engleski

Predicting location using ANN, based on sensors data

Background and aims: The spread of smartphone applications and fitness monitors provides less expensive Methods for tracking time-location- activity data, which is a critical source of information for modelling personal exposure. The present study examines the potential use of smart consumer products data for predicting location status. Methods: A trial campaign of instrument reliability took place examining a series of monitors, such as the FitBit Flex and Moves app, for tracking people’s location and activities. Four participants in the city of Thessaloniki wore these devices along with a wearable temperature sensor, an Actigraph and a GPS sensor for a week. Since location data alone does not reliably determine whether a person is indoors outdoors or in transit, the predictive value of the aforementioned devices data was explored using an ANN, resulting to a time-activity model based solely on sensor records. The independent variables that fed the ANN input layer were consisted of a) personal temperature, Temp, b) the change of personal temperature with time, dTemp/dt, c) personal speed, Speed, e) the observed temperature, based on a central weather station measurements, Tempout, d) and the ratio of personal temperature to the observed one, Temp/Tempout. Moreover, day light information was transformed into a categorical element (day or night) which was also included as an input variable. The initial database was divided into training and validation set (85% and 15% of the total record entries, respectively) and the models developed from the training set were tested using the validation set. Results: The ANN predicted results were compared to real data based on time-activity log records, filled out on paper by participants. The accuracy of the ANN predictions is close to 85%. Conclusions: While the model is being refined, it is already clear that this kind of investigation provides useful information on the utility of commercial devices as modular add-ons to exposure studies.

smartphone applications; tracking time-location-activity data

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