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Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models.

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  • Additional Information
    • Source:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
    • Publication Information:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • Subject Terms:
      Machine Learning* ; Algorithms*; Humans
    • Abstract:
      Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient's tissue require precise sensor data. However, since the deformation of the patient's body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.
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    • Grant Information:
      3293 KIT-Publication Fund of the Karlsruhe Institute of Technology
    • Contributed Indexing:
      Keywords: Kalman filter; ROS2; anomaly detection; deep learning; machine learning; medical; sensor fusion; service-oriented architecture (SOA); simulation; time series analysis
    • Publication Date:
      Date Created: 20240511 Date Completed: 20240511 Latest Revision: 20240513
    • Publication Date:
      20240513
    • Accession Number:
      PMC11086117
    • Accession Number:
      10.3390/s24092895
    • Accession Number:
      38733000