Similarity Segmentation Approach For Sensor-Based Human Activity Recognition
The researchers attempted to enhance the segmentation method by proposing various techniques. However, most of them focus on each window’s features, and few consider the temporal relationships between the adjacent windows. Therefore, an analysis of the impact of window size on the performance of bas...
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| Format: | Thesis |
| Language: | English |
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2024
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| Online Access: | http://eprints.usm.my/62947/ http://eprints.usm.my/62947/1/Pages%20from%20ABDULRAHMAN%20M%20A%20BARAKA%20-%20TESIS.pdf |
| _version_ | 1848885126133448704 |
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| author | Baraka, Abdulrahman M. A. |
| author_facet | Baraka, Abdulrahman M. A. |
| author_sort | Baraka, Abdulrahman M. A. |
| building | USM Institutional Repository |
| collection | Online Access |
| description | The researchers attempted to enhance the segmentation method by proposing various techniques. However, most of them focus on each window’s features, and few consider the temporal relationships between the adjacent windows. Therefore, an analysis of the impact of window size on the performance of basic and transitional activity recognition is performed using a deep learning model. |
| first_indexed | 2025-11-15T19:17:38Z |
| format | Thesis |
| id | usm-62947 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:17:38Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-629472025-10-13T07:44:20Z http://eprints.usm.my/62947/ Similarity Segmentation Approach For Sensor-Based Human Activity Recognition Baraka, Abdulrahman M. A. QA75.5-76.95 Electronic computers. Computer science The researchers attempted to enhance the segmentation method by proposing various techniques. However, most of them focus on each window’s features, and few consider the temporal relationships between the adjacent windows. Therefore, an analysis of the impact of window size on the performance of basic and transitional activity recognition is performed using a deep learning model. 2024-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/62947/1/Pages%20from%20ABDULRAHMAN%20M%20A%20BARAKA%20-%20TESIS.pdf Baraka, Abdulrahman M. A. (2024) Similarity Segmentation Approach For Sensor-Based Human Activity Recognition. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Baraka, Abdulrahman M. A. Similarity Segmentation Approach For Sensor-Based Human Activity Recognition |
| title | Similarity Segmentation Approach For Sensor-Based Human Activity Recognition |
| title_full | Similarity Segmentation Approach For Sensor-Based Human Activity Recognition |
| title_fullStr | Similarity Segmentation Approach For Sensor-Based Human Activity Recognition |
| title_full_unstemmed | Similarity Segmentation Approach For Sensor-Based Human Activity Recognition |
| title_short | Similarity Segmentation Approach For Sensor-Based Human Activity Recognition |
| title_sort | similarity segmentation approach for sensor-based human activity recognition |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/62947/ http://eprints.usm.my/62947/1/Pages%20from%20ABDULRAHMAN%20M%20A%20BARAKA%20-%20TESIS.pdf |