Haptic information significantly improves human awareness of objects in virtual reality. One way of presenting this information is via encountered-type haptic feedback. An advantage of encounter type feedback is that it enables physical interaction with virtual environments without the need for specialized haptic devices on the hand. Additionally, encountered-type haptics are known for being able to provide high quality contact feedback to the user. However, such systems are typically designed to be grounded (i.e., fixed to the floor). As such, they typically have bounded workspace and a limited range of possible applications. In this work, we present a novel, wearable approach to presenting a user with encountered-type haptic feedback. We realize this feedback using a wearable robotic limb that holds a plate where the user might interact with their environment. An appropriate location for the plate is determined by a novel haptic solver while control of the arm is made possible using motion trackers. The system was designed to be stable, for presenting consistent haptic feedback, while also being safe and lightweight for wearability. By making the feedback system wearable, we enable the presentation of stiff feedback while maintaining the spatial freedom and unbounded workspace of natural hand interaction. Herein, we present the design of the novel system, mechanical and safety considerations when designing a wearable encountered-type system, and an evaluation of the system. A technical evaluation of the implemented system showed that the system provides a stiffness over 25 N/m and slant angle errors under 3°. Three user studies show the limitations of haptic slant perception in humans and the quantitative and qualitative effectiveness of the current prototype system. We conclude the paper by discussing various potentialapplications and possible improvements that could be made to the system.

We present DualVib, a compact handheld device that simulates the haptic sensation of manipulating dynamic mass; mass that causes haptic feedback as the user’s hand moves (e.g., shaking a jar and feeling coins rattling inside). Unlike other devices that require actual displacement of weight, DualVib dispenses with heavy and bulky mechanical structures and, instead, uses four vibration actuators. DualVib simulates a dynamic mass by simultaneously delivering two types of haptic feedback to the user’s hand: (1) pseudo-force feedback created by asymmetric vibrations that render the kinesthetic force arising from the moving mass; and (2) texture feedback through acoustic vibrations that render the object’s surface vibrations correlated with mass material properties. By means of our user study, we found out that DualVib allowed users to more effectively distinguish dynamic masses when compared to using either pseudo-force or texture feedback alone. We also report qualitative feedback from users who experienced five virtual reality applications with our device.

Recently, many learning systems, such as e-learning and WBT (Web based teaching) systems have been developed. In these systems, users can get educational contents and graphical material in a remote place through the network. However, the learning is always one-sided. That is, all users learn the same contents and cannot convey their states like "what is he or she doing now?" to these systems. Therefore, these systems are not necessarily satisfying all the users' demands. In this paper, we propose an interactive learning application of cooking in mixed reality (MR) environment. In an MR environment, a system should not only provide the useful knowledge, but also recognize users' state. Furthermore, by making a parallel transition model about cooking, it is possible to control the user's cooking process. Therefore, users can learn details about cooking more flexibly and effectively

Using multimedia techniques in systems that provide assistance to users has become a common interest in recent research. To achieve a supreme level of interactivity in these systems, taking into account users' states as well as users' preferences is extremely important. However, most actual systems do not consider users' preferences when providing assistance to users. We design a system that selects a desirable recipe for a user to cook based on preference, then guide him through the cooking. The recipe selection relies on people's common interpretation of cooking recipes. We store users' feedback (interpretations) in the form of collaborative filtering, and this feedback is used to identify properties of other recipes. Furthermore, users can learn more flexibly and effectively by controlling their process using a parallel transition model for cooking. Using a parallel transition model, users are able to perform parallel actions in any order they want and the system advise them according to the action taken