EAI Endorsed Transactions on Context-aware Systems and Applications 18(13): e3

Research Article

Towards an Efficient Implementation of Human Activity Recognition for Mobile Devices

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  • @ARTICLE{10.4108/eai.14-3-2018.154340,
        author={Ilham Amezzane and Youssef Fakhri and Mohamed El Aroussi and Mohamed Bakhouya},
        title={Towards an Efficient Implementation of Human Activity Recognition for Mobile Devices},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={18},
        number={13},
        publisher={EAI},
        journal_a={CASA},
        year={2018},
        month={3},
        keywords={human activity recognition, smartphone sensors, feature selection.},
        doi={10.4108/eai.14-3-2018.154340}
    }
    
  • Ilham Amezzane
    Youssef Fakhri
    Mohamed El Aroussi
    Mohamed Bakhouya
    Year: 2018
    Towards an Efficient Implementation of Human Activity Recognition for Mobile Devices
    CASA
    EAI
    DOI: 10.4108/eai.14-3-2018.154340
Ilham Amezzane1,*, Youssef Fakhri1, Mohamed El Aroussi1, Mohamed Bakhouya2
  • 1: Ibn Tofail University, Kenitra, Morocco.
  • 2: International University of Rabat, Sala Aljadida, Moroco
*Contact email: ilhammaj@gmail.com

Abstract

The availability of diverse and powerful sensors embedded in modern Smartphones/mobile devices has created exciting opportunities for developing context-aware applications. Although there is good capacity for collecting and classifying human activity data with such devices, data pre-processing and model building techniques that achieve this goal are required to operate while meeting hardware resource constraints, particularly for real-time applications. In this paper, we present a comparison study for HAR exploiting feature selection approaches to reduce the computation and training time needed for the discrimination of targeted activities while maintaining significant accuracy. We validated our approach on a publicly available dataset. Results show that Recursive Feature Elimination method combined with Radial Basis Function Support Vector Machine classifier offered the best tradeoff between training time/recognition performance.