Emerging trends in IoT and big data analytics for biomedical and health care technologies

Banerjee, Amit; Chakraborty, Chinmay; Kumar, Anand; Biswas, Debabrata

doi:10.1016/b978-0-12-818318-2.00005-2

Cited by 93 publications

(50 citation statements)

References 43 publications

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“…We defined five keywords from the main scope and used them to organize this paper: Rehabilitation, Continuous Monitoring, Real-Time Tracking of People with Disabilities, Assisted Living, and Home Health Care. These topics were selected due to being observed in the current applications in biomedical research [15,16].…”

Section: Methodology For This Reviewmentioning

confidence: 99%

Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review

Nascimento

Bonfati

Freitas

et al. 2020

Sensors

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The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

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Section: Methodology For This Reviewmentioning

confidence: 99%

Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review

Nascimento

Bonfati

Freitas

et al. 2020

Sensors

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“…Complementing such paradigms, smart cities generate vast amounts of data, captured and stored in different heterogenous formats that need to be transformed to be of analytical value [53]. IoT and big data analytics are emerging trends in new biomedical and healthcare technologies [54]. These various different types of data need to be integrated to ensure fast and effective response during pandemic Although there are concerns about data privacy, there are many types of data that are already shared openly in different community settings (e.g.…”

Section: Types Of Digital Data In Pandemic Managementmentioning

confidence: 99%

COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine

et al. 2020

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Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries' digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual's location databases, code scanning and individual's online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on social machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a social machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the social aspect of human-computer interactions (social machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

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“…The internet of things 32 is expanding its footprint, and it aims to interconnect different devices and data streams to improve experience and output, health care in this case. As patient care improves with real-time monitoring of various physiological parameters, treatment decisions are being expedited for several diseases.…”

Section: The Interface Of Data Of Different Disorders or Specialtiesmentioning

confidence: 99%

Data Handling for E-Mental Health Professionals

Grover

Sarkar

Gupta

2020

Indian Journal of Psychological Medicine

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Digital psychiatry and e-mental health have proliferated and permeated vastly in the current landscape of mental health care provision. The COVID-19 crisis has accelerated this digital transformation, and changes that usually take many years to translate into clinical practice have been implemented in a matter of weeks. These have outpaced the checks and balances that would typically accompany such changes, which has brought into focus a need to have a proper approach for digital data handling. Health care data is sensitive, and is prone to hacking due to the lack of stringent protocols regarding its storage and access. Mental health care data need to be more secure due to the stigma associated with having a mental health condition. Thus, there is a need to emphasize proper data handling by mental health professionals, and policies to ensure safeguarding patient’s privacy are required. The aim of useful, free, and fair use of mental health care data for clinical, business, and research purposes should be balanced with the need to ensure the data is accessible to only those who are authorized. Systems and policies should be in place to ensure that data storage, access, and disposal are systematic and conform to data safety norms.

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Emerging trends in IoT and big data analytics for biomedical and health care technologies

Cited by 93 publications

References 43 publications

Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review

Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review

COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine

Data Handling for E-Mental Health Professionals

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