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Recognizing Human Activities in Real-Time Using Mobile Phone Sensors

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Advances in Wireless Sensor Networks (CWSN 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 501))

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Abstract

To overcome the defects that previous research cannot recognize human activities accurately in real-time, we proposed a novel method, which collects data from the accelerator and gyroscope on a mobile phone, and then extracts features of both time domain and frequency domain. These features are used to learn random forest models offline, which make our mobile app can recognize human activities accurately online in real-time. Verified by theoretical analysis and a large number of contrast experiments, the recognition is rapid and accurate on mobile phones with accuracy at 97 %.

This work is supported in part by the National Natural Science Foundation of China(NSFC) under Grant No.61370222 and No.61070193, Heilongjiang Province Founds for Distinguished Young Scientists under Grant No.JC201104, Natural Science Foundation of Heilongjiang Province of China No.F201225, Technology Innovation of Heilongjiang Educational Committee under grant No.2013TD012.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China(NSFC) under Grant No.61370222 and No.61070193, Heilongjiang Province Founds for Distinguished Young Scientists under Grant No.JC201104, Natural Science Foundation of Heilongjiang Province of China No.F201225, Technology Innovation of Heilongjiang Educational Committee under grant No.2013TD012.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, Heilongjiang, China

    Boxuan Jia

  2. Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Harbin, 150080, Heilongjiang, China

    Jinbao Li

Authors
  1. Boxuan Jia
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  2. Jinbao Li
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Corresponding author

Correspondence to Jinbao Li .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Limin Sun

  2. Beijing, China

    Huadong Ma

  3. Northwest University, Xi’an, China

    Dingyi Fang

  4. Northwest University, Xi’an, China

    Jinping Niu

  5. Northwest University, Xi’an, China

    Wei Wang

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Jia, B., Li, J. (2015). Recognizing Human Activities in Real-Time Using Mobile Phone Sensors. In: Sun, L., Ma, H., Fang, D., Niu, J., Wang, W. (eds) Advances in Wireless Sensor Networks. CWSN 2014. Communications in Computer and Information Science, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46981-1_60

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  • DOI: https://doi.org/10.1007/978-3-662-46981-1_60

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46980-4

  • Online ISBN: 978-3-662-46981-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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