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Descripción
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| This paper presents an human sensing (HS) system based on Hidden Markov Models (HMMs) for classifying physical activities: walking, walking-upstairs, walking-downstairs, sitting, standing and lying down. The system includes a feature extractor (developed by the authors and presented in a previous work), an HMMs training module and an HAR module. All experiments have been done using a publicly available dataset named UCI Human Activity Recognition Using Smartphones. The final results using HMMs obtain comparable results to other recognition methods. Some improvements have been obtained when considering a discriminative HMM training procedure. The best result obtains an activity recognition error rate (ARER) of 2.5%. This work is focused on independent activity recognition and extends other works from the same authors focused on activity segmentation and feature extraction. | |
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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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Ieee Instrumentation & Measurement Magazine |
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ISSN
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1094-6969 |
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Factor de impacto JCR
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0,759 |
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Información de impacto
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Volumen
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19 |
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DOI
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10.1109/MIM.2016.7777649 |
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Número de revista
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6 |
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Desde la página
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27 |
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Hasta la página
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31 |
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Mes
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DICIEMBRE |
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Ranking
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Journal Rank in Category 179/257; Quartile in Category Q3 |