000			03559nam a22002897a 4500
003			MX-MeUAM
005			20251203062218.0
008			251202s2025 mx ||||fo||d| 00| 0 spa d
040			_cMX-MeUAM _bspa _aMX-MeUAM
050		4	_aTK5103.2 _bA75 2025
100	1		_aArmenta García, Jesús Albany _927072
245	1	0	_aA system for human activity recognition using WI-FI CSI on embedded device _h[recurso electrónico] / _cJesús Albany Armenta García ; director, Félix Fernando González Navarro ; codirector, Jesús Caro Gutiérrez
260			_aMexicali, Baja California, _c2025
300			_a1 recurso en línea, 163 p. ; _bil. col., gráficas
500			_aMaestría y Doctorado en Ciencias e Ingeniería
502			_aTesis (Doctorado)--Universidad Autónoma de Baja California, Instituto de Ingeniería, Mexicali, 2025.
504			_aIncluye referencias bibliográficas
520			_aThis thesis addresses the limitations of current safety alert systems that rely on wearable sensors by proposing a novel Human Activity Recognition system based on Wi-Fi Channel State Information (CSI) and designed following the Edge Computing paradigm, where data processing occurs directly on the device responsible for data collection. The research is structured in three phases. First, an Structured Literature Review (SLR) was conducted to assess state-of-the-art methods for Wi-Fi CSI collection and processing, re- vealing two critical gaps: the lack of a user-friendly, high-performance CSI collection tool and the absence of edge-based implementations for near real-time Wi-Fi sensing with low-cost devices. To address these gaps, this work first introduces the ESP32 CSI Web Collecting Tool, a tool for collecting Wi-Fi CSI from ESP32 devices. Experimental results demonstrate that the proposed tool outperforms existing ESP32-based solutions, achieving a packet rate of up to 85 packets per second (compared to 30 packets per second in prior tools) when transmitting CSI data to a computer via a USB port. Addition- ally, the tool supports local data storage via an SD card and real-time transmission to external devices through GPIO pins, enhancing its versatility for edge applications. Leveraging this tool, a lightweight Deep Learning model was opti- mized for deployment on the resource-constrained ESP32 microcon- troller. By considering the hardware limitations of the implemen- tation device, the model achieves an overall classification accuracy of 90.65% with an inference time of 232 ms, enabling near real-time functioning at the edge. The contributions of this work include a high-performance, open- source CSI collection tool for the ESP32 platform and the first known implementation of an embedded system leveraging Deep Learning model for Wi-Fi HAR on an ESP32 device, which demonstrates the feasibility of near real-time, device-free safety monitoring.
650		4	_aSistemas de comunicación inalámbrica _vTesis y disertaciones académicas
650		4	_aSistemas de comunicación inalámbrica: innovaciones tecnológicas _vTesis y disertaciones académicas
650		4	_aSistemas de comunicación inalámbrica: medidas de seguridad _vTesis y disertaciones académicas
700	1		_aGonzález Navarro, Félix Fernando _914607 _edir.
700	1		_aCaro Gutiérrez, Jesús _918691 _ecodir.
710	2		_aUniversidad Autónoma de Baja California. _bInstituto de Ingeniería _93321
856	4		_uhttps://drive.google.com/file/d/1h_rEWMm1V4xU7wOIjI7De-fvtkvrteeu/view?usp=sharing _zTesis Digital
942			_cTESIS
999			_c281845 _d281844