Monitoring vital signs at rest while using channel state information of wi-fi signals and artificial intelligence tools / [recurso electrónico] / Jesús Albany Armenta García ; director, Félix Fernando González Navarro; codirector, Jorge Eduardo Ibarra Esquer
Tipo de material:
TextoDetalles de publicación: Mexicali, Baja California, 2022Descripción: 1 recurso en línea, 97 p. ; il. col., gráficas, fotsTema(s): Inteligencia artificial -- Tesis y disertaciones académicas. -- lembClasificación LoC:Q335 | A75 2022Recursos en línea: Tesis Digital.
Nota de disertación: Tesis (Maestría) -- Universidad Autónoma de Baja California. Instituto de Ingeniería, Mexicali, 2022 Resumen: 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.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Tesis | Biblioteca Central Mexicali | Colección de Tesis | Q335 A75 2022 (Browse shelf(Abre debajo)) | 1 | Disponible | MXL123567 |
Maestría y Doctorado en Ciencias e Ingeniería
Tesis (Maestría) -- Universidad Autónoma de Baja California. Instituto de Ingeniería, Mexicali, 2022
Incluye referencias bibliográficas
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.
