Painting an apple with an apple : a tangible tabletop interface for painting with physical objects

We introduce UnicrePaint, a digital painting system that allows the user to paint with physical objects by acquiring three parameters from the interacting object: the form, the color pattern and the contact pressure. The design of the system is motivated by a hypothesis that integrating direct input from physical objects with digital painting offers unique creative experiences to the user. A major technical challenge in implementing UnicrePaint is to resolve the conflict between input and output, i.e., to be able to capture the form and color pattern of contacting objects from a camera, while at the same time be able to present the captured data using a projector. We present a solution for this problem. We implemented a prototype and carried out a user study with fifteen novice users. Additionally, five professional users with art-related backgrounds participated in a user study to obtain insights into how professionals might view our system. The results show that UnicrePaint offers unique experiences with painting in a creative manner. Also, its potentials beyond mere artwork are suggested

On-Body Smartphone Localization with an Accelerometer

A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device’s functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented