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Human Activity Recognition on Mobile Devices Using Artificial Hydrocarbon Networks

  • Hiram PonceEmail author
  • Guillermo González
  • Luis Miralles-Pechuán
  • Ma Lourdes Martínez-Villaseñor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10632)

Abstract

Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published.

Keywords

Artificial organic networks Human activity recognition Classification Machine learning Sensors Mobile 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hiram Ponce
    • 1
    Email author
  • Guillermo González
    • 1
  • Luis Miralles-Pechuán
    • 2
  • Ma Lourdes Martínez-Villaseñor
    • 1
  1. 1.Facultad de IngenieríaUniversidad PanamericanaMexico CityMexico
  2. 2.Centre for Applied Data Analytics Research (CeADAR)University College DublinDublin 4Ireland

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