Towards identifying context-enriched multimodal behavioral patterns for digital phenotyping of human behaviors

Abstract

Digital phenotyping is a research area that proposes the automatic collection of context data through sensors available in pervasive devices, so allowing computational techniques capable of processing this data to automatically detect human behaviors (e.g., sociability, physical activity). This objective information can support professionals specialized in the process of monitoring and treating mental health of individuals. Based on this scenario, we present a solution capable of processing behavioral inference streams to recognize multimodal patterns of sociability, physical activity, and home stay. The proposed solution combines frequent pattern mining with complex event processing, enabling it to incrementally learn periods of the day that compose individuals’ behavioral habits (i.e., behavioral patterns). The identification of these patterns is performed based on context attributes to model individuals’ behavior in specific situations, such as weekends and working days. Our solution also recognizes behavioral changes through knowledge modeling of the mental health specialist from fuzzy logic concepts. The experiments performed identified that the routine stability of individuals presents a high positive correlation with the ability of our solution to recognize multimodal behavioral patterns capable of modeling the behavioral routine. This evaluation also recognizes that our solution has sensitivity to identify behavioral changes.