Electrovibration technology has the potential for seamless integration into ordinary smartphones and tablets to provide programmable haptic feedback. The aim of this work is to seek effective ways to improve 3D perception of visual objects rendered on an electrovibration display. Utilizing a gradient-based algorithm, we first investigated whether rendering only lateral frictional force on an electrovibration display improves 3D shape perception compared to doing the same using a force-feedback interface. We observed that although users do not naturally associate electrovibration patterns to geometrical shapes, they can map patterns to shapes with moderate accuracy if guidance or context is given. Motivated by this finding, we generalized the gradient-based rendering algorithm to estimate the surface gradient for any 3D mesh and added an edge detection algorithm to render sharp edges. Then we evaluated the advantages of our algorithm in a user study and found that our algorithm can notably improve the performance of 3D shape recognition when visual information is limited.
Two experiments tested young adults’ ability to discriminate the direction of friction-defined textural gradients rendered by the Senseg FeelScreenTM. Gradients were particularly effective when they spanned the low end of the intensity range. This trend likely reflects saturation of the device’s rendering capabilities at high intensities, as confirmed by measurements with a manual linear tribometer. The results show promise for use of gradients rendered with variable friction displays to aid non-visual navigation on tablets.
This paper reports a user study on the effects of latency in visual and haptic feedback on touchscreen interaction for a painting task. Our work was motivated by recently emerging multimodal use of touchscreens and electrostatic friction displays with high-quality 3D graphics. We designed and implemented a painting application on a touchscreen that enabled users to paint a 3D sculpture with their finger pad while perceiving haptic feedback through electrovibration. Software-induced latency was varied from 0 to 120 ms for both visual and haptic feedback. Participants’ task was to paint on the 3D sculpture as quickly and accurately as possible. For performance assessment, we measured task completion time and execution error. We also obtained subjective responses to four questions (easiness, responsiveness, pleasantness, and the sense of control) related to user experiences. Experiment results indicated that visual latency is critical for both task completion time and task execution error whereas haptic latency is for task execution error, but not for task completion time. Both latencies affected the subjective responses, but visual latency had more
Electrostatic displays are an effective platform in enabling programmed haptic feedback on a touchscreen using variable friction. In this work, we investigate the extent to which users can correctly recognize 3D primitive geometrical shapes rendered using only the tangential friction force produced by an electrostatic display. The algorithm for that was based on a previous study that demonstrated using a force-feedback interface that lateral force has a dominant role in shape perception. The main findings of this study are two-fold: 1) Users do not naturally associate electrovibration patterns to primitive shapes unless guidance or context for that is given; and 2) Users can map electrovibration patterns to primitive shapes with moderately high accuracy if they are asked to do so. These results provide some promise that electrostatic displays can further improve the user experience of exploring the visual content displayed on a touchscreen.
This paper presents a new approach to datadriven modeling of isotropic haptic textures using frequencydecomposed neural networks from the contact acceleration data that are captured when a stylus is scanned on a textured surface with diverse scanning velocities and normal forces. We first describe a motorized texture scanner that has been developed for accurate and easy data collection under a wide variety of conditions. We then propose two neural network models with different topologies: a unified model that feeds all of acceleration data, scanning velocity, and normal force as input variables to a single large neural network and a decomposed model that consists of a number of smaller neural networks each trained with the acceleration data for a pair of scanning velocity and normal force. An experiment with real samples showed that the unified model has better cross-validation ability in terms of spectral rms errors and its performance is comparable to the best available in the literature. In addition, we present some preliminary results of anisotropic texture modeling achieved by extending the unified model.
In this paper, we evaluate the adequacy of several performance measures for the evaluation of driving skills between different drivers. This work was motivated by the need for a training system that captures the driving skills of an expert driver and transfers the skills to novice drivers using a haptic-enabled driving simulator. The performance measures examined include traditional task performance measures, e.g., the mean position error, and a stochastic distance between a pair of hidden Markov models (HMMs), each of which is trained for an individual driver. The emphasis of the latter is on the differences between the stochastic somatosensory processes of human driving skills. For the evaluation, we developed a driving simulator and carried out an experiment that collected the driving data of an expert driver whose data were used as a reference for comparison and of many other subjects. The performance measures were computed from the experimental data, and they were compared to each other. We also collected the subjective judgement scores of the driver’s skills made by a highly-experienced external evaluator, and these subjective scores were compared with the objective performance measures. Analysis results showed that the HMM-based distance metric had a moderately high correlation between the subjective scores
and it was also consistent with the other task performance measures, indicating the adequacy of the HMM-based metric as an objective performance measure for driving skill learning. The findings of this work can contribute to developing a driving simulator for training with an objective assessment function of driving skills.
In this paper, we address a model-based objective measure for the evaluation of driving skills between different drivers. This metric is based on a stochastic distance between a pair of hidden Markov models (HMMs) each of which is trained for an individual driver. The emphasis of comparison is on the differences between the stochastic somatosensory processes of human driving skills. To evaluate the adequacy of the metric, we developed a driving simulator and carried out an experiment that collected the driving data of many novice drivers and an expert driver. The objective measures were computed between each novice driver and the expert driver, and they were compared with the subjective judgement of each novice driver’s skills made by the expert driver. Analysis results showed high agreement between the two measures, supporting that the objective metric is a suitable descriptor for the differences in driving skills. The findings of this work can contribute to developing a driving simulator for training with an objective assessment function of driving skills.