Abstract
Multimodal learning is an expanding research area and aims to pursue a better understanding of given data by regarding different modals. Multimodal approaches for qualitative data are used for the quantitative proofing of ground-truth datasets and discovering unexpected phenomena. In this paper, we investigate the effect of multimodal learning schemes of quantitative data to assess its qualitative state. We try to interpret human fatigue levels through analyzing video, thermal image and voice data together. The experiment showed that the multimodal approach using three types of data was more effective than the method of using each dataset individually. As a result, we identified the possibility of predicting human fatigue states.
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