A Review of Context-Aware Machine Learning for Stress Detection

Excessive stress can lead to poor physical and psychological outcomes thereby reducing quality of life and increasing health-related consequences. Context awareness refers to sources of information that can help technological applications be aware of human–environment interaction. Machine learning with context awareness is an emerging field with the potential to accurately detect stress and subsequently allowing for intervention implementation. Most currently existing applications do not incorporate context in machine-learning models for stress detection, and as such their applicability in real-life settings is quite low. Using context awareness will provide relevant information regarding the activity, location, and day-time resulting in increased accuracy and precision of stress detection. In this article, existing machine learning-based approaches for stress detection that considers context are reviewed.