engleski

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

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.

geospatial analysis/GIS; activity patterns; consumer product

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