The main contribution of this paper is to develop a method for alleviating fatigue during handling medium-weight objects and augmenting our endurance by affecting our weight perception with augmented reality technology. To assist people to lift medium-weight objects without a complex structure or various costs, we focus on the phenomenon that our weight perception during handling objects is affected by visual properties. Our hypothesis is that this illusionary effect in weight perception can be applied to reduce fatigue while handling medium-weight objects without mechatronics-based physical assistance. In this paper, we propose an augmented reality system that changes the brightness value of an object in order to reduce fatigue while handling the object. We conducted two fundamental experiments to investigate the effectiveness of the proposed system. Our results suggested that the system eliminates the need to use excess energy for handling objects and reduces fatigue during the handling task.

In this paper, we propose a new, efficient retrieval system for large-scale multi-modal data including video tracks. With large-scale multi-modal data, the huge data size and various contents cause degradation of efficiency and precision of retrieval results. Recent research on image annotation and retrieval shows that image features based on the Bag-of-Visual Words approach with local descriptors such as SIFT perform surprisingly well with large-scale image datasets. Those powerful descriptors tend to be high-dimensional, imposing a high computational cost for approximate nearest neighbor searching in raw feature space. Our video retrieval method is focused on the correlation between image, sound, and location information recorded simultaneously, and to learn conceptual space describing the contents of the data to realize efficient searching. Experiments show good performance of our retrieval system with low memory usage and temporal complexity.