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

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%.

• Autores: Pinto Bernal, Marìa J.; Cifuentes, Carlos A.; Perdomo, Oscar; Rincón Roncancio, Mónica; Múnera, Marcela
• Tipo de recurso: Article of journal
• Fecha de publicación: 2021
• Institución: Escuela Colombiana de Ingeniería Julio Garavito
• Repositorio: Repositorio Institucional ECI
• Idioma: eng
• OAI Identifier: oai:repositorio.escuelaing.edu.co:001/3229
• Acceso en línea: https://repositorio.escuelaing.edu.co/handle/001/3229; https://repositorio.escuelaing.edu.co/
• Palabra clave: Análisis de datos fácticos; Factual data analysis; Fatiga; Fatigue; Programas de rehabilitación; Rehabilitation programs; Aprendizaje automático (Inteligencia artificial); Machine learning; fatigue diagnosis; classification models; Inertial measurement units; EMG; Physical exercise; Diagnóstico de fatiga; Modelos de clasificación; Unidades de medida inerciales; Ejercicio fisico
• Rights: closedAccess
• License: http://purl.org/coar/access_right/c_14cb