A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework.
This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. The CNN-GRU successfully captures spatial and temporal dependencie...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0328181 |
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| _version_ | 1849228150623436800 |
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| author | Sarmad Sohaib Syed Mohsin Bokhari Muhammad Shafi Anas Alhashmi |
| author_facet | Sarmad Sohaib Syed Mohsin Bokhari Muhammad Shafi Anas Alhashmi |
| author_sort | Sarmad Sohaib |
| collection | DOAJ |
| description | This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. The CNN-GRU successfully captures spatial and temporal dependencies, while the MRF models the mutual relations of activities and locations by estimating their joint probability distribution. The new system was tested on a public smart home dataset with four activities (sitting, lying, walking, and standing) and four indoor locations (kitchen, bedroom, living room, and stairs). The hybrid framework obtained an accuracy of 95% for activity recognition and 93% for indoor localization with a combined activity-location classification accuracy of 81%. Such results confirm the ability of the system to provide robust predictions in real-world smart environments, make it highly suitable for healthcare and intelligent living applications, and is efficient and deployable in real-world scenarios, addressing the critical challenges of noisy and dynamic indoor environments. |
| format | Article |
| id | doaj-art-4f9fb95b89524adaac2553b8a45eeca0 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-4f9fb95b89524adaac2553b8a45eeca02025-08-23T05:31:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01208e032818110.1371/journal.pone.0328181A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework.Sarmad SohaibSyed Mohsin BokhariMuhammad ShafiAnas AlhashmiThis work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. The CNN-GRU successfully captures spatial and temporal dependencies, while the MRF models the mutual relations of activities and locations by estimating their joint probability distribution. The new system was tested on a public smart home dataset with four activities (sitting, lying, walking, and standing) and four indoor locations (kitchen, bedroom, living room, and stairs). The hybrid framework obtained an accuracy of 95% for activity recognition and 93% for indoor localization with a combined activity-location classification accuracy of 81%. Such results confirm the ability of the system to provide robust predictions in real-world smart environments, make it highly suitable for healthcare and intelligent living applications, and is efficient and deployable in real-world scenarios, addressing the critical challenges of noisy and dynamic indoor environments.https://doi.org/10.1371/journal.pone.0328181 |
| spellingShingle | Sarmad Sohaib Syed Mohsin Bokhari Muhammad Shafi Anas Alhashmi A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. PLoS ONE |
| title | A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. |
| title_full | A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. |
| title_fullStr | A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. |
| title_full_unstemmed | A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. |
| title_short | A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. |
| title_sort | novel approach for joint indoor localization and activity recognition using a hybrid cnn gru and mrf framework |
| url | https://doi.org/10.1371/journal.pone.0328181 |
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