A system for human activity recognition using WI-FI CSI on embedded device [recurso electrónico] / Jesús Albany Armenta García ; director, Félix Fernando González Navarro ; codirector, Jesús Caro Gutiérrez
Tipo de material:
TextoDetalles de publicación: Mexicali, Baja California, 2025Descripción: 1 recurso en línea, 163 p. ; il. col., gráficasTema(s): Sistemas 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 académicasClasificación LoC:TK5103.2 | A75 2025Recursos en línea: Tesis Digital
Nota de disertación: Tesis (Doctorado)--Universidad Autónoma de Baja California, Instituto de Ingeniería, Mexicali, 2025. Resumen: This 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.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Tesis | Biblioteca Central Mexicali | Colección de Tesis | TK5103.2 A75 2025 (Browse shelf(Abre debajo)) | 1 | Disponible | MXL125977 |
Maestría y Doctorado en Ciencias e Ingeniería
Tesis (Doctorado)--Universidad Autónoma de Baja California, Instituto de Ingeniería, Mexicali, 2025.
Incluye referencias bibliográficas
This 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.
