IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices
It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide....
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-04-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/19/9/1978 |
id |
doaj-775420a576094865b962b634484bfab2 |
---|---|
record_format |
Article |
spelling |
doaj-775420a576094865b962b634484bfab22020-11-25T01:23:18ZengMDPI AGSensors1424-82202019-04-01199197810.3390/s19091978s19091978IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical DevicesArgyro Mavrogiorgou0Athanasios Kiourtis1Konstantinos Perakis2Stamatios Pitsios3Dimosthenis Kyriazis4Department of Digital Systems, University of Piraeus, M. Karaoli & A. Dimitriou 80, 18534 Piraeus, GreeceDepartment of Digital Systems, University of Piraeus, M. Karaoli & A. Dimitriou 80, 18534 Piraeus, GreeceSingular Logic EU Projects Department, Achaias 3, 14564 Kifisia, GreeceSingular Logic EU Projects Department, Achaias 3, 14564 Kifisia, GreeceDepartment of Digital Systems, University of Piraeus, M. Karaoli & A. Dimitriou 80, 18534 Piraeus, GreeceIt is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.https://www.mdpi.com/1424-8220/19/9/1978internet of thingshealthcaremedical devicesheterogeneous devicesdata heterogeneityquality assessmentdata cleaningdata qualitydata interoperability |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Argyro Mavrogiorgou Athanasios Kiourtis Konstantinos Perakis Stamatios Pitsios Dimosthenis Kyriazis |
spellingShingle |
Argyro Mavrogiorgou Athanasios Kiourtis Konstantinos Perakis Stamatios Pitsios Dimosthenis Kyriazis IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices Sensors internet of things healthcare medical devices heterogeneous devices data heterogeneity quality assessment data cleaning data quality data interoperability |
author_facet |
Argyro Mavrogiorgou Athanasios Kiourtis Konstantinos Perakis Stamatios Pitsios Dimosthenis Kyriazis |
author_sort |
Argyro Mavrogiorgou |
title |
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices |
title_short |
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices |
title_full |
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices |
title_fullStr |
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices |
title_full_unstemmed |
IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices |
title_sort |
iot in healthcare: achieving interoperability of high-quality data acquired by iot medical devices |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-04-01 |
description |
It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy. |
topic |
internet of things healthcare medical devices heterogeneous devices data heterogeneity quality assessment data cleaning data quality data interoperability |
url |
https://www.mdpi.com/1424-8220/19/9/1978 |
work_keys_str_mv |
AT argyromavrogiorgou iotinhealthcareachievinginteroperabilityofhighqualitydataacquiredbyiotmedicaldevices AT athanasioskiourtis iotinhealthcareachievinginteroperabilityofhighqualitydataacquiredbyiotmedicaldevices AT konstantinosperakis iotinhealthcareachievinginteroperabilityofhighqualitydataacquiredbyiotmedicaldevices AT stamatiospitsios iotinhealthcareachievinginteroperabilityofhighqualitydataacquiredbyiotmedicaldevices AT dimostheniskyriazis iotinhealthcareachievinginteroperabilityofhighqualitydataacquiredbyiotmedicaldevices |
_version_ |
1725123090939117568 |