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Improving Human Activity Recognition and its Application in Early Stroke Diagnosis

José R. Villar

Computer Science Department, University of Oviedo, ETSIMO, Oviedo, Asturias 33005, Spain

Corresponding author.

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Silvia González

Instituto Tecnológico de Castilla y León c/López Bravo 70 Burgos, Burgos 09001, Spain

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Javier Sedano

Instituto Tecnológico de Castilla y León c/López Bravo 70 Burgos, Burgos 09001, Spain

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Camelia Chira

Computer Science Department, Tech. University of Cluj-Napoca, 28 Gh. Baritiu Street, 400027 Cluj-Napoca, Romania

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, and

Jose M. Trejo-Gabriel-Galan

Neurology Department of the Burgos' Hospital, Burgos, Spain

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https://doi.org/10.1142/S0129065714500361Cited by:62 (Source: Crossref)

Abstract

The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

Keywords:

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