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Activity recognition from physiological data using conditional random fields (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Kaelbling, Leslie Pack Lee, Wee Sun Chieu, Hai Leong |
| Abstract | 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 minuteby-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. |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Training Domain Sequential Information Activity Recognition Mixture Crf Physiological Data Transfer Learning Different Domain Marginal Probability New Domain Conditional Random Field Minuteby-minute Sensor Reading Physiological Session Mixture Node Chain Crf Expectation Maximization Test Data Human Subject Physiological Data Modeling Contest Unlabeled Session |
| Content Type | Text |
