Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare

Yang, Po; Stankevičius, Dainius; Marozas, Vaidotas; Deng, Zhikun; Liu, Enjie; Lukoševičius, Arūnas; Dong, Feng; Xu, Lida; Min, Geyong

doi:10.1109/tsmc.2016.2586075

Cited by 134 publications

(55 citation statements)

References 35 publications

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“…Tcarenko et al [27] introduce a system to send notications remotely to caregivers on critical health events, such as a fall. Some systems integrate mobile-like notifications further; Kubitza et al [28] describe an architecture that allows notifications to be displayed contextually through capable smart devices, reducing reliance on the mobile phone.…”

Section: Notifications and Remindersmentioning

confidence: 99%

IoTility: Architectural Requirements for Enabling Health IoT Ecosystems

Lindquist

Helal

Khaled

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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The increasing ubiquity of the Internet of Things (IoT) has the potential to drastically alter the way healthcare systems are utilized at home or in a care environment. Smart things offer new ways to assist in general patient wellness, such as promoting an active and healthy lifestyle and simplifying treatment management. We believe smart health things bring new requirements not typically addressed in traditional IoT systems, and that an architecture targeting these devices must address such requirements to fully utilize their potential and safe usage. We believe such an architecture will help improve adoption and efficacy, closing gaps between the variety of emerging health IoT systems. In this paper, we present a number of requirements we consider integral to the continued expansion of the digital health IoT ecosystem (Health IoT). We consider the current landscape of IoT in relation to these requirements and present solutions that address two pressing requirements: 1) democratizing mobile health apps (giving users control and ownership over their app and data), and 2) making mobile apps act and behave like any other thing in an IoT. We present an implementation and evaluation of these Health IoT requirements to show how health-specific solutions can drive and influence the design of more generalized IoT architectures.

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Section: Notifications and Remindersmentioning

confidence: 99%

IoTility: Architectural Requirements for Enabling Health IoT Ecosystems

Lindquist

Helal

Khaled

et al. 2021

IEEE Trans. Emerg. Topics Comput.

View full text Add to dashboard Cite

show abstract

“…Health IoT applications commonly use patient wearable devices, mobile, and social media applications to collect data. Research related to data collection process include topics such as improving validity of data [29], accurately and precisely taking measurements in real-time [30], and collecting and aggregating data using different sources such as mobile, wearables, and social media applications [31]. Not surprisingly, researchers have also focused their attention on effectively handling big data, which naturally overlaps with topics related to the cloud & smart health.…”

Section: Cluster 2: Datamentioning

confidence: 99%

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

Dantu¹,

Dissanayake²,

Nerur³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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The rapid integration of the physical and cyber worlds through the Internet of Things, or IoTs, is transforming our lives in ways that we could not have imagined even five years ago. Although they are still in their infancy, IoTs have already made a significant impact, particularly in the healthcare domain. The purpose of this study is to unravel key themes latent in the sparse but growing academic literature on the application of IoTs in healthcare. Specifically, we performed topic modeling and identified five dominant clusters of research, namely, privacy and security, wireless network technologies, applications, data, and smart health and cloud. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. In addition to categorizing and discussing the topics identified, the paper provides directions for future research.

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“…During the last few years, WSNs (wireless sensor network) have been widely studied and utilised in many industrial applications, related to forest monitoring [28], agriculture monitoring [29], and healthcare [30][31]. Compared to the practical working environment of above applications, marine environment systems are quite sensitive to the effects of human activities.…”

Section: Literature Reviewmentioning

confidence: 99%

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Cao

Zhao

Yang

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-Effective monitoring marine environment has become a vital problem in the marine applications. Traditionally, marine application mostly utilizes oceanographic research vessel methods to monitor the environment and human parameters. But these methods are usually expensive and time-consuming, also limited resolution in time and space. Due to easy deployment and cost-effective, WSNs have recently been considered as a promising alternative for next generation IMGs. This paper focuses on solving the issue of 3D WSN deployment in a 3D engine room space of a very large crude-oil carrier (VLCC), in which many power devices are also considered. To address this 3D WSN deployment problem for maritime applications, a 3D uncertain coverage model is proposed with a new 3D sensing model and an uncertain fusion operator, is presented. The deployment problem is converted into a multi-objective problems (MOP) in which three objectives are simultaneously considered: Coverage, Lifetime and Reliability. Our aim is to achieve extensive Coverage, long Lifetime and high Reliability. We also propose a distributed parallel cooperative co-evolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications. In the simulation experiments, the effectiveness of this algorithm is verified in comparing with five state-of-the-art algorithms. The numerical outputs demonstrate that the proposed method performs the best with respect to both optimization performance and computation time.Index Terms-wireless sensor network deployment, 3D engine room, Very large crude-oil carrier (VLCC), Coverage, Lifetime, Reliability, Parallel, Multi-objective evolutionary algorithm.

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Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare

Cited by 134 publications

References 35 publications

IoTility: Architectural Requirements for Enabling Health IoT Ecosystems

IoTility: Architectural Requirements for Enabling Health IoT Ecosystems

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

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