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ThinkMind // SIGNAL 2022, The Seventh International Conference on Advances in Signal, Image and Video Processing // View article signal_2022_1_10_60003
Cleaning Outdoor Activity Logs using Deep Learning
 Authors:
Davide Sbetti
Sergio Tessaris Keywords: Data Cleaning, GPS Traces, Trajectory repairing, recurrent neural networks Abstract:
Nowadays, position recording personal tracking devices are ubiquitous and used by both athletes and outdoor enthusiasts to track and analyse their activities. These devices rely on Global Navigation Satellite Systems to obtain the position in real time. Although the nominal precisions of the different GNSS are high enough for analysis, there are several environmental factors that affect the precision of such devices. Most of the commercial services providing analysis of outdoor activities use techniques to ``clean'' the user-uploaded data (tracklogs). Most of these techniques require and exploit the huge amount of data that they collect and analyse, but the resulting logs still manifest outliers and recording errors. In this paper, we present a deep learning based technique to identify part of tracklogs that might be influenced by recording errors, in such a way that can be corrected using standard techniques. Our approach does not require geographical or crowdsourced data, and can be also used on low powered devices. Pages: 1 to 6 Copyright: Copyright (c) IARIA, 2022 Publication date: May 22, 2022 Published in: conference ISSN: 2519-8432 ISBN: 978-1-61208-970-6 Location: Venice, Italy Dates: from May 22, 2022 to May 26, 2022
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