Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets.

In Japan, where natural disasters occurs frequently, obtaining and delivering accurate information promptly when a disaster occurs is essential to minimize damage. Information from traditional mass media contain a number of general information unrelated to disaster, so there are limitations in delivering necessary information to the resident in affected area. On the other hand, Twitter, one of the popular social media, is expected to play an important role during disaster because of its simplicity, promptness and wide propagation. However, because of its huge size of users, there are too many tweets which hinders timely extraction of relevant information. Disaster information is also useful for business travellers and tourists. They are less informed about the area and the challenge is to provide them with accurate information promptly. Our study proposes to establish a system to assist real time understanding of disaster by extracting relevant information efficiently from messages tweeted during two typhoons. First, binary classification is applied to classify and extract disaster tweets from tweets group. By using BNS method, the improvement in accuracy is confirmed. Then clustering is applied to the disaster tweets. The tweets are classified by 15 clusters generated. The result yields F measure of 0.59.