An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This paper presents a new unsupervised...
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Full title
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
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TN_cdi_proquest_journals_2085772179
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2085772179
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E-ISSN
2331-8422
DOI
10.48550/arxiv.1312.6965
