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Activity Recognition from Physiological Data using Conditional Random Fields

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dc.contributor.author Chieu, Hai Leong
dc.contributor.author Lee, Wee Sun
dc.contributor.author Kaelbling, Leslie P.
dc.date.accessioned 2005-12-14T18:52:36Z
dc.date.available 2005-12-14T18:52:36Z
dc.date.issued 2006-01
dc.identifier.uri http://hdl.handle.net/1721.1/30197
dc.description.abstract We describe the application of conditional random fields (CRF) to physiological data modeling for the application of activity recognition. We use the data provided by the Physiological Data Modeling Contest (PDMC), a Workshop at ICML 2004. Data used in PDMC are sequential in nature: they consist of physiological sessions, and each session consists of minute-by-minute sensor readings. We show that linear chain CRF can effectively make use of the sequential information in the data, and, with Expectation Maximization, can be trained on partially unlabeled sessions to improve performance. We also formulate a mixture CRF to make use of the identities of the human subjects to further improve performance. We propose that mixture CRF can be used for transfer learning, where models can be trained on data from different domains. During testing, if the domain of the test data is known, it can be used to instantiate the mixture node, and when it is unknown (or when it is a completely new domain), the marginal probabilities of the labels over all training domains can still be used effectively for prediction. en
dc.description.sponsorship Singapore-MIT Alliance (SMA) en
dc.format.extent 155719 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Computer Science (CS) en
dc.subject Machine Learning en
dc.subject Graphical Models en
dc.subject Applications en
dc.title Activity Recognition from Physiological Data using Conditional Random Fields en
dc.type Article en


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