sas 16(6): e1

Research Article

Ascending and Descending Walking Recognition using Smartphone

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  • @ARTICLE{10.4108/eai.14-10-2015.2261961,
        author={Sang Vu and Khoa Truong and Shiro Yano and Toshiyuki Kondo},
        title={Ascending and Descending Walking Recognition using Smartphone},
        journal={EAI Endorsed Transactions on Self-Adaptive Systems},
        volume={2},
        number={6},
        publisher={ACM},
        journal_a={SAS},
        year={2015},
        month={12},
        keywords={fractal dimension, descending, ascending, human activity recognition, smartphone},
        doi={10.4108/eai.14-10-2015.2261961}
    }
    
  • Sang Vu
    Khoa Truong
    Shiro Yano
    Toshiyuki Kondo
    Year: 2015
    Ascending and Descending Walking Recognition using Smartphone
    SAS
    EAI
    DOI: 10.4108/eai.14-10-2015.2261961
Sang Vu1,*, Khoa Truong1, Shiro Yano1, Toshiyuki Kondo1
  • 1: Tokyo University of Agriculture and Technology
*Contact email: vsang@livingsys.lab.tuat.ac.jp

Abstract

In the recent decades, activity recognition for human behavior monitoring has been one of the major interesting research subjects. This paper investigates fractal dimension (FD) as an effective feature for human activity recognition. The target activities are descending and ascending stairs, which are performed by six male participants. To testify the validity of feature, the classification methods are k-nearest neighbors (kNN) and support vector machine (SVM). The best result achieved in this results is using SVM classifier and obtaining 95.30% for walking downstairs and 88.56% for walking upstairs with window of 4s and equalized data. Another aim of this paper is to examine the impact of database on the classification results. The finding in the paper provides evidence that the classification accuracy is affected insignificantly by the ratio of descending and ascending data.