Electrostatic Friction Displays to Enhance Touchscreen Experience

Touchscreens are versatile devices that can display visual content and receive touch input, but they lack the ability to provide programmable tactile feedback. This limitation has been addressed by a few approaches generally called surface haptics technology. This technology modulates the friction between a user’s fingertip and a touchscreen surface to create different tactile sensations when the finger explores the touchscreen. This functionality enables the user to see and feel digital content simultaneously, leading to improved usability and user experiences. One major approach in surface haptics relies on the electrostatic force induced between the finger and an insulating surface on the touchscreen by supplying high AC voltage. The use of AC also induces a vibrational sensation called electrovibration to the user. Electrostatic friction displays require only electrical components and provide uniform friction over the screen. This tactile feedback technology not only allows easy and lightweight integration into touchscreen devices but also provides dynamic, rich, and satisfactory user interfaces. In this chapter, we review the fundamental operation of the electrovibration technology as well as applications have been built upon

Driver Behaviour RecognitionUsing Hidden Markov Models

MasterIn this work we addressed the problem of modelling human driving behavior using hidden Markov models (HMMs). It is part of a bigger objective towards capturing and transferring driving skills from an expert driver to a novice trainee. We believe driving behaviors are in result of driver's decision making rules. So we drew our attention to identify and recognize driver's decisions or in another sense driving rules using driving time-series signals. For this end, first a driving simulator based on a commercial racing wheel is developed to simulate a desired driving task. The required driving signals including acceleration pedal position, steering wheel angle, velocity and heading of the vehicle are collected using the driving simulator. Then inspired by the fact that the only variables a driver has control on them are velocity and heading, their first-order derivative are extracted as the two most important features of driving patterns. Following the same inspiration, we developed an automatic segmentation method to detect the local extrema of controlling variables and divide data samples into a number of segments. We suggest during each segment, the driver keeps the pedal and wheel operations unchanged. Not all data segments come from different sourcesthere might be some criteria to group similar segments. In this line we proposed three partitioning methods, threshold-based, GMM-based, and hierarchical, all originated from dividing the two dimensional feature space into a number of classes. In our belief, data classes are the time-domain realization of driving behaviors. According to each class, the parameters of one HMM are optimized to be used later for recognizing driving behaviors. The achieved high average correct classification rate, between 85% to 95% depend on the partitioning criteria, reveals the efficacy of proposed approach in classifying and recognizing driving behaviors. Finally we made use of behavior recognizers to compare an expert and a novice driver's performances in order to provide some feedback. Two methods one road-dependent and another road-independent are proposed for this end. The evaluation results proved the applicability of proposed methods

정전기 마찰 디스플레이에서의 3차원 곡면 및 표면 햅틱 질감 렌더링

DoctorIn this work, we address the problem of rendering surface curvature and fine texture using an electrovibration display. We proposed effective algorithms to address each problem separately. In the first part, we introduced a gradient-based method to render 3D objects on an electrovibration display. It includes a generalized real-time algorithm to estimate surface gradient from the surface of any 3D mesh. In addition, a separate edge detection method is included to emphasize sharp edges while scanning the surface of a mesh. Conducting a human user study, we showed that in the presence of haptic feedback generated using our algorithm, the users can better recognize 3D bumps and holes when the visual feedback is limited and puzzling. In the second part, we proposed a neural network based texture modeling and rendering method. We first created an inverse neural network dynamic model for the electrovibration display to estimate an actuation signal from the forces collected from the surface of real texture samples. For the force measurement, we developed a linear motorized tribometer enabling adjusting applied normal pressure and scanning velocity. We trained neural networks to learn from the forces resulted from applying a full-band PRBS (pseudo-random binary signal) to the electrovibration display and generate similar actuation signals. While the networks are trained under known normal pressure and scanning velocity, for untested conditions, we proposed a two-part interpolation scheme to produce actuation signal from the neighborhood conditions. The first part generates a signal by taking a weighted average between the signals with the same scanning velocity but different masses. The second part, performs a re-sampling process, either down-sampling or up-sampling, on the newly estimated signals to produce a final signal according to the user applied normal pressure and scanning velocity. We conducted a user study to put the proposed algorithm to test. We asked users to rate the similarity between a real texture and its virtual counterpart. The experimental setup included a load-cell to measure user applied pressure and an IR-frame to track his/her finger position and eventually calculate user's scanning velocity. Testing six different real texture samples, we achieved an average similarity score of 60% using the proposed algorithm against 39% using a basic record-and-playback method. This revealed the potentials of the proposed texture modeling and rendering algorithm accompanied by a linear interpolation scheme in creating virtual textures similar to the real ones

Developing intelligent instruments for improved clinical examination - Re-learning basic skills

International audienc

Improving 3D Shape Recognition with Electrostatic Friction Display

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.111sciescopu

Effects of visual and haptic latency on touchscreen interaction: A case study using painting task

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Soft Pneumatic Actuator for Rendering Anal Sphincter Tone

International audienc

Improving early detection of colorectal cancer through an innovative highly realistic simulation environment

International audienc

An Inverse Neural Network Model for Data-driven Texture Rendering on Electrovibration Display

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