Learning Human Activity From Visual Data Using Deep Learning
<p>Advances in wearable technologies have the ability to revolutionize and improve people's lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers...
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2021
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| _version_ | 1864513560230494208 |
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| author | Taha Alhersh (16888806) |
| author2 | Heiner Stuckenschmidt (16888809) Atiq Ur Rehman (8843024) Samir Brahim Belhaouari (9427347) |
| author2_role | author author author |
| author_facet | Taha Alhersh (16888806) Heiner Stuckenschmidt (16888809) Atiq Ur Rehman (8843024) Samir Brahim Belhaouari (9427347) |
| author_role | author |
| dc.creator.none.fl_str_mv | Taha Alhersh (16888806) Heiner Stuckenschmidt (16888809) Atiq Ur Rehman (8843024) Samir Brahim Belhaouari (9427347) |
| dc.date.none.fl_str_mv | 2021-07-26T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3099567 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Learning_Human_Activity_From_Visual_Data_Using_Deep_Learning/24038979 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Human-centred computing Machine learning Sensors Visualization Activity recognition Feature extraction Cameras Optical sensors Optical network units Human activity recognition Deep learning First-person vision |
| dc.title.none.fl_str_mv | Learning Human Activity From Visual Data Using Deep Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Advances in wearable technologies have the ability to revolutionize and improve people's lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers' bodies and their immediate environment. Human activities recognition, which involves the use of various body sensors and modalities either separately or simultaneously, is one of the most important areas of wearable technology development. In real-life scenarios, the number of sensors deployed is dictated by practical and financial considerations. In the research for this article, we reviewed our earlier efforts and have accordingly reduced the number of required sensors, limiting ourselves to first-person vision data for activities recognition. Nonetheless, our results beat state of the art by more than 4% of F1 score.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3099567" target="_blank">https://dx.doi.org/10.1109/access.2021.3099567</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_abcb4f12fd483b0d70ee811f2e3e7f3e |
| identifier_str_mv | 10.1109/access.2021.3099567 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24038979 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Learning Human Activity From Visual Data Using Deep LearningTaha Alhersh (16888806)Heiner Stuckenschmidt (16888809)Atiq Ur Rehman (8843024)Samir Brahim Belhaouari (9427347)Information and computing sciencesComputer vision and multimedia computationHuman-centred computingMachine learningSensorsVisualizationActivity recognitionFeature extractionCamerasOptical sensorsOptical network unitsHuman activity recognitionDeep learningFirst-person vision<p>Advances in wearable technologies have the ability to revolutionize and improve people's lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers' bodies and their immediate environment. Human activities recognition, which involves the use of various body sensors and modalities either separately or simultaneously, is one of the most important areas of wearable technology development. In real-life scenarios, the number of sensors deployed is dictated by practical and financial considerations. In the research for this article, we reviewed our earlier efforts and have accordingly reduced the number of required sensors, limiting ourselves to first-person vision data for activities recognition. Nonetheless, our results beat state of the art by more than 4% of F1 score.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3099567" target="_blank">https://dx.doi.org/10.1109/access.2021.3099567</a></p>2021-07-26T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3099567https://figshare.com/articles/journal_contribution/Learning_Human_Activity_From_Visual_Data_Using_Deep_Learning/24038979CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240389792021-07-26T00:00:00Z |
| spellingShingle | Learning Human Activity From Visual Data Using Deep Learning Taha Alhersh (16888806) Information and computing sciences Computer vision and multimedia computation Human-centred computing Machine learning Sensors Visualization Activity recognition Feature extraction Cameras Optical sensors Optical network units Human activity recognition Deep learning First-person vision |
| status_str | publishedVersion |
| title | Learning Human Activity From Visual Data Using Deep Learning |
| title_full | Learning Human Activity From Visual Data Using Deep Learning |
| title_fullStr | Learning Human Activity From Visual Data Using Deep Learning |
| title_full_unstemmed | Learning Human Activity From Visual Data Using Deep Learning |
| title_short | Learning Human Activity From Visual Data Using Deep Learning |
| title_sort | Learning Human Activity From Visual Data Using Deep Learning |
| topic | Information and computing sciences Computer vision and multimedia computation Human-centred computing Machine learning Sensors Visualization Activity recognition Feature extraction Cameras Optical sensors Optical network units Human activity recognition Deep learning First-person vision |