A highly-wearable single–channel instrument, conceived with off-the-shelf components and dry electrodes, is proposed for detecting human stress in real time by electroencephalography (EEG). The instrument exploits EEG robustness to movement artifacts with respect to other biosignals for stress assessment. The single-channel differential measurement aims at analyzing the frontal asymmetry, a well-claimed EEG feature for stress assessment. The instrument was characterized metrologically on human subjects. As triple metrological references, standardized stress tests, observational questionnaires given by psychologists, and performance measurements were exploited. Four standard machine learning classifiers (SVM, k-NN, Random Forest, and ANN), trained on 50% of the data set, reached more than 90% accuracy in classifying each 2-s epoch of EEG acquired from stressed subjects.

Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, there is growing evidence in the literature that ML approaches are not always used appropriately, leading to incorrect and sometimes overly optimistic results. One reason for this inappropriate use of ML may be the increasing availability of machine learning tools, leading to what we call the “push the button” approach. While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation. In particular, this paper addresses a critical issue in ML, known as data leakage, where unintended information contaminates the training data, impacting model performance evaluation. Indeed, crucial steps in ML pipeline can be inadvertently overlooked, leading to optimistic performance estimates that may not hold in real-world scenarios. The discrepancy between evaluated and actual performance on new data is a significant concern. In particular, this paper categorizes data leakage in ML, discussing how certain conditions can propagate through the ML approach workflow. Furthermore, it explores the connection between data leakage and the specific task being addressed, investigates its occurrence in Transfer Learning framework, and compares standard inductive ML with transductive ML paradigms. The conclusion summarizes key findings, emphasizing the importance of addressing data leakage for robust and reliable ML applications considering tasks and generalization goals.

In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually defined to detect anomalous data. However, this process could be made automatic using machine learning methods. This paper proposes a semi-supervised method with a data-driven approach to detect structural anomalies. The methodology consists of: 1) a Variational Autoencoder (VAE) to approximate undamaged data distribution and 2) a One-Class Support Vector Machine (OC-SVM) to discriminate different health conditions using damage-sensitive features extracted from VAE’s signal reconstruction. The method is applied to a scale steel structure that was tested in nine damage scenarios by IASC-ASCE Structural Health Monitoring Task Group.