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Model-Based and Class-Based Fusion of Multisensor Data

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11962))

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Abstract

In the recent years, the advancement of technology, the constantly aging population and the developments in medicine have resulted in the creation of numerous ambient assisted living systems. Most of these systems consist of a variety of sensors that provide information about the health condition of patients, their activities and also create alerts in case of harmful events. Successfully combining and utilizing all the multimodal information is an important research topic. The current paper compares model-based and class-based fusion, in order to recognize activities by combining data from multiple sensors or sensors of different body placements. More specifically, we tested the performance of three fusion methods; weighted accuracy, averaging and a recently introduced detection rate based fusion method. Weighted accuracy and the detection rate based fusion achieved the best performance in most of the experiments.

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Acknowledgments

This research has been co–financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (T1EDK-00686) and the EC funded project V4Design (H2020-779962).

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Authors and Affiliations

  1. Information Technologies Institute, Center for Research and Technology Hellas, 6th km Charilaou-Thermi, 57001, Thessaloniki, Greece

    Athina Tsanousa, Angelos Chatzimichail, Georgios Meditskos, Stefanos Vrochidis & Ioannis Kompatsiaris

Authors
  1. Athina Tsanousa
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  2. Angelos Chatzimichail
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  3. Georgios Meditskos
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  4. Stefanos Vrochidis
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  5. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Athina Tsanousa .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Tsanousa, A., Chatzimichail, A., Meditskos, G., Vrochidis, S., Kompatsiaris, I. (2020). Model-Based and Class-Based Fusion of Multisensor Data. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_50

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  • DOI: https://doi.org/10.1007/978-3-030-37734-2_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37733-5

  • Online ISBN: 978-3-030-37734-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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