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Accelerometry-Based Classification of Human Activities Using Markov Modeling
Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Au...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166724/ https://www.ncbi.nlm.nih.gov/pubmed/21904542 http://dx.doi.org/10.1155/2011/647858 |
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author | Mannini, Andrea Sabatini, Angelo Maria |
author_facet | Mannini, Andrea Sabatini, Angelo Maria |
author_sort | Mannini, Andrea |
collection | PubMed |
description | Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series. |
format | Online Article Text |
id | pubmed-3166724 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-31667242011-09-08 Accelerometry-Based Classification of Human Activities Using Markov Modeling Mannini, Andrea Sabatini, Angelo Maria Comput Intell Neurosci Research Article Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series. Hindawi Publishing Corporation 2011 2011-09-04 /pmc/articles/PMC3166724/ /pubmed/21904542 http://dx.doi.org/10.1155/2011/647858 Text en Copyright © 2011 A. Mannini and A. M. Sabatini. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Research Article Mannini, Andrea Sabatini, Angelo Maria Accelerometry-Based Classification of Human Activities Using Markov Modeling |
title | Accelerometry-Based Classification of Human Activities Using Markov Modeling |
title_full | Accelerometry-Based Classification of Human Activities Using Markov Modeling |
title_fullStr | Accelerometry-Based Classification of Human Activities Using Markov Modeling |
title_full_unstemmed | Accelerometry-Based Classification of Human Activities Using Markov Modeling |
title_short | Accelerometry-Based Classification of Human Activities Using Markov Modeling |
title_sort | accelerometry-based classification of human activities using markov modeling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166724/ https://www.ncbi.nlm.nih.gov/pubmed/21904542 http://dx.doi.org/10.1155/2011/647858 |
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