Ingeniería en Sistemas, Electrónica e Industrial

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Author: search.filters.author.Guamán Egas, Jefferson IsmaelSubject: search.filters.subject.DETECCION DE CAIDAS

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    Sistema electrónico de notificación de emergencias basado en IoT para la asistencia médica a personas de la tercera edad.
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Ingeniería en Electrónica y Comunicaciones, 2023-03) Guamán Egas, Jefferson Ismael; Castro Martin, Ana Pamela
    In the present work, a system with IoT architecture is implemented that allows detecting and alerting falls in older adults, these events being of great incidence in this group of people; In addition, vital signs are monitored. A bracelet type device was designed with the ESP32 development board with the MPU6050 sensor for fall detection and the MAX30100 and MLX90614 sensors for monitoring heart rate, oxygen saturation and body temperature. For fall detection, models based on "Machine Learning" are created using the "open source" platform Edge Impulse. To create the model, 10 participants were drawn: 5 men and 5 women. Each participant made 5 types of falls, each fall was repeated 10 times, obtaining 50 for each participant, in a similar way activities of daily living carried out by an elderly person were implemented. For the mobile application, Flutter compatible with Android and IOS was brought, different widgets were programmed for the development of the graphical interface to build sensor data, user information, create alerts on the status of vital signs at an interval of time and to present the history of alerts. Dependencies such as "provider" were used to manage the state of the application, allowing control of connectivity and incoming messages through the MQTT protocol. To alert the caregiver of the elderly, push notifications based on Google's own "Firebase Cloud Messaging" architecture are used. An IoT device was obtained based on a microcontroller with the ability to: connect to the WiFi network to send data to a server; run the "Machine Learning" model for fall detection; monitor heart rate, oxygen saturation and body temperature through the wrist; and generate automatic alerts on the cell phone through the application.