Keynote Paper: From EDA to IoT eHealth: Promises, Challenges, and Solutions

Firouzi, Farshad; Farahani, Bahar; Ibrahim, Mohamed; Chakrabarty, Krishnendu

doi:10.1109/tcad.2018.2801227

Cited by 96 publications

(50 citation statements)

References 90 publications

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“…Big Data Technology Challenges. Big data in healthcare is enormous and highly fragmented which causes problems in quality of information, as well as technology-wise, big data creates a barrier to accomplish the healthcare vision [44].…”

Section: Issues In Healthcare Bigmentioning

confidence: 99%

A Systematic Review of Healthcare Big Data

Raja

Mukherjee

Sarkar

2020

Scientific Programming

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Over the past decade, data recorded (due to digitization) in healthcare sectors have continued to increase, intriguing the thought about big data in healthcare. There already exists plenty of information, ready for analysis. Researchers are always putting their best effort to find valuable insight from the healthcare big data for quality medical services. This article provides a systematic review study on healthcare big data based on the systematic literature review (SLR) protocol. In particular, the present study highlights some valuable research aspects on healthcare big data, evaluating 34 journal articles (between 2015 and 2019) according to the defined inclusion-exclusion criteria. More specifically, the present study focuses to determine the extent of healthcare big data analytics together with its applications and challenges in healthcare adoption. Besides, the article discusses big data produced by these healthcare systems, big data characteristics, and various issues in dealing with big data, as well as how big data analytics contributes to achieve a meaningful insight on these data set. In short, the article summarizes the existing literature based on healthcare big data, and it also helps the researchers with a foundation for future study in healthcare contexts.

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Section: Issues In Healthcare Bigmentioning

confidence: 99%

A Systematic Review of Healthcare Big Data

Raja

Mukherjee

Sarkar

2020

Scientific Programming

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“…As mentioned before, to realize power-efficient and constantly running systems for eHealth monitoring applications, it is essential to focus on lightweight algorithms that do not require heavy arithmetic operations [1]. It should be noted that lightweight algorithms are not inferior to heavier and more computationally intensive algorithms in terms of detection accuracy.…”

Section: A Various Ehealth Monitoring Applicationsmentioning

confidence: 99%

“…Advances in Internet of Things (IoT) technologies have highlighted the value of IoT devices in healthcare systems. With the help of information and communication technologies in the healthcare domain (i.e., eHealth), a variety of monitoring applications can be realized using real-time data such as accelerometer and biomedical signals like electrocardiogram (ECG), electroencephalogram (EEG), and blood pressure to help with diagnoses and decision making [1]. Because monitoring tasks must be conducted on site, eHealth devices are battery-powered embedded systems.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Lightweight eHealth Applications on a Low-Power Embedded Processor

Yang

Hara-Azumi

2020

IEEE Access

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The rapid development of Internet of Things (IoT) has opened new opportunities for healthcare systems through so-called eHealth systems. With the help of monitoring using portable IoT devices with biomedical sensors, disease diagnoses can be conducted in real time. However, there is a challenge in that monitoring is an always-on activity that requires constant power supply and IoT devices are battery-powered and face heavy resource constraints. This work addresses a realistic implementation of a low-power eHealth device using both hardware and software approaches. We realize various lightweight eHealth applications (particularly monitoring applications) using a memory-conscious dynamic time warping (DTW) algorithm to be deployed on a small and low-power embedded processor. Actual prototypes of the processor are currently being fabricated. Our evaluation showcases the effectiveness of our work compared with other state-of-the-art embedded processors in terms of circuit area (fabrication cost) and power efficiency. We also demonstrated the scalability of the software implementation by varying the amount of data used in DTW for various eHealth monitoring applications. INDEX TERMS Low power, Internet of Things (IoT), eHealth, dynamic time warping

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“…Moreover, functional elements such as integration with other devices, portability of the data and supporting software are relevant to the services of the wearables. Similar to infrastructural domains, data collected via wearables are subject to privacy and security risks, especially in the field of health wearables owing to the sensitivity of the collected data [47]. Usage ban has been proposed for some wearables (e.g., smart glasses) in some places, such as in movie theaters to avoid movie piracy or in changing rooms to avoid illegal filming.…”

Section: Individual Domain: Wearablesmentioning

confidence: 99%

Adoption Barriers of IoT in Large Scale Pilots

et al. 2019

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The pervasive connectivity of devices enabled by Internet of Things (IoT) technologies is leading the way in various innovative services and applications. This increasing connectivity comes with its own complexity. Thus, large scale pilots (LSPs) are designed to develop, test and use IoT innovations in various domains in conditions very similar to their operational scalable setting. One of the key challenges facing the diffusion of such innovations within the course of an LSP is understanding the conditions in which their respective users decide to adopt them (or not). Accordingly, in this study we explore IoT adoption barriers in four LSPs in Europe from the following domains: smart cities, autonomous driving, wearables and smart agriculture and farming. By applying Roger's Diffusion of Innovation as a theoretical lens and using empirical data from workshops and expert interviews, we identify a set of common and domain specific adoption barriers. Our results reveal that trust, cost, perceived value, privacy and security are common concerns, yet shape differently across domains. In order to overcome various barriers, the relative advantage or value of using the innovation needs to be clearly communicated and related to the users' situational use; while this value can be economic in some domains, it is more hedonic in others. LSPs were particularly challenged in applying established strategies to overcome some of those barriers (e.g., co-creation with end-users) due to the immaturity of the technology as well as the scale of pilots. Accordingly, we reflect on the theoretical choice in the discussion as well as the implications of this study on research and practice. We conclude with providing practical recommendations to LSPs and avenues for future research.

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Keynote Paper: From EDA to IoT eHealth: Promises, Challenges, and Solutions

Cited by 96 publications

References 90 publications

A Systematic Review of Healthcare Big Data

A Systematic Review of Healthcare Big Data

Implementation of Lightweight eHealth Applications on a Low-Power Embedded Processor

Adoption Barriers of IoT in Large Scale Pilots

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