Sano, A., Johns, P., Czerwinski, M. "Designing Opportune Stress Intervention Delivery Timing using Multi-modal Data," International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, Texas, October 2017.

Abstract

This paper describes a micro-stress intervention system for information office workers in the workplace, their responses to the interventions and machine learning models to predict the most opportune timing for providing the interventions. We studied 30 office workers for 10 days and examined their work patterns by monitoring their computer and application usage, sleep, activity, heart rate and its variability, as well as the history of micro-stress interventions provided through our desktop software. We analyzed temporal patterns of stress intervention acceptance/rejection and the relationships between their subjective and objective responses to the interventions and perceived work engagement, challenge and stress levels. We then developed machine learning models to predict better stress intervention delivery timing based on this multi-modal data. We found that features from computer and application usage, activity, heart rate variability and stress intervention history showed up to 80.0% accuracy in predicting good or bad intervention timing using a multi-kernel support vector machine algorithm. These findings could help practitioners design the most effective, just-in-time, closedloop, stress interventions. To our knowledge, this is one of the first papers to review opportune stress interventions’ delivery timing research, which could have a big influence in designing stress intervention technologies.