Public scene recognition using mobile phone sensors

Shuang Liang, Xiaojiang Du, Ping Dong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Smartphones evolve rapidly and become more powerful in computing capabilities. More importantly, they are becoming smarter as more sensors such as the accelerometer, gyroscope, compass and the camera have been embedded on the digital board. In this paper, we propose a novel framework to recognize public scenes based on the sensors embedded in mobile phones. We build individual models for audio, light, wifi and bluetooth first, then integrate these sub-models using dynamically-weighted majority voting. We consider two factors when deciding the voting weight. One factor is the recognition rate of each sub-model and the other factor is recognition precision of the sub-model in specific scenes. We build the data-collecting app on the Android phone and implement the recognition algorithm on a Linux server. Evaluation of the data collected in the bar, cafe, elevator, library, subway station and the office shows that the ensemble recognition model is more accurate and robust than each individual sub-models. We achieved 83.33% (13.33% higher than audio sub-model) recognition accuracy when we evaluated the ensemble model with test dataset.

Original languageEnglish
Title of host publication2016 International Conference on Computing, Networking and Communications, ICNC 2016
ISBN (Electronic)9781467385794
DOIs
StatePublished - 23 Mar 2016
EventInternational Conference on Computing, Networking and Communications, ICNC 2016 - Kauai, United States
Duration: 15 Feb 201618 Feb 2016

Publication series

Name2016 International Conference on Computing, Networking and Communications, ICNC 2016

Conference

ConferenceInternational Conference on Computing, Networking and Communications, ICNC 2016
Country/TerritoryUnited States
CityKauai
Period15/02/1618/02/16

Keywords

  • ensemble learning
  • mobile sensing
  • scene recognition

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