Modeling Regularity and Predictability in Human Behavior from Multidimensional Sensing Signals and Personal Characteristics

Forecasting behavioral patterns can help humans comprehend their habits and tendencies, allowing them to inter-vene before problems arise. Among the numerous techniques of modeling human behavior, cyclic modeling can better identify recurring patterns, providing regularity and predictability in human behavior. However, existing approaches to cyclic modeling ignore the effects of human characteristics, such as resilience and coping abilities, which substantially influence the stability of behavior. To explore the value of adding such information in behavior modeling and prediction, we introduce a transformer-based architecture with an advanced attention mechanism and parallel operation that models regularity in human behavior from multidimensional sensing signals and predicts future behavior patterns. The architecture further transforms static human characteristics metadata into dynamic time series that conform to behavioral patterns and serve as covariates to assist prediction. Our experiments with a wearable dataset indicate that our architecture 1) is more accurate in forecasting human behavior patterns than current time-series models and 2) enables us to investigate the impact of human characteristics on behavior patterns. Specifically, we analyze the influence of resilience and coping strategies on behavioral regularity and predictability. We show that supplementing bio-behavioral wearable data with resilience and coping scores in the forecasting model increases the convergence speed of the model and decreases prediction loss.