Examinando por Autor "Armenta García, Jesús Albany"

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  • Miniatura
    Ítem
    A system for human activity recognition using WI-FI CSI on embedded device
    (Universidad Autónoma de Baja California., ) Armenta García, Jesús Albany; Caro Gutiérrez, Jesús; González Navarro, Félix Fernando
    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.
  • Miniatura
    Publicación
    Monitoring vital signs at rest while using channel state information of wi-fi signals and artificial intelligence tools /
    Armenta García, Jesús Albany; Ibarra Esquer, Jorge Eduardo dir.; González Navarro, Félix Fernando
    Breathing and heart rate are vital signs that might help identifying pathological conditions by its monitoring. This master’s thesis presents a system for monitoring breathing and heart rate, which combines con- ventional Channel State Information sensing approaches with Machine Learning techniques to provide a reliable monitoring. Also, a new sen- sitive subcarrier selection method, which is an important step for pro- cessing Channel State Information data, based on Hilbert Transform is presented. Along with the system’s description, this thesis provides the base theory for understanding each system’s component and the task that each component does. An exhaustive analysis was also performed and presented in order to understand Channel State Information data as well as the processing of data for vital signs monitoring. Results show that a reliable breathing rate monitoring can be achieved and raise questions about heart rate monitoring which are also answered in the same chapter.