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• W4382118082 abstract "Smartwatches are increasingly being used as fitness and health trackers. To provide such a service, these devices have to collect and process movement data gathered by built-in accelerometers and gyroscopes. Based on these data, existing studies leveraging smartphones have shown that it is possible to distinguish users when they (1) walk, (2) perform different hand gestures, or (3) pick up their phone from the table. However, to the best of our knowledge, the case of cycling has not been addressed yet. The goal of this paper is to close this gap by investigating whether it is possible to infer information about users wearing a smartwatch coupled with their smartphone when cycling, their bike type, seat height, gear, and the terrain. In addition, we explore whether it is possible to distinguish individual users based on their movement patterns that may lead to their (re)identification. To this end, we conducted a user study with 17 participants, equipped with a smartphone and a smartwatch, who had to ride along a bike road for two km. Among others, our results show that it is possible to infer the four characteristics bike type, gear, seat height, and terrain with accuracies of 93.05%, 92.23%, 95.76%, 94.24% respectively and distinguish participants with a probability of 99.01%." @default.
• W4382118082 created "2023-06-27" @default.
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• W4382118082 date "2023-01-01" @default.
• W4382118082 modified "2023-10-15" @default.
• W4382118082 title "Does Cycling Reveal Insights About You? Investigation of User and Environmental Characteristics During Cycling" @default.
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• W4382118082 doi "https://doi.org/10.1007/978-3-031-34776-4_10" @default.
• W4382118082 hasPublicationYear "2023" @default.
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