Resumen
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 ?MultiSenseX?, 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, MultiSenseX 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 MultiSenseX?s superior performance in both localization and activity recognition tasks, achieving remarkable accuracy and scalability. By enhancing multi-user sensing technologies, MultiSenseX 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.