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A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

Hiram Ponce1, Luis Miralles-Pechuán2, María de Lourdes Martínez-Villaseñor3

  • 1Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico. hponce@up.edu.mx.

Sensors (Basel, Switzerland)
|October 30, 2016
PubMed
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Artificial hydrocarbon networks offer a flexible solution for human activity recognition systems. This novel approach demonstrates effectiveness in both user-dependent and user-independent scenarios, advancing wearable sensor technology.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Wearable sensor technology has significantly advanced, driving interest in physical activity recognition.
  • Existing human activity recognition systems face challenges, particularly in flexibility.
  • Proactive and personalized services rely on accurate monitoring of personal activities and behavior.

Purpose of the Study:

  • To introduce artificial hydrocarbon networks (AHNs) as a novel and flexible approach for human activity recognition.
  • To evaluate the performance of AHN-based classifiers in activity recognition.
  • To address the challenge of flexibility in human activity recognition systems.

Main Methods:

  • Development of a human activity recognition system utilizing artificial hydrocarbon networks.
Keywords:
artificial hydrocarbon networksartificial organic networksflexibilityflexible human activity recognitionsupervised machine learningwearable sensors

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  • Experimentation designed for both user-dependent and user-independent case scenarios.
  • Performance evaluation of the AHN-based classifier.

    • Main Results:

    • Artificial hydrocarbon networks demonstrate significant flexibility in activity recognition.
    • The AHN classifier proved effective in user-dependent scenarios.
    • The AHN classifier also showed effectiveness in user-independent scenarios.

    Conclusions:

    • Artificial hydrocarbon networks provide a flexible and effective method for human activity recognition.
    • AHNs are suitable for building human activity recognition systems using both user-dependent and user-independent strategies.
    • This research contributes a novel technique to overcome flexibility limitations in current activity recognition systems.