Pre-Processing Inertial Measurement Unit-Based Data for Process Mining Using Convolutional Neural Networks

Daniel Polle (), Milda Aleknonytė-Resch (), Dominik Janssen (), Clint Hansen, Elke Warmerdam, Walter Maetzler and Agnes Koschmider ()
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Daniel Polle: University of Bayreuth, Department of Business Informatics and Process Analytics
Milda Aleknonytė-Resch: Kiel University, Department of Computer Science
Dominik Janssen: University of Bayreuth, Department of Business Informatics and Process Analytics
Clint Hansen: Kiel University, Department of Neurology
Elke Warmerdam: Saarland University, Werner Siemens Endowed Chair of Innovative Implant Development (Fracture Healing)
Walter Maetzler: Kiel University, Department of Neurology
Agnes Koschmider: University of Bayreuth, Department of Business Informatics and Process Analytics

A chapter in Digital Innovation and Organizational Transformation, 2026, pp 103-116 from Springer

Abstract: Abstract The analysis of inertial measurement unit (IMU)-based data allows tracking human behavior, detecting anomalies, and predicting human activity changes. As IMU-based data is unstructured and continuous, the application of process mining could provide additional insights into the underlying human performance. Therefore, the data has to be efficiently pre-processed in order to be used by process mining algorithms. This paper presents an approach to convert IMU-based data into structured event data for process mining. Particularly, the approach relies on methods for time-series segmentation and convolutional neural networks. In this way, activities of daily living can be identified from the unstructured data. The evaluation results show that convolutional neural networks are suitable for discovering activities when window sizes are previously known and have low cutoff values. The combination with a fixed sliding window approach for unknown window sizes appears superior.

Keywords: process mining; unstructured data; convolutional neural networks; sensor data; human activity detection (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-08483-5_8

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