Consumer-grade virtual reality (VR) controllers are typically equipped with one vibrotactile actuator, allowing to create simple and non-spatialized tactile sensations through the vibration of the entire controller. Leveraging the funneling effect, an illusion in which multiple vibrations are perceived as a single one, we propose MultiVibes, a VR controller capable of rendering spatialized sensations at different locations on the user’s hand and fingers. The designed prototype includes ten vibrotactile actuators, directly in contact with the skin of the hand, limiting the propagation of vibrations through the controller. We evaluated MultiVibes through two controlled experiments. The first one focused on the ability of users to recognize spatio-temporal patterns, while the second one focused on the impact of MultiVibes on the users’ haptic experience when interacting with virtual objects they can feel. Taken together, the results show that MultiVibes is capable of providing accurate spatialized feedback and that users prefer MultiVibes over recent VR controllers.

When designing virtual embodiment studies, one of the key choices is the nature of the experimental factors, either between-subjects or within-subjects. However, it is well known that each design has ad-vantages and disadvantages in terms of statistical power, sample size requirements and confounding factors. This paper reports a within-subjects experiment with 92 participants comparing self-reported embodiment scores under a visuomotor task with two conditions: synchronous motions and asynchronous motions with a latency of 300 ms. With the gathered data, using a Monte-Carlo method, we created numerous simulations of within- and between-subjects experiments by selecting subsets of the data. In particular, we explored the impact of the number of participants on the replicability of the results from the 92 within-subjects experiment. For the between-subjects simulations, only the first condition for each user was considered to create the simulations. The results showed that while the replicability of the results increased as the number of participants increased for the within-subjects simulations, no matter the number of participants, between-subjects simulations were not able to replicate the initial results. We discuss the potential reasons that could have led to this surprising result and potential methodological practices to mitigate them.

Touch sensitive interface enables new interaction methods, like using gesture commands. The use of gesture commands give rise to a cross-learning situation where the user has to learn and memorize the command gestures and the classifier has to learn and recognize drawn gestures. To easily memorize more than a dozen of gesture commands, it is important to be able to customize them. The classification task associated with the use of customized gesture commands is complex because the classifier only has very few samples per class to start learning from. We thus need an evolving recognition system that can start from very few data samples and that will learn incrementally to achieve good performance after some using time. This article presents the impact of using rejection based user interactions to supervise the on-line training of the evolving classifier. The objective is to obtain a gesture command system that cooperates as best as possible with the user: to learn from its mistakes without soliciting him too often. To detect confusing classes we apply confusion reject principles to our evolving recognizer, which is based on a first order fuzzy inference system. A significant user experiment has been performed on 63 persons that validates our approach. This user experiment shows the interest of optimizing user interactions by taking into account the confusion detection capability of our recognition system.

This paper presents a new method to help users defining personalized gesture commands (on pen-based devices) that maximize recognition performance from the classifier. The use of gesture commands give rise to a cross-learning situation where the user has to learn and memorize the command gestures and the classifier has to learn and recognize drawn gestures. The classification task associated with the use of customized gesture commands is complex because the classifier only has very few samples per class to start learning from. We thus need an evolving recognition system that can start from scratch or very few data samples and that will learn incrementally to achieve good performance after some using time. Our objective is to make the user aware of the recognizer difficulties during the definition of commands, by detecting confusion among gesture classes, in order to help him define a gesture set that yield good recognition performance from the beginning. To detect confusing classes we apply confusion reject principles to our evolving recognizer, which is based on a first order fuzzy inference system. A realistic experiment has been made on 55 persons to validate our confusion detection technique, and it shows that our method leads to a significant improvement of the classifier recognition performance.