IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-032-08483-5_8.html

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

In: Digital Innovation and Organizational Transformation

Author

Listed:
  • 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)

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.

Suggested Citation

  • Daniel Polle & Milda Aleknonytė-Resch & Dominik Janssen & Clint Hansen & Elke Warmerdam & Walter Maetzler & Agnes Koschmider, 2026. "Pre-Processing Inertial Measurement Unit-Based Data for Process Mining Using Convolutional Neural Networks," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), Digital Innovation and Organizational Transformation, pages 103-116, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08483-5_8
    DOI: 10.1007/978-3-032-08483-5_8
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lnichp:978-3-032-08483-5_8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.