cogcom 15(5): e3

Research Article

Classification of Steps on Road Surface Using Acceleration Signals

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  • @ARTICLE{10.4108/eai.22-7-2015.2260293,
        author={Junji Takahashi and Yusuke Kobana and Yoshito Tobe and Gullaume Lopez},
        title={Classification of Steps on Road Surface Using Acceleration Signals},
        journal={EAI Endorsed Transactions on Cognitive Communications},
        volume={1},
        number={5},
        publisher={EAI},
        journal_a={COGCOM},
        year={2015},
        month={8},
        keywords={wearable sensor, road monitoring, independent component analysis},
        doi={10.4108/eai.22-7-2015.2260293}
    }
    
  • Junji Takahashi
    Yusuke Kobana
    Yoshito Tobe
    Gullaume Lopez
    Year: 2015
    Classification of Steps on Road Surface Using Acceleration Signals
    COGCOM
    EAI
    DOI: 10.4108/eai.22-7-2015.2260293
Junji Takahashi1,*, Yusuke Kobana2, Yoshito Tobe1, Gullaume Lopez1
  • 1: Aoyama Gakuin University
  • 2: Graduate School of Aoyama Gakuin University
*Contact email: takahashi@it.aoyama.ac.jp

Abstract

In order to reduce a road monitoring cost, we propose a system to monitor extensively road condition by cyclists with a smartphone. In this paper, we propose two methods towards road monitoring. First is to classify road signals to four road conditions. Second is to extract road signal from a smartphone's accelerometer in three positions: pants' side pocket, chest pocket and a bag in a front basket. In pants' side pocket, road signal is extracted by Independent Component Analysis. In chest pocket and bag in a front basket, road signal is extracted by selecting 1-axis affected from gravitational acceleration. In the experiment of the classification method, overall accuracy was 75%. The experimental results of the extraction methods with correlation coefficient showed the overall accuracy were more than 0.7 in pants' side pocket and chest pocket, the overall accuracy was less than 0.3 in bag in a front basket.