Insightful stress detection from physiology modalities using Learning Vector Quantization

Stress in daily life can lead to severe conditions as burn-out and depression and has a major impact on society. Being able to measure mental stress reliably opens up the ability to intervene in an early stage. We performed a large-scale study in which skin conductance, respiration and electrocardiogram were measured in semi-controlled conditions. Using Learning Vector Quantization techniques, we obtained up to 88% accuracy in the classification task to separate stress from relaxation. Relevance learning was used to identify the most informative features, indicating that most information is embedded in the cardiac signals. In addition to commonly used features, we also explored various novel features, of which the very-high frequency band of the power spectrum was found to be a very relevant addition.