Abstract
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, where 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.
Highlights
In smart environments one of the most relevant information that can be gathered about users is their position
We have developed a building partitioning method in order to improve the performance of the reverse geocoding tool provided by Nominatim (https://github.com/osm-search/Nominatim), the public Application Programming Interface (API) of OSM
During this manuscript we have proposed an innovative solution in order to address the task of recognizing users’ activities with a minimal infrastructure: a mobile application installed in an smartphone or smartwatch and some Bluetooth Low Energy (BLE) beacons
Summary
In smart environments one of the most relevant information that can be gathered about users is their position. There are situations where an environment has been adapted to users with special needs or pathologies where having a dense sensor infrastructure is justified To address those situations, we propose an extension of our previous work [7] on location-based activity recognition systems in two environments: indoor and outdoor. We propose an extension of our previous work [7] on location-based activity recognition systems in two environments: indoor and outdoor When it comes to the outdoor positioning system, we exploit the semantic location data exposed in services like OSM to identify the activities that the user is performing. The system requires minimal infrastructure at low cost Such a system can extend the proposed method to provide a more comprehensive solution in mixed environments where the indoor positioning in places like a home is needed.
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