MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart Environments
WiFi-based human sensing has emerged as a transformative technology for advancing sustainable living environments and promoting well-being by enabling non-intrusive and device-free monitoring of human behaviors. This offers significant potential in applications such as smart homes and sustainable ur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/1/6 |
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| author | Hamada Rizk Ahmed Elmogy Mohamed Rihan Hirozumi Yamaguchi |
| author_facet | Hamada Rizk Ahmed Elmogy Mohamed Rihan Hirozumi Yamaguchi |
| author_sort | Hamada Rizk |
| collection | DOAJ |
| description | WiFi-based human sensing has emerged as a transformative technology for advancing sustainable living environments and promoting well-being by enabling non-intrusive and device-free monitoring of human behaviors. This offers significant potential in applications such as smart homes and sustainable urban spaces and healthcare systems that enhance well-being and patient monitoring. However, current research predominantly addresses single-user scenarios, limiting its applicability in multi-user environments. In this work, we introduce “<i>MultiSenseX</i>”, a cutting-edge system leveraging a multi-label, multi-view Transformer-based architecture to achieve simultaneous localization and activity recognition in multi-occupant settings. By employing advanced preprocessing techniques and utilizing the Transformer’s self-attention mechanism, <i>MultiSenseX</i> effectively learns complex patterns of human activity and location from Channel State Information (CSI) data. This approach transcends traditional sequential methods, enabling accurate and real-time analysis in dynamic, multi-user contexts. Our empirical evaluation demonstrates <i>MultiSenseX</i>’s superior performance in both localization and activity recognition tasks, achieving remarkable accuracy and scalability. By enhancing multi-user sensing technologies, <i>MultiSenseX</i> supports the development of intelligent, efficient, and sustainable communities, contributing to SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) through safer, smarter, and more inclusive urban living solutions. |
| format | Article |
| id | doaj-art-f8ae99848fe0408197d5f2dd187070ff |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-f8ae99848fe0408197d5f2dd187070ff2025-01-24T13:17:22ZengMDPI AGAI2673-26882025-01-0161610.3390/ai6010006MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart EnvironmentsHamada Rizk0Ahmed Elmogy1Mohamed Rihan2Hirozumi Yamaguchi3Computers & Control Engineering Department, Tanta University, Tanta 31527, EgyptFaculty of Computer Engineering & Sciences, Prince Sattam Ibn Abdelaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Communications Engineering, University of Bremen, 28725 Bremen, GermanyGraduate School of Information Science and Technology, Osaka University, Suita 565-0871, JapanWiFi-based human sensing has emerged as a transformative technology for advancing sustainable living environments and promoting well-being by enabling non-intrusive and device-free monitoring of human behaviors. This offers significant potential in applications such as smart homes and sustainable urban spaces and healthcare systems that enhance well-being and patient monitoring. However, current research predominantly addresses single-user scenarios, limiting its applicability in multi-user environments. In this work, we introduce “<i>MultiSenseX</i>”, a cutting-edge system leveraging a multi-label, multi-view Transformer-based architecture to achieve simultaneous localization and activity recognition in multi-occupant settings. By employing advanced preprocessing techniques and utilizing the Transformer’s self-attention mechanism, <i>MultiSenseX</i> effectively learns complex patterns of human activity and location from Channel State Information (CSI) data. This approach transcends traditional sequential methods, enabling accurate and real-time analysis in dynamic, multi-user contexts. Our empirical evaluation demonstrates <i>MultiSenseX</i>’s superior performance in both localization and activity recognition tasks, achieving remarkable accuracy and scalability. By enhancing multi-user sensing technologies, <i>MultiSenseX</i> supports the development of intelligent, efficient, and sustainable communities, contributing to SDG 11 (Sustainable Cities and Communities) and SDG 3 (Good Health and Well-being) through safer, smarter, and more inclusive urban living solutions.https://www.mdpi.com/2673-2688/6/1/6wirelesshuman activity recognitionlocalizationAIoTsmart environmentsCSI |
| spellingShingle | Hamada Rizk Ahmed Elmogy Mohamed Rihan Hirozumi Yamaguchi MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart Environments AI wireless human activity recognition localization AIoT smart environments CSI |
| title | MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart Environments |
| title_full | MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart Environments |
| title_fullStr | MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart Environments |
| title_full_unstemmed | MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart Environments |
| title_short | MultiSenseX: A Sustainable Solution for Multi-Human Activity Recognition and Localization in Smart Environments |
| title_sort | multisensex a sustainable solution for multi human activity recognition and localization in smart environments |
| topic | wireless human activity recognition localization AIoT smart environments CSI |
| url | https://www.mdpi.com/2673-2688/6/1/6 |
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