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Edge-Assisted Control for Healthcare Internet of Things: A Case Study on PPG-Based Early Warning Score

Authors:

Arman Anzanpour,

Marco Levorato,

Pasi Liljeberg,

Amir M. RahmaniAuthors Info & Claims

ACM Transactions on Internet of Things, Volume 2, Issue 1

Article No.: 1, Pages 1 - 21

https://doi.org/10.1145/3407091

Published: 19 October 2020 Publication History

Abstract

Recent advances in pervasive Internet of Things technologies and edge computing have opened new avenues for development of ubiquitous health monitoring applications. Delivering an acceptable level of usability and accuracy for these healthcare Internet of Things applications requires optimization of both system-driven and data-driven aspects, which are typically done in a disjoint manner. Although decoupled optimization of these processes yields local optima at each level, synergistic coupling of the system and data levels can lead to a holistic solution opening new opportunities for optimization. In this article, we present an edge-assisted resource manager that dynamically controls the fidelity and duration of sensing w.r.t. changes in the patient’s activity and health state, thus fine-tuning the trade-off between energy efficiency and measurement accuracy. The cornerstone of our proposed solution is an intelligent low-latency real-time controller implemented at the edge layer that detects abnormalities in the patient’s condition and accordingly adjusts the sensing parameters of a reconfigurable wireless sensor node. We assess the efficiency of our proposed system via a case study of the photoplethysmography-based medical early warning score system. Our experiments on a real full hardware-software early warning score system reveal up to 49% power savings while maintaining the accuracy of the sensory data.

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Edge-Assisted Control for Healthcare Internet of Things: A Case Study on PPG-Based Early Warning Score

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Published In

cover image ACM Transactions on Internet of Things

ACM Transactions on Internet of Things Volume 2, Issue 1

February 2021

199 pages

EISSN:2577-6207

DOI:10.1145/3430935

Editors:
Schahram Dustdar
TU Wien, Austria
,
Gian Pietro Picco
University of Trento, Italy

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 19 October 2020

Accepted: 01 June 2020

Revised: 01 March 2020

Received: 01 August 2018

Published in TIOT Volume 2, Issue 1

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https://doi.org/10.1109/MeMeA60663.2024.10596893
Lopes SPinho PMarques PAbreu CMilheiro JBraga BQueirós GSilva FAlmeida RCarvalho N(2024)CoViS: A Contactless Health Monitoring System for the Nursing Home Lessons learned from practiceIEEE Access10.1109/ACCESS.2024.3355060(1-1)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3355060
Andrade ACabral ABellini BFacco Rodrigues Vda Rosa Righi RAndré da Costa CBarbosa J(2024)IoT-based vital sign monitoring: A literature reviewSmart Health10.1016/j.smhl.2024.10046232(100462)Online publication date: Jun-2024
https://doi.org/10.1016/j.smhl.2024.100462
Mtowe DKim D(2023)Edge-Computing-Enabled Low-Latency Communication for a Wireless Networked Control SystemElectronics10.3390/electronics1214318112:14(3181)Online publication date: 22-Jul-2023
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Pazienza AAnglani RFasciano CTatulli CVitulano F(2022)Evolving and explainable clinical risk assessment at the edgeEvolving Systems10.1007/s12530-021-09403-313:3(403-422)Online publication date: 23-Jan-2022
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