Where are you?: Human activity recognition with smartphone sensor data

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Association for Computing Machinery

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info:eu-repo/semantics/closedAccess

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This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge. © 2020 ACM.

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ACM SIGCHI;ACM SIGMOBILE
2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020 -- 12 September 2020 through 17 September 2020 -- -- 162964

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activity recognition, locomotion classification, machine learning, transportation mode prediction

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UbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

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