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Descripción
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| This paper describes the development of a Human Activity Recognition and Segmentation (HARS) system based on Hidden Markov Models (HMMs). This system uses inertial signals from a smartphone to recognize and segment six different physical activities: walking, walking-upstairs, walking-downstairs, sitting, standing and lying down. All the experiments have been done using a publicly available dataset called UCI Human Activity Recognition Using Smartphones. The developed system improves the results obtained on this dataset in previous works. The main contribution of this paper is the incorporation of an Activity Sequence Model. The best results show an Activity Segmentation Error Rate of 2.1%. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Pervasive And Mobile Computing |
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ISSN
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1574-1192 |
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Factor de impacto JCR
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1,719 |
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Información de impacto
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Volumen
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30 |
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DOI
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doi:10.1016/j.pmcj.2016.01.004 |
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Número de revista
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34 |
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Desde la página
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84 |
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Hasta la página
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96 |
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Mes
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AGOSTO |
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Ranking
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Journal Rank in Category 40/143; Quartile in Category Q2 |