Evaluating Muscle Fatigue With Non-Invasive Approaches: A Review of Methods, Metrics, and Implications.

An accurate and reliable assessment of muscle fatigue is crucial for understanding its underlying mechanisms, monitoring training adaptations, evaluating rehabilitation progress, and optimizing performance in sports and occupational settings. Over the years, numerous methods and metrics have been developed to quantify and characterize muscle fatigue. This paper comprehensively reviews the various assessment techniques used to measure muscle fatigue, encompassing physiological and functional perspectives based on questionnaires, biosignals, and robotics interfaces.

Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods.

Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the assumption of linearity, might potentially provide unsatisfactory results in terms of false alarms and missed detections. In recent years, many authors have proposed machine learning (ML) techniques to improve fault diagnosis performance to mitigate this problem.

A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation.

Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals.