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Hilbert-Huang transform based pupil changes analysis for concentration assessment in skilled mowing.

Bo Wu1, Yuan Wu2, Ran Dong3

  • 1School of Computer Science, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji City, Tokyo, Japan.

Scientific Reports
|July 2, 2025
PubMed
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Researchers developed a new method using Hilbert-Huang Transform (HHT) to analyze pupil changes during mowing on slopes. This technique helps assess worker concentration and can aid in training and developing safety systems.

Area of Science:

  • Human-computer interaction
  • Biomedical engineering
  • Ergonomics

Background:

  • Mowing on steep terrain in Japan relies on manual labor.
  • Complex visual environments impact worker concentration and pupil responses.
  • Existing analysis methods may not fully capture nonlinear pupil dynamics.

Purpose of the Study:

  • To propose a novel analysis method for human pupil changes during mowing on varied terrain.
  • To identify specific pupil frequency patterns (IMFs) linked to mowing actions.
  • To infer worker concentration status from pupil movement data.

Main Methods:

  • Experiments conducted on flat and sloped terrain in Hiroshima, Japan.
  • Application of Hilbert-Huang Transform (HHT) for action decomposition and nonlinear analysis of pupil data.
Keywords:
Conservation of rice terracesEmpirical mode decompositionEye movement measurementHilbert–Huang transformPupil accommodation speed analysisSlope mowing behaviors analysis

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  • Utilized a Multiple Comparisons and Filtering framework (MCFID) to identify relevant intrinsic mode functions (IMFs).

    • Main Results:

    • Successfully identified IMFs directly related to specific mowing actions (cutting, lifting).
    • Demonstrated that HHT is effective in analyzing nonlinear pupil changes.
    • Established a method to infer concentration status by monitoring corresponding IMFs.

    Conclusions:

    • The proposed HHT-based method offers a more effective way to analyze pupil dynamics during complex tasks.
    • Insights gained can improve training for lawn mower operators in challenging environments.
    • The methodology provides a foundation for developing advanced fall detection systems.