Layered hidden Markov models to recognize activity with built-in sensors on Android smartphone

Young Seol Lee, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Recently, activity recognition using built-in sensors in a mobile phone becomes an important issue. It can help us to provide context-aware services: health care, suitable content recommendation for a user’s activity, and user adaptive interface. This paper proposes a layered hidden Markov model (HMM) to recognize both short-term activity and long-term activity in real time. The first layer of HMMs detects short, primitive activities with acceleration, magnetic field, and orientation data, while the second layer exploits the inference of the previous layer and other sensor values to recognize goal-oriented activities of longer time period. Experimental results demonstrate the superior performance of the proposed method over the alternatives in classifying long-term activities as well as short-term activities. The performance improvement is up to 10 % in the experiments, depending on the models compared.

Original languageEnglish
Pages (from-to)1181-1193
Number of pages13
JournalPattern Analysis and Applications
Volume19
Issue number4
DOIs
Publication statusPublished - 2016 Nov 1

Bibliographical note

Funding Information:
This work was supported by the industrial strategic technology development program, 10044828, Development of augmenting multisensory technology for enhancing significant effect on service industry, funded by the Ministry of Trade, Industry & Energy (MI, Korea).

Publisher Copyright:
© 2016, Springer-Verlag London.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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