Activity recognition and abnormality detection with the switching hidden semi-Markov model
This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which isan important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the ac...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society Press
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/10349 |
| _version_ | 1848746208686768128 |
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| author | Duong, Thi Bui, Hung H. Phung, Dinh Venkatesh, Svetha |
| author2 | Schnid, C. |
| author_facet | Schnid, C. Duong, Thi Bui, Hung H. Phung, Dinh Venkatesh, Svetha |
| author_sort | Duong, Thi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which isan important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model usingmultinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMMperforms better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model. |
| first_indexed | 2025-11-14T06:29:36Z |
| format | Conference Paper |
| id | curtin-20.500.11937-10349 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:29:36Z |
| publishDate | 2005 |
| publisher | IEEE Computer Society Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-103492022-10-20T04:24:51Z Activity recognition and abnormality detection with the switching hidden semi-Markov model Duong, Thi Bui, Hung H. Phung, Dinh Venkatesh, Svetha Schnid, C. Soatto, S. Tomasi, C. This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which isan important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model usingmultinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMMperforms better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model. 2005 Conference Paper http://hdl.handle.net/20.500.11937/10349 10.1109/CVPR.2005.61 IEEE Computer Society Press fulltext |
| spellingShingle | Duong, Thi Bui, Hung H. Phung, Dinh Venkatesh, Svetha Activity recognition and abnormality detection with the switching hidden semi-Markov model |
| title | Activity recognition and abnormality detection with the switching hidden semi-Markov model |
| title_full | Activity recognition and abnormality detection with the switching hidden semi-Markov model |
| title_fullStr | Activity recognition and abnormality detection with the switching hidden semi-Markov model |
| title_full_unstemmed | Activity recognition and abnormality detection with the switching hidden semi-Markov model |
| title_short | Activity recognition and abnormality detection with the switching hidden semi-Markov model |
| title_sort | activity recognition and abnormality detection with the switching hidden semi-markov model |
| url | http://hdl.handle.net/20.500.11937/10349 |