The amount of seasonings used during food preparation is quite important information for modern people to enable them to cook delicious dishes as well as to take care for their health. In this paper, we propose a near real-time automated system for measuring and recording the amount of seasonings used during food preparation. Our proposed system is equipped with two devices: electronic scales and a camera. Seasoning bottles are basically placed on the electronic scales in the proposed system, and the scales continually measure the total weight of the bottles placed on them. When a chef uses a certain seasoning, he/she first picks up the bottle containing it from the scales, then adds the seasoning to a dish, and then returns the bottle to the scales. In this process, the chef's picking and returning actions are monitored by the camera. The consumed amount of each seasoning is calculated as the difference in weight between before and after it is used. We evaluated the performance of the proposed system with experiments in 301 trials in actual food preparation performed by seven participants. The results revealed that our system successfully measured the consumption of seasonings in 60.1% of all the trials.

This study explores significant eye-gaze features that can be used to estimate subjective difficulty while reading educational comics. Educational comics have grown rapidly as a promising way to teach difficult topics using illustrations and texts. However, comics include a variety of information on one page, so automatically detecting learners' states such as subjective difficulty is difficult with approaches such as system log-based detection, which is common in the Learning Analytics field. In order to solve this problem, this study focused on 28 eye-gaze features, including the proposal of three new features called “Variance in Gaze Convergence,” “Movement between Panels,” and “Movement between Tiles” to estimate two degrees of subjective difficulty. We then ran an experiment in a simulated environment using Virtual Reality (VR) to accurately collect gaze information. We extracted features in two unit levels, page- and panel-units, and evaluated the accuracy with each pattern in user-dependent and user-independent settings, respectively. Our proposed features achieved an average F1 classification-score of 0.721 and 0.742 in user-dependent and user-independent models at panel unit levels, respectively, trained by a Support Vector Machine (SVM).