ArticleSensors (Basel, Switzerland)2021
A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
9 citing papers in PubMed, 43 citations in OpenAlex.
- Estimated sleep from an under-mattress device predicts next-day vigilance, working memory, and mental arithmetic performance.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Machine-Learning-Based Fatigue Trend Analysis on IMU Wearable Sensor Data from Construction Site Workers.Sensors (Basel, Switzerland) · 2025Article
- A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model.Sensors (Basel, Switzerland) · 2025Article
- Characterization of Muscle Fatigue Degree in Cyclical Movements Based on the High-Frequency Components of sEMG.Biomimetics (Basel, Switzerland) · 2025Article
- Walking Stability and Kinematic Variability Following Motor Fatigue Induced by Incline Treadmill Walking.Sensors (Basel, Switzerland) · 2025Article
- A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables.Scientific data · 2024Article
- Microfluidic Wearable Devices for Sports Applications.Micromachines · 2023Review
- Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.