A Controlled Set-Up Experiment to Establish Personalized Baselines for Real-Life Emotion Recognition
This work addresses the need for personalized emotion recognition systems, though it is incremental as it builds on existing controlled-setup experiments.
The paper tackled the problem of establishing reliable personalized baselines for emotion recognition in real-life settings using physiological sensors, achieving an initial accuracy of 85% and identifying key features suitable for portable devices.
We design, conduct and present the results of a highly personalized baseline emotion recognition experiment, which aims to set reliable ground-truth estimates for the subject's emotional state for real-life prediction under similar conditions using a small number of physiological sensors. We also propose an adaptive stimuli-selection mechanism that would use the user's feedback as guide for future stimuli selection in the controlled-setup experiment and generate optimal ground-truth personalized sessions systematically. Initial results are very promising (85% accuracy) and variable importance analysis shows that only a few features, which are easy-to-implement in portable devices, would suffice to predict the subject's emotional state.