A system for human activity recognition using WI-FI CSI on embedded device

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dc.contributor.authorArmenta García, Jesús Albany
dc.contributor.codirectorCaro Gutiérrez, Jesús
dc.contributor.directorGonzález Navarro, Félix Fernando
dc.coverage.placeofpublicationMexicali, Baja California.
dc.date.accessioned2026-04-19T21:39:50Z
dc.date.available2026-04-19T21:39:50Z
dc.date.created2025
dc.degree.deparmentUniversidad Autónoma de Baja California, Instituto de Ingeniería, Mexicali.
dc.degree.grantorTesis Doctorado / doctoral Thesis.
dc.degree.nameMaestría y Doctorado en Ciencias e Licenciatura
dc.description.abstractThis 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.
dc.format.extentRecurso en línea, 163 p.
dc.format.mimetypepdf
dc.identifier.urihttps://hdl.handle.net/20.500.12930/13684
dc.language.isospa
dc.publisherUniversidad Autónoma de Baja California.
dc.relation.urlhttps://drive.google.com/file/d/1h_rEWMm1V4xU7wOIjI7De-fvtkvrteeu/view?usp=sharing
dc.rightsopenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4
dc.subjectSistemas de comunicación inalámbrica||Tesis y disertaciones académicas Sistemas de comunicación inalámbrica: innovaciones tecnológicas||Tesis y disertaciones académicas Sistemas de comunicación inalámbrica: medidas de seguridad||Tesis y disertaciones académicas
dc.subject.lccTK5103.2 A75 2025
dc.titleA system for human activity recognition using WI-FI CSI on embedded device
dc.uabc.bibliographycNoteIncluye referencias bibliográficas.
dc.uabc.bilbiotecaMEXICALI
dc.uabc.identifier281845
dc.uabc.numInventarioMXL125977
dc.uabc.typeMaterialTESIS
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