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International Conference on Mobile Computing, Applications, and Services

MobiCASE 2018: Mobile Computing, Applications, and Services pp 106–124Cite as

SmokeSense: Online Activity Recognition Framework on Smartwatches

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 240)

Abstract

In most cases, human activity recognition (AR) with smartphones and smartwatches has been done offline due to the limited resources of these devices. Initially, these devices were used for logging sensor data which was later on processed in machine learning tools on a desktop or laptop. However, current versions of these devices are more capable of running an activity recognition system. Therefore, in this paper, we present SmokeSense, an online activity recognition (AR) framework developed for both smartphones and smartwatches on Android platform. This framework can log data from various sensors and can run an AR process in real-time locally on these devices. Any classifier or feature can easily be added on demand. As a case study, we evaluate the recognition performance of smoking with four classifiers, four features, and two sensors on a smartwatch. The activity set includes variants of smoking such as smoking while sitting, standing, walking, biking, as well as other similar activities. Our analysis shows that, similar recognition performance can be achieved in an online recognition as in an offline analysis, even if no training data is available for some smoking postures. We also propose a smoking session detection algorithm to count the number of cigarettes smoked and evaluate its performance.

Keywords

  • Online Activity Recognition
  • Smart Watches
  • Smoking Session
  • AR Process
  • Human Action Recognition

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-90740-6_7
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Notes

  1. 1.

    However, due to page size limitation we only present the results of smoking recognition. Interested readers can refer to [17].

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Acknowledgements

This work is supported by Dutch National Program COMMIT in the context of SWELL project, by the Galatasaray University Research Fund under the grant number 17.401.004.

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Authors and Affiliations

  1. Pervasive Systems Research Group, University of Twente, Enschede, The Netherlands

    Muhammad Shoaib, Hans Scholten & Paul Havinga

  2. Department of Computer Engineering, Galatasaray University, Istanbul, Turkey

    Ozlem Durmaz Incel

Authors
  1. Muhammad Shoaib
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  2. Ozlem Durmaz Incel
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  3. Hans Scholten
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  4. Paul Havinga
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Corresponding author

Correspondence to Ozlem Durmaz Incel .

Editor information

Editors and Affiliations

  1. Ritsumeikan University, Kusatsu-shi, Shiga, Japan

    Kazuya Murao

  2. Toyohashi University of Technology, Toyohashi-shi, Aichi, Japan

    Ren Ohmura

  3. Kyushu Institute of Technology, Kitakyushu-shi, Fukuoka, Japan

    Sozo Inoue

  4. Okayama University, Okayama-shi, Okayama, Japan

    Yusuke Gotoh

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Shoaib, M., Incel, O.D., Scholten, H., Havinga, P. (2018). SmokeSense: Online Activity Recognition Framework on Smartwatches. In: Murao, K., Ohmura, R., Inoue, S., Gotoh, Y. (eds) Mobile Computing, Applications, and Services. MobiCASE 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-90740-6_7

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