Model Adaptation and Personalization for Physiological Stress Detection

Stress and accompanying physiological responses can occur when everyday emotional, mental and physical challenges exceed one's ability to cope. A long-term exposure to stressful situations can have negative health consequences, such as increased risk of cardiovascular diseases and immune system disorder. It is also shown to adversely affect productivity, wellbeing, and self-confidence, which can lead to social and economic inequality. Hence, a timely stress recognition can contribute to better strategies for its management and prevention in the future. Stress can be detected from multimodal physiological signals (e.g. skin conductance and heart rate) using well-trained models. However, these models need to be adapted to a new target domain and personalized for each test subject. In this paper, we propose a deep reconstruction classification network and multi-task learning (MTL) for domain adaption and personalization of stress recognition models. The domain adaption is achieved via a hybrid model consisting of temporal convolutional and recurrent layers that perform shared feature extraction through supervised source label predictions and unsupervised target data reconstruction. Furthermore, MTL based neural network approach with hard parameter sharing of mutual representation and task-specific layers is utilized to acquire personalized models. The proposed methods are tested on multimodal physiological time-series data collected during driving tasks, in both real-world and driving simulator settings.