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dc.contributor.authorPonce, Hiram
dc.contributor.authorMartinez-Villaseñor, Lourdes
dc.contributor.authorMiralles, Luis
dc.contributor.otherCampus Ciudad de Méxicoes
dc.identifier.citationPonce, H., Martinez-Villaseñor, L. y Miralles, L. (2016). A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks. Sensors, 16 (7). DOI:
dc.description.abstractHuman 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. © 2016 by the authors; licensee MDPI, Basel, Switzerland.en
dc.publisherMDPI AGen
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.rightsAcceso Abiertoes
dc.subjectArtificial intelligenceen
dc.subjectBehavioral researchen
dc.subjectLearning systemsen
dc.subjectPattern recognitionen
dc.subjectSupervised learningen
dc.subjectWearable technologyen
dc.subjectActivity recognitionen
dc.subjectClassification processen
dc.subjectHuman activity recognitionen
dc.subjectMachine learning methodsen
dc.subjectMachine learning techniquesen
dc.subjectNoise toleranceen
dc.subjectOrganic networksen
dc.subjectSupervised machine learningen
dc.subjectWearable sensorsen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.titleA novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networksen
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