research-article

A review of frameworks on continuous data acquisition for e-Health and m-Health

Authors:

Ivan Miguel Pires,

Nuno M. Garcia,

Nuno PomboAuthors Info & Claims

GoodTechs '19: Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good

Pages 231 - 234

https://doi.org/10.1145/3342428.3342702

Published: 25 September 2019 Publication History

Abstract

There is a huge number of mobile devices which use as an e-Health and m-Health system. The main purpose of the article is to make a review of frameworks for continuous data acquisition to identify the most commonly used and better methods. We are discussing environmental monitoring, middle-tier, cross-sending, and middleware frameworks such as SeeMon, DEAMON (Distributed Energy-Aware Monitoring), PRISM (Performance of Routine Information System Management), Medusa, MOSDEN (Mobile Sensor Data Processing Engine), C-MOSDEN (Context-aware data streaming engine called Mobile Sensor Date Engine) and MECA (Mobile edge capture and analysis middleware for social sensing applications) frameworks. These results are able to develop e-Health and m-Health systems in order to improve their efficiency.

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Cited By

Francisco LDuarte JGodinho AZdravevski EAlbuquerque CPires ICoelho P(2024)Sensor-based systems for the measurement of Functional Reach Test results: a systematic reviewPeerJ Computer Science10.7717/peerj-cs.182310(e1823)Online publication date: 15-Mar-2024
https://doi.org/10.7717/peerj-cs.1823
Szabo DNeagu NTeodorescu SApostu MPredescu CPârvu CVeres C(2023)The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic ApproachesSensors10.3390/s2321895023:21(8950)Online publication date: 3-Nov-2023
https://doi.org/10.3390/s23218950
Duarte JFrancisco LPires ICoelho P(2023)Smart Wearables Data Collection and Analysis for Medical Applications: A Preliminary Approach for Functional Reach TestBioinformatics and Biomedical Engineering10.1007/978-3-031-34960-7_34(481-491)Online publication date: 29-Jun-2023
https://doi.org/10.1007/978-3-031-34960-7_34
Show More Cited By

Index Terms

A review of frameworks on continuous data acquisition for e-Health and m-Health
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Health informatics
  2. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design

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cover image ACM Other conferences

GoodTechs '19: Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good

September 2019

272 pages

ISBN:9781450362610

DOI:10.1145/3342428

General Chairs:
Armir Bujari
Università di Padova, Italy
,
Pietro Manzoni
Universitat Politecnica de Valencia, Spain
,
Program Chairs:
Anna Forster
University of Bremen, Germany
,
Edjair Mota
UFAM, Brazil
,
Ombretta Gaggi
Università di Padova, Italy

Copyright © 2019 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2019

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Fundação para a Ciência e a Tecnologia

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GoodTechs '19

GoodTechs '19: EAI International Conference on Smart Objects and Technologies for Social Good

September 25 - 27, 2019

Valencia, Spain

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Cited By

Francisco LDuarte JGodinho AZdravevski EAlbuquerque CPires ICoelho P(2024)Sensor-based systems for the measurement of Functional Reach Test results: a systematic reviewPeerJ Computer Science10.7717/peerj-cs.182310(e1823)Online publication date: 15-Mar-2024
https://doi.org/10.7717/peerj-cs.1823
Szabo DNeagu NTeodorescu SApostu MPredescu CPârvu CVeres C(2023)The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic ApproachesSensors10.3390/s2321895023:21(8950)Online publication date: 3-Nov-2023
https://doi.org/10.3390/s23218950
Duarte JFrancisco LPires ICoelho P(2023)Smart Wearables Data Collection and Analysis for Medical Applications: A Preliminary Approach for Functional Reach TestBioinformatics and Biomedical Engineering10.1007/978-3-031-34960-7_34(481-491)Online publication date: 29-Jun-2023
https://doi.org/10.1007/978-3-031-34960-7_34
Salem MElkaseer AEl-Maddah IYoussef KScholz SMohamed H(2022)Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future ProspectsSensors10.3390/s2217662522:17(6625)Online publication date: 1-Sep-2022
https://doi.org/10.3390/s22176625
Pires ILopes EVillasana MGarcia NZdravevski EPonciano V(2021)A Brief Review on the Sensor Measurement Solutions for the Ten-Meter Walk TestComputers10.3390/computers1004004910:4(49)Online publication date: 11-Apr-2021
https://doi.org/10.3390/computers10040049
Capris TMelo PPereira PMorgado JGarcia NPires I(2021)Approach for the Development of a System for COVID-19 Preliminary TestScience and Technologies for Smart Cities10.1007/978-3-030-76063-2_9(117-124)Online publication date: 22-May-2021
https://doi.org/10.1007/978-3-030-76063-2_9
Oniani SMukhashavria SMarques GShalikiani VMosashvili I(2021)Mobile Computing Technologies for Enhanced Living Environments: A Literature ReviewThe Big Data-Driven Digital Economy: Artificial and Computational Intelligence10.1007/978-3-030-73057-4_2(21-32)Online publication date: 29-May-2021
https://doi.org/10.1007/978-3-030-73057-4_2
Oniani SMarques GPires IMuhkashavria SGarcia N(2020)E-health and M-health applications in Georgia: A review on the free available applications for Android Devices2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378291(3793-3796)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378291

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