Naslov
Prediction of Location in Indoor/Outdoor Micro-Environments Using Smart Consumer Products

Autori
Pronk, Anjoeka ; Sarigiannis, Denis ; Chapizanis, Dimitrios ; Karakitsios, Spiros ; Kuijpers, Eelco ; Boessen, R. ; Pierik, F. ; Maggos, Tomas ; Stamatelopoulou, Asimina ; Bartzis, John ; Špirić, Zdravko ; Schieberle, Christian ; Loh, Miranda ; Cherrie, John

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
The International Society of Exposure Science 25th Annual Meeting ISES 2015 Abstract book / Blount, Ben ; LaKind, Judy - Henderson (NV) : The International Society of Exposure Science, 2015, 205-205

Skup
The International Society of Exposure Science 25th Annual Meeting

Mjesto i datum
Henderson (NV), Sjedinjene Američke Države, 18.10.2015. - 22.10.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
geospatial analysis/GIS; activity patterns; consumer product

Sažetak
Introduction: The determination of presence in micro environments including indoor vs outdoor is critical for modelling personal exposure based on time-location-activity data. The aim of this study was to investigate the potential use of smart consumer products in combination with other (sensor) data for predicting the presence in indoor and outdoor micro-environments . Methods: As part of the HEALS project time-location-activity data were collected from 28 office workers for 7 days with the MOVES app on a personal smartphone and the Fitbit Flex. In addition, real time personal air temperature (Elitech RC) was measured for all participants and real time personal UV level (Extech Luxmeter with Semrock 300/80 nm filter) was measured at 4 participants, both devices were attached to the outer clothing. Paper logs were kept by each participants for logging time-activity and indoor and outdoor locations. Results: The MOVES classification (place(=cluster), walk, cycle, transport) and the paper log correlated well. The predictive value of personal temperature, personal UV level, historical weather data (mean local temperate, rainy day) and day/time indicators (day of the week and time of the day) for further classification of indoor cluster versus outdoor cluster was explored using random forest models. Preliminary results indicate a moderate to high accuracy (65-99%) for the different study subjects. Discussion: The preliminary results indicate that when using MOVES to assess personal time-location-activity information additional (sensor) data may be used to further classify the places into indoor and outdoor places. Ongoing analyses focus on optimizing of the models for predicting indoor versus outdoor places and on generalizability of these models.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Javno zdravstvo i zdravstvena zaštita

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