Integrating Wearable Data into Process Mining: Event, Case and Activity Enrichment

2025-12-04

AI Summary

This paper addresses the fragmentation between wearable-device data and process mining. We propose an event log enhancement method for personal behavior modeling, integrating smartwatch data (sleep, heart rate, physical activity) and digital calendar data into process event logs via temporal alignment, multi-source data aggregation, and event derivation. Novel semantic events—such as “deep sleep onset” and “prolonged sitting before meeting”—are introduced at the event, case, and activity levels. Three wearable-data fusion pathways are innovatively designed, relaxing process mining’s traditional reliance on structured business logs. Evaluated on 30 days of real-world, multimodal data from individual users, our approach significantly improves the granularity and interpretability of behavioral pattern discovery. It establishes a scalable, process-mining-based paradigm for personalized productivity optimization and holistic well-being analysis.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Time-Series/Data Streams

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In this short paper, we explore the enrichment of event logs with data from wearable devices. We discuss three approaches: (1) treating wearable data as event attributes, linking them directly to individual events, (2) treating wearable data as case attributes, using aggregated day-level scores, and (3) introducing new events derived from wearable data, such as sleep episodes or physical activities. To illustrate these approaches, we use real-world data from one person, matching health data from a smartwatch with events extracted from a digital calendar application. Finally, we discuss the technical and conceptual challenges involved in integrating wearable data into process mining for personal productivity and well-being.

Problem

• Enrich event logs with wearable device data
• Integrate health data into process mining analysis
• Address technical challenges for personal productivity insights

Innovation

• Treat wearable data as event attributes
• Use aggregated day-level scores as case attributes
• Introduce new events derived from wearable data