GPS-Based Daily Context Recognition for Lifelog Generation Using Smartphone

Go Tanaka, Masaya Okada, Hiroshi Mineno
<span title="">2015</span> <i title="The Science and Information Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2yzw5hsmlfa6bkafwsibbudu64" style="color: black;">International Journal of Advanced Computer Science and Applications</a> </i> &nbsp;
Mobile devices are becoming increasingly more sophisticated with their many diverse and powerful sensors, such as GPS, acceleration, and gyroscope sensors. They provide numerous services for supporting daily human life and are now being studied as a tool to reduce the worldwide increase of lifestyle-related diseases. This paper describes a method for recognizing the contexts of daily human life by recording a lifelog based on a person's location. The proposed method can distinguish and
more &raquo; ... several contexts at the same location by extracting features from the GPS data transmitted from smartphones. The GPS data are then used to generate classification models by machine learning. Five classification models were generated: a mobile or stationary recognition model, a transportation recognition model, and three daily context recognition models. In addition, optimal learning algorithms for machine learning were determined. The experimental results show that this method is highly accurate. As examples, the Fmeasure of the daily context recognition was approximately 0.954 overall at a tavern and approximately 0.920 overall at a university 1 .
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14569/ijacsa.2015.060216">doi:10.14569/ijacsa.2015.060216</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nhjph3l2fzfd5dlm3e7psem6ue">fatcat:nhjph3l2fzfd5dlm3e7psem6ue</a> </span>
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