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A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

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Pinto Bernal, Marìa J.
Cifuentes, Carlos A.
Perdomo, Oscar
Rincón Roncancio, Mónica
Múnera, Marcela
Abstract (Spanish)
El ejercicio físico contribuye al éxito de los programas de rehabilitación y rehabilitación Procesos asistidos a través de robots sociales. Sin embargo, la cantidad e intensidad del ejercicio necesario para obtener resultados positivos se desconocen. Se deben tener en cuenta varias consideraciones para su implementación en rehabilitación, como el seguimiento de la intensidad de los pacientes, fundamental para evitar situaciones extremas. Las condiciones de fatiga pueden causar complicaciones físicas y fisiológicas. El uso del aprendizaje automático Se han implementado modelos en la gestión de la fatiga, pero en la práctica están limitados debido a la falta de comprender cómo el desempeño de un individuo se deteriora con la fatiga; esto puede variar dependiendo de ejercicio físico, entorno y características del individuo. Como primer paso, este documento establece la base para un enfoque analítico de datos para gestionar la fatiga en las tareas de caminar. la propuesta El marco establece los criterios para la selección de una característica y un algoritmo de aprendizaje automático para la fatiga. manejo, clasificando cuatro estados diagnósticos de fatiga. A partir del marco propuesto y de la clasificador implementado, el modelo de bosque aleatorio presentó el mejor desempeño con un promedio precisión de ≥98% y puntuación F de ≥93%. Este modelo estaba compuesto por ≤16 características. Además, el El rendimiento de la predicción se analizó limitando los sensores utilizados de cuatro IMU a dos o incluso una IMU con una eficiencia general de ≥88%.
Abstract (English)
Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.
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URI: https://repositorio.escuelaing.edu.co/handle/001/3229
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