mQoL smart lab: quality of life living lab for interdisciplinary experiments
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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mQoL smart lab : quality of life living lab for interdisciplinary experiments. / De Masi, Alexandre; Ciman, Matteo; Gustarini, Mattia; Wac, Katarzyna.
UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, 2016. p. 635-640.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - mQoL smart lab
AU - De Masi, Alexandre
AU - Ciman, Matteo
AU - Gustarini, Mattia
AU - Wac, Katarzyna
PY - 2016
Y1 - 2016
N2 - As a base for hypothesis formulation and testing, accurate, timely and reproducible data collection is a challenge for all researchers. Data collection is especially challenging in uncontrolled environments, outside of the lab and when it involves many collaborating disciplines, where the data must serve quality research in all of them. In this paper, we present own "mQoL Smart Lab" for interdisciplinary research efforts on individuals' "Quality of Life" improvement. We present an evolution of our current in-house living lab platform enabling continuous, pervasive data collection from individuals' smartphones. We discuss opportunities for mQoL stemming from developments in machine learning and big data for advanced data analytics in different disciplines, better meeting the requirements put on the platform.
AB - As a base for hypothesis formulation and testing, accurate, timely and reproducible data collection is a challenge for all researchers. Data collection is especially challenging in uncontrolled environments, outside of the lab and when it involves many collaborating disciplines, where the data must serve quality research in all of them. In this paper, we present own "mQoL Smart Lab" for interdisciplinary research efforts on individuals' "Quality of Life" improvement. We present an evolution of our current in-house living lab platform enabling continuous, pervasive data collection from individuals' smartphones. We discuss opportunities for mQoL stemming from developments in machine learning and big data for advanced data analytics in different disciplines, better meeting the requirements put on the platform.
KW - Data Analysis
KW - Data Collection
KW - Data Science
KW - People Centric Sensing
KW - Platforms
KW - Smartphones
UR - http://www.scopus.com/inward/record.url?scp=84991049629&partnerID=8YFLogxK
U2 - 10.1145/2968219.2971593
DO - 10.1145/2968219.2971593
M3 - Article in proceedings
AN - SCOPUS:84991049629
SP - 635
EP - 640
BT - UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery
Y2 - 12 September 2016 through 16 September 2016
ER -
ID: 168136295