Catching Patient’s Attention at the Right Time to Help Them Undergo Behavioural Change: Stress Classification Experiment from Blood Volume Pulse

Abstract

The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a coaching system recommending personalized evidence-based health behavioral change interventions and supporting patients compliance. Focusing on managing stress via deep breathing intervention, we hypothesise that the patients are more likely to perform suggested breathing exercises when they need calming down. To prompt them at the right time, we developed a machine-learning stress detector based on blood volume pulse that can be measured via consumer-grade smartwatches. We used a publicly available WESAD dataset to evaluate it. Simple 1D CNN achieves 0.837 average F1-score in binary stress vs. non-stress classification and 0.653 in stress vs. amusement vs. neutral classification reaching the state-of-art performance. Personalisation of the population model via fine-tuning on a small number of annotated patient-specific samples yields 12% improvement in stress vs. amusement vs. neutral classification. In future work we will include additional context information to further refine the timing of the prompt and adjust the exercise level.