Paper Info
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.
Year
DOI
Venue
2016
10.3390/s16111715
SENSORS
Keywords
Field
DocType
artificial organic networks,artificial hydrocarbon networks,flexible human activity recognition,supervised machine learning,wearable sensors,flexibility
Activity recognition,Wearable computer,Artificial intelligence,Engineering,Classifier (linguistics),Machine learning,Area of interest
Journal
Volume
Issue
Citations 
16
11.0
1
PageRank 
References 
Authors
0.36
0
3
Name
Order
Citations
PageRank
Hiram E. Ponce12613.63
Luis Miralles Pechuán260.78
María de Lourdes Martínez-Villaseñor3268.35