Modeling Behavioral Traits and Well-being Using Human Biosignals: Challenges and Methods

Measuring and understanding self-reported behavioral responses plays an important role in various clinical applications. However, individual differences in physiology, perception, and behavior cause data heterogeneity in both selfreported responses and biosignals, which can pose challenges for machine learning models. Data heterogeneity not only impacts the performance of the models but can also yield biased predictions that may lead to unfair decision making. In this work, we will explore approaches to address the modeling challenges that rise from heterogeneity of human behavioral data.