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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-09, Vol.21 (19), p.6401
• Main Authors: Pinto-Bernal, Maria J, Cifuentes, Carlos A, Perdomo, Oscar, Rincón-Roncancio, Monica, Múnera, Marcela
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Chronic fatigue syndrome; Classification; classification models; Electromyography; EMG; Exercise intensity; Fatigue - diagnosis; Human performance; Humans; inertial measurement units; Kinematics; Machine Learning; Measurement techniques; Patients; Physical exercise; Physical fitness; Physiology; Rehabilitation; Sensors; Walking; walking rehabilitation; Wearable Electronic Devices
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21196401

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%.