Location Based Indoor and Outdoor Lightweight Activity Recognition System

Aritz Bilbao-Jayo, Xabier Cantero, Aitor Almeida, Luca Fasano, Teodoro Montanaro, Ilaria Sergi, Luigi Patrono
<span title="2022-01-25">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care,
more &raquo; ... re inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user's daily activities, requiring a minimal infrastructure.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics11030360">doi:10.3390/electronics11030360</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yqsddaoi55g4hjloz3ic6fm6iu">fatcat:yqsddaoi55g4hjloz3ic6fm6iu</a> </span>
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