Context-Enriched Process Discovery from IoT Data Sources for Human Behavioral Monitoring

The increasing availability of data from diverse sources presents new opportunities for inclusive and data-driven modeling, particularly in the area of human behavior analysis. Process discovery, as a data-driven technique of Process Mining (PM), can be used to extract behavioral and workflow models from event logs. However, this technique requires well-structured event logs that accurately reflect actual processes. In many cases, discovery approaches rely on single-source datasets and rarely integrate heterogeneous data and contextual information. This lack of integration limits the ability to extract deeper insights into human behavior and hinders comprehensive and interpretable modeling. To address this, we propose a methodological approach called Human Behavior Monitoring with Unified Log and Contextualized Process Discovery (HB-UniContex). HB-UniContex consolidates data from multiple sources, including smart sensing systems, multimedia data streams and human-generated data sources, into a unified event log that supports context-aware process discovery and detailed visualization. HB-UniContex enables a clear extraction and interpretation of human behavior in relation to contextual information. We demonstrate the applicability of this method through a case study in an Ambient Assisted Living (AAL) setting, analyzing human behavior models and examining the relationship between mood states and daily activities. Our findings reveal specific behavior patterns associated with different mood states, providing precise insights into how an individual’s daily mood correlates with certain daily activities.