Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition

  • Hiram PonceEmail author
  • María de Lourdes Martínez-Villaseñor
  • Luis Miralles-Pechúan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9454)


In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.


Human activity recognition Artificial organic networks Artificial hydrocarbon networks Wearable sensors Supervised learning Classification 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hiram Ponce
    • 1
    Email author
  • María de Lourdes Martínez-Villaseñor
    • 1
  • Luis Miralles-Pechúan
    • 1
  1. 1.Universidad Panamericana Campus MéxicoMexico, D.F.Mexico

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