Classify Mental Stress Levels With Privacy-Preserving Machine Learning

Abstract

The growing need for mental stress detection in the workplace has prompted the exploration of machine-learning solutions. Nevertheless, traditional centralized methods often encounter critical data privacy issues, especially when dealing with sensitive physiological signals. To address this, we introduced a privacy-preserving mental stress detection framework utilizing federated learning, focusing on human-robot collaboration scenarios. We first developed classifiers employing traditional centralized algorithms including SVM, multilayer perceptron, random forest, and Naïve Bayes, followed by implementing a federated SVM classifier. These classifiers utilize multimodal physiological features to distinguish between relaxed, low-level stressed, and high-level stressed states. Comparative analysis regarding precision, recall, and F1-score was conducted to evaluate the performance of the federated learning model against centralized models. The results demonstrate that federated learning not only offers comparable accuracy to centralized methods but also ensures the protection of sensitive data, making it a valuable approach in scenarios where data privacy is paramount.