Wu Bo, Wu Yuan, Dong Ran, Sato Kiminori, Ikuno Soichiro, Nishimura Shoji, Jin Qun
School of Computer Science, Tokyo University of Technology, 1404-1 Katakuramachi, Hachioji City, Tokyo, Japan.
Advanced Research Center for Human Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, 359-1192, Saitama Prefecture, Japan.
Sci Rep. 2025 Jul 1;15(1):21862. doi: 10.1038/s41598-025-08203-y.
In the hilly and mountainous areas of Japan, mowing operations can only be carried out by human labor because of the steep slopes. However, the environment faced by workers when mowing is complex, requiring them to deal with different visual stimuli at the same time. These factors will also be reflected in the data of specific pupil changes, further impacting their concentration while mowing. Therefore, in this study, based on a set of experiments on various terrain (flat land and slope) in Hiroshima, Japan, an analysis method of human pupil changes was proposed based on action decomposition technology Hilbert-Huang Transform (HHT) which can be used to calculate the different frequency patterns (intrinsic mode function, IMF) that represent nonlinearity in pupil changes more effectively than Fourier Transform or Wavelet Transform. Based on the use of our proposed Multiple Comparisons and Filtering framework named MCFID, the IMFs which directly related to specific mowing actions (cutting and lifting) were found though the statistical tools. By monitoring the corresponding IMFs, it is possible to calculate the period of the corresponding pupil movement, and further inversely infer information such as the subject's concentration status. Our approach can also be validated using other pupil movement datasets. The results of the study can provide useful insights for training new lawn mowers, and the relevant data can be used as data accumulation for the development of future fall detection systems.
在日本的山区和丘陵地区，由于坡度陡峭，割草作业只能靠人力进行。然而，工人在割草时面临的环境复杂，需要他们同时应对不同的视觉刺激。这些因素也会反映在特定瞳孔变化的数据中，进而影响他们割草时的注意力。因此，在本研究中，基于在日本广岛对各种地形（平地和斜坡）进行的一组实验，提出了一种基于动作分解技术希尔伯特 - 黄变换（HHT）的人类瞳孔变化分析方法，该方法比傅里叶变换或小波变换能更有效地计算出代表瞳孔变化非线性的不同频率模式（固有模态函数，IMF）。基于我们提出的名为MCFID的多重比较和滤波框架，通过统计工具找到了与特定割草动作（切割和抬起）直接相关的IMF。通过监测相应的IMF，可以计算出相应瞳孔运动的周期，并进一步反向推断出受试者注意力状态等信息。我们的方法也可以使用其他瞳孔运动数据集进行验证。该研究结果可为培训新的割草机提供有用的见解，相关数据可作为未来跌倒检测系统开发的数据积累。
