Hidden Markov Mined Activity Model for Human Activity Recognition
Object-usage-based human activity recognition systems require activity data for learning. Acquiring such data from the real world is expensive and time consuming. To overcome such difficulties, the exploitation of web activity data is gaining popularity. However, due to a lack of much real-world inf...
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| Format: | Article |
| Language: | English |
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Wiley
2014-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/949175 |
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| _version_ | 1849473033392095232 |
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| author | A. M. Jehad Sarkar |
| author_facet | A. M. Jehad Sarkar |
| author_sort | A. M. Jehad Sarkar |
| collection | DOAJ |
| description | Object-usage-based human activity recognition systems require activity data for learning. Acquiring such data from the real world is expensive and time consuming. To overcome such difficulties, the exploitation of web activity data is gaining popularity. However, due to a lack of much real-world information in such data, existing activity models are not suitable for web data. In this paper, we propose a hidden Markov model- (HMM-) based activity model specially designed to use web activity data for activity recognition. It utilizes a sequence of object-usage information for activity recognition. We also propose a web activity data mining algorithm for this model. It is extremely fast and efficient in comparison with the existing algorithms. We perform three experiments to validate the proposed model. We show that the model can be effectively utilized by an activity recognition system. |
| format | Article |
| id | doaj-art-d9637a9c012e46d38716d30871bc8fd8 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-d9637a9c012e46d38716d30871bc8fd82025-08-20T03:24:17ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-03-011010.1155/2014/949175949175Hidden Markov Mined Activity Model for Human Activity RecognitionA. M. Jehad SarkarObject-usage-based human activity recognition systems require activity data for learning. Acquiring such data from the real world is expensive and time consuming. To overcome such difficulties, the exploitation of web activity data is gaining popularity. However, due to a lack of much real-world information in such data, existing activity models are not suitable for web data. In this paper, we propose a hidden Markov model- (HMM-) based activity model specially designed to use web activity data for activity recognition. It utilizes a sequence of object-usage information for activity recognition. We also propose a web activity data mining algorithm for this model. It is extremely fast and efficient in comparison with the existing algorithms. We perform three experiments to validate the proposed model. We show that the model can be effectively utilized by an activity recognition system.https://doi.org/10.1155/2014/949175 |
| spellingShingle | A. M. Jehad Sarkar Hidden Markov Mined Activity Model for Human Activity Recognition International Journal of Distributed Sensor Networks |
| title | Hidden Markov Mined Activity Model for Human Activity Recognition |
| title_full | Hidden Markov Mined Activity Model for Human Activity Recognition |
| title_fullStr | Hidden Markov Mined Activity Model for Human Activity Recognition |
| title_full_unstemmed | Hidden Markov Mined Activity Model for Human Activity Recognition |
| title_short | Hidden Markov Mined Activity Model for Human Activity Recognition |
| title_sort | hidden markov mined activity model for human activity recognition |
| url | https://doi.org/10.1155/2014/949175 |
| work_keys_str_mv | AT amjehadsarkar hiddenmarkovminedactivitymodelforhumanactivityrecognition |