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Sensors 2016, 16(7), 1033; doi:10.3390/s16071033

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

Faculty of Engineering, Universidad Panamericana, Mexico City 03920, Mexico
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Author to whom correspondence should be addressed.
Academic Editors: Vladimir Villarreal and Carmelo R. García
Received: 31 March 2016 / Revised: 22 June 2016 / Accepted: 24 June 2016 / Published: 5 July 2016
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
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Abstract

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods. View Full-Text
Keywords: artificial organic networks; artificial hydrocarbon networks; robust human activity recognition; supervised machine learning; wearable sensors; noise tolerance artificial organic networks; artificial hydrocarbon networks; robust human activity recognition; supervised machine learning; wearable sensors; noise tolerance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ponce, H.; Martínez-Villaseñor, M.L.; Miralles-Pechuán, L. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks. Sensors 2016, 16, 1033.

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