Indoor Position Detection Using Smartwatch and Beacons

Sittichai Sukreep, Chakarida Nukoolkit, Pornchai Mongkolnam
<span title="2020-02-10">2020</span> <i title="MYU K.K."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bt2ehz5ef5fhlkzwztxe5angja" style="color: black;">Sensors and materials</a> </i> &nbsp;
Nowadays, the number of elderly and other people living alone is increasing. Although living alone allows more independence, it raises the risk of serious or even fatal accidents. To help assist those who live alone, we propose a monitoring system to detect indoor position by using a smartwatch and beacons, which are effective and low cost, easy to install, convenient, and unobtrusive in daily life. Data mining techniques were applied to classify indoor positioning zones. A noise reduction
more &raquo; ... ss combining two data smoothing techniques was incorporated. The best model for indoor positioning was chosen from various algorithms and different window sizes of data to achieve the best usage in a real-time classification. Both the number and positioning of beacons were also considered in this research. Various useful screens with easy-to-understand visualizations are provided for monitoring subject behaviors and time spent in certain areas, giving a summary of indoor positioning. Finally, the system allows users to manage indoor positioning by combining the marked spots as zoning areas. The management of different numbers of beacons and their locations is also provided to users.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18494/sam.2020.2386">doi:10.18494/sam.2020.2386</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oevrjrrhc5fhrhlbewldab3sj4">fatcat:oevrjrrhc5fhrhlbewldab3sj4</a> </span>
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