TY - GEN
T1 - AUDAPRET
T2 - 12th IADIS International Conference e-Health 2020, EH 2020, Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020
AU - Von Frankenberg, Nadine
AU - Matschilles, Felix
AU - Jonas, Stephan
N1 - Publisher Copyright:
© Proceedings of the 12th IADIS International Conference e-Health 2020, EH 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Recent improvements in the functional range and measurement accuracy of commercially available biosignal measurement devices enable cost-effective human trials with large numbers of participants. Biosignals can be tracked outside the research institution, with only minor restrictions in the daily activities of the study participants, compared to stationary measuring devices. However, there is a lack of data acquisition systems supporting the use of several different devices needed, so that the collected data from measurement devices have to be processed manually. Researchers must also abide by strict legal regulations when handling health-related data of study participants, which is often cumbersome and requires additional time-consuming efforts. To address these challenges, this paper proposes the AUtomatic DAta Processing of REsearch Trials (AUDAPRET) architecture, which enables researchers to conduct trials requiring health-related data of participants in a less time-consuming and legally compliant manner. AUDAPRET is designed in such a way that study participants retain full control over their data and the access rights of anyone requesting access.
AB - Recent improvements in the functional range and measurement accuracy of commercially available biosignal measurement devices enable cost-effective human trials with large numbers of participants. Biosignals can be tracked outside the research institution, with only minor restrictions in the daily activities of the study participants, compared to stationary measuring devices. However, there is a lack of data acquisition systems supporting the use of several different devices needed, so that the collected data from measurement devices have to be processed manually. Researchers must also abide by strict legal regulations when handling health-related data of study participants, which is often cumbersome and requires additional time-consuming efforts. To address these challenges, this paper proposes the AUtomatic DAta Processing of REsearch Trials (AUDAPRET) architecture, which enables researchers to conduct trials requiring health-related data of participants in a less time-consuming and legally compliant manner. AUDAPRET is designed in such a way that study participants retain full control over their data and the access rights of anyone requesting access.
KW - Data Collection
KW - EHealth Architectures
KW - Health Information Systems
KW - Human Study Management
UR - http://www.scopus.com/inward/record.url?scp=85101158704&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85101158704
T3 - Proceedings of the 12th IADIS International Conference e-Health 2020, EH 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020
SP - 209
EP - 212
BT - Proceedings of the 12th IADIS International Conference e-Health 2020, EH 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020
PB - IADIS
Y2 - 21 July 2020 through 23 July 2020
ER -