped regenerated plants to distinct clusters. AMOVA analysis also showed a significant (P = 0.01) genetic distinction between studied groups. This result also confirmed differentiation of regenerated plants. The amount of Aloe gel in the four groups (based on clustering method) was compared by using analysis of variance (ANOVA). The results showed no significant (P = 0.746) differences between the amount of gel in four group. In total, our findings showed somaclonal variations on genomic level while no significant differences were observed in amount of gel among regenerated Aloe plantlets

Towards Collaborative Machine Learning Driven Healthcare Internet of Things

© 2019 Copyright held by the owner/author(s). The relationship between technology and healthcare due to the rise of Intelligent Internet of Things (IoT) and the rapid public embracement of medical-grade wearables has been dramatically transformed in the past few years. Powered by IoT, technology brought disruptive changes and unique opportunities to the healthcare industry including personalized services, tailored content, improved availability and accessibility, and cost-effective delivery. Despite these exciting advancements in transition from clinic-centric to patient-centric healthcare, many challenges still need to be tackled. The key to successfully unlock and enable this digital shift is adopting a holistic architecture to provide high-level of quality in attributes such as latency, availability, and real-time analytics processing. In this paper, we discuss applicability of Intelligent IoT based on Collaborative Machine Learning in healthcare and medicine by presenting a holistic multi-layer architecture. This solution enables real-time actionable insights which ultimately improves decision-making powers of patients and healthcare providers. The feasibility of such architecture is investigated by a case study, ECG-based arrhythmia detection, based on deep learning and Convolutional Neural Network (CNN) methods distributed across endpoint IoT Devices, Edge (Fog) nodes, and Cloud servers.status: Published onlin

Towards collaborative intelligent IoT eHealth: From device to fog, and cloud

The relationship between technology and healthcare due to the rise of intelligent Internet of Things (IoT), Artificial Intelligence (AI), and the rapid public embracement of medical-grade wearables has been dramatically transformed in the past few years. AI-powered IoT enabled disruptive changes and unique opportunities to the healthcare industry through personalized services, tailored content, improved availability and accessibility, and cost-effective delivery. Despite these exciting advancements in the transition from clinic-centric to patient-centric healthcare, many challenges still need to be tackled. The key to successfully unlock and enable this horizon shift is adopting hierarchical and collaborative architectures to provide a high level of quality in key attributes such as latency, availability, and real-time analytics. In this paper, we propose a holistic AI-driven IoT eHealth architecture based on the concept of Collaborative Machine Learning approach in which the intelligence is distributed across Device layer, Edge/Fog layer, and Cloud layer. This solution enables healthcare professionals to continuously monitor health-related data of subjects anywhere at any time and provide real-time actionable insights which ultimately improves the decision-making power. The feasibility of such architecture is investigated using a comprehensive ECG-based arrhythmia detection case study. This illustrative example discusses and addresses all important aspects of the proposed architecture from design implications such as corresponding overheads, energy consumption, latency, and performance, to mapping and deploying advanced machine learning techniques (e.g., Convolutional Neural Network) to such architecture.status: Published onlin