Small data, big challenges: pitfalls and strategies for machine learning in fatigue detection

This research addresses the pitfalls and strategies for machine learning with small data sets in the context of sensor-based fatigue detection. It is shown that many existing studies in this area rely on small data sets and that classification results can vary considerably depending on the evaluation method. Our analysis is based on a study with 46 subjects performing multiple sets of squat exercises in a laboratory setting. Data from ratings of perceived exertion, inertial measurement units, and pose estimation were used to train and compare different classifiers. Our findings suggest that commonly used evaluation methods, such as leave-one-subject-out, should be used with caution and may not lead to generalizable classifiers. Furthermore, challenges related to imbalanced data and oversampling are discussed.