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

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.