Vincenzo Marciano'
Machine Learning and Deep Learning models to discern indoor from outdoor environments based on data recorded by a tri-axial digital magnetic sensor.
Rel. Andrea Cereatti, Stefano Bertuletti. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2022
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Abstract
Wearable devices (e.g., smartphones, smartwatches, etc.) are increasingly used for position estimation of users both in indoor environments to provide personalized contents (e.g., malls, museums, crowded venues, etc.) and outdoor environments to provide additional information about their way of moving. Existing mainly solutions exploit the use of GPS which can provide very accurate location information in outdoor environments, while in indoor environments due to a variety of physical barriers that can attenuate the GPS signal (e.g., walls, floors) the quality of the provided information can be very poor. Other approaches which consist in the use of Wi-Fi, Bluetooth, barometers, and light sensors have been investigated and presented in the literature.
Despite these approaches can provide very accurate indoor location information, their use is limited because they require the presence of beacons (i.e., Wi-Fi, Bluetooth) and suitable mapping surveys of the interested areas (i.e., barometer, light sensors) which is not feasible for outdoor environments
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