Detección de actividades mediante modelos ocultos de Markov jerárquicos
DOI:
https://doi.org/10.17979/ja-cea.2024.45.10778Palabras clave:
Métodos bayesianos, Modelos de series temporales, Control basado en eventos, Control basado en el conocimiento, Informática basada en el ser humanoResumen
En los últimos años, el interés en el desarrollo de tecnologías avanzadas para detectar y analizar las actividades y los patrones de comportamiento de las personas mayores en sus hogares ha crecido, con el objetivo de mejorar su salud y bienestar. Este artículo explora el uso de Modelos Ocultos de Markov Jerárquicos (HHMM) para abordar estos desafíos. Los HHMM permiten la representación y análisis de secuencias temporales de actividades, capturando tanto variaciones a corto plazo como estructuras jerárquicas complejas en los comportamientos humanos. Se presenta una arquitectura que incluye sensores no intrusivos y un robot social para la monitorización y atención de personas mayores en sus viviendas. Se han expuesto los principios matemáticos del modelo HHMM y la ejecución de sus algoritmos de predicción. El sistema ha sido validado en cinco viviendas reales durante dos meses, proporcionando como resultado los patrones de comportamiento de los usuarios con el fin de detectar las posibles anomalías que pueden ocurrir.
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Derechos de autor 2024 Raúl Gomez Ramos, Jaime Duque-Domingo, Eduardo Zalama, Jaime Gómez-García-Bermejo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
