Wearable Sensors for Monitoring Exercise and Fatigue Estimation in Rehabilitation

Pinto-Bernal, Maria J.; Aguirre, Andres; Cifuentes, Carlos A.; Múnera, Marcela

doi:10.1201/9780429296864-6

Cited by 2 publications

(1 citation statement)

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“…Additionally, it can lower the risk of heart disease, diabetes, cancer, stroke, reduce the risk of orthopedic problems, recover mobility of limbs, strengthen the immune defense (influenza), among others [ 4 , 5 , 6 , 7 ]. In this context, physical exercise has the potential to affect people’s health conditions in many ways; hence, the American Health Association established physical exercise as one of the main components of improving people’s health and decreasing morbidity and mortality levels [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Pinto-Bernal

Cifuentes

Perdomo

et al. 2021

Sensors

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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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Section: Introductionmentioning

confidence: 99%

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

Pinto-Bernal

Cifuentes

Perdomo

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables

Yasar,

Sica,

O’Flynn

et al. 2024

Sci Data

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Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.

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Wearable Sensors for Monitoring Exercise and Fatigue Estimation in Rehabilitation

Cited by 2 publications

References 0 publications

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

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

A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables

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