Sensor-Based Detection of Food Hypersensitivity Using Machine Learning

Lennart Jablonski, Torge Jensen, Greta M. Ahlemann, Xinyu Huang, Vivian V. Tetzlaff-Lelleck, Artur Piet, Franziska Schmelter, Valerie S. Dinkler, Christian Sina, Marcin Grzegorzek


The recognition of physiological reactions with the help of machine learning methods already plays a major role in many research areas, but is still little represented in the field of food hypersensitivity recognition. The present work addresses the question of how food hypersensitivity can be detected by analysing sensor data with explainable machine learning algorithms. In a first step, the Empatica E4 wristband, a wearable device that can be easily integrated into everyday life, collects raw data on various physiological patterns, and algorithms are implemented to extract a variety of features from the raw data. Subsequently, machine learning methods are used to target this classification problem and examine how food hypersensitivity can be detected using these objectively measurable features. In a subject-independent setup, an accuracy of 91% could be achieved, which provides a promising basis for a new non-invasive and objectively measurable method to detect food hypersensitivity.

Original languageGerman
Title of host publicationProceedings of the 8th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence, iWOAR 2023
EditorsD. J. C. Matthies, M. Gregorzek, A. Kuijper, H. Leutheuser
PublisherAssociation for Computing Machinery
Publication date21.09.2023
Article numberNo. 11
ISBN (Print)979-840070816-9
Publication statusPublished - 21.09.2023

Research Areas and Centers

  • Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)
  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 205-05 Nutritional Science, Nutritional Medicine
  • 205-07 Medical Informatics and Medical Bioinformatics

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