title: A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.
abstract: Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.
authors: Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico. hponce@up.edu.mx.
authors: Hiram
authors: H
authors: Ponce
authors: Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico. lmiralles@up.edu.mx.
authors: Luis
authors: L
authors: Miralles-Pechuán
authors: Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico. lmartine@up.edu.mx.
authors: María de Lourdes
authors: ML
authors: Martínez-Villaseñor
mesh_terms: D000465:Algorithms; D015374:Biosensing Techniques / Q000379:methods*; D015444:Exercise; D006801:Humans; D006838:Hydrocarbons / Q000032:analysis*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis
chemical_list: D006838:Hydrocarbons
keywords: artificial hydrocarbon networks; artificial organic networks; flexibility; flexible human activity recognition; supervised machine learning; wearable sensors