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94 results for “Haptics”
Memory influences haptic perception of softness
<p>The memory of an object’s property (e.g. its typical colour) can affect its visual perception. We investigated whether memoryof the softness of every-day objects influences their haptic perception. We produced bipartite silicone rubber stimuli: one half of the stimuli was covered with a layer of an object (sponge, wood, tennis ball, foam ball); the other half was uncovered silicone. Participants were not aware of the partition. They first used their bare finger to stroke laterally over the covering layer to recognize the well-known object and then indented the other half of the stimulus with a probe to compare its softness to that of an uncovered silicone stimulus. Across four experiments with different methods we showed that silicon stimuli covered with a layer of rather hard objects (tennis ball and wood) were perceived harder than the same silicon stimuli when being covered with a layer of rather soft objects (sponge and foam ball), indicating that haptic perception of softness is affected by memory.</p> <p> </p> <p>There are zip files for every experiment (1-4), which contain all data relative to the publication. Variables of all experiments are described in the file VARIABLE_CODES.txt.</p>
Immersive haptic simulation for training nurses in emergency medical procedures - Data collected and statistical analysis
<p>Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper.</p> <table> <caption>HR ALL</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.857</td> <td>29</td> <td>0.008</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-8.089</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>7.567</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.962</td> <td>29</td> <td>0.006</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST MANN</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>1.665</td> <td>14</td> <td>0.118</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-7.104</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>6.498</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-1.461</td> <td>14</td> <td>0.166</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST VR</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.341</td> <td>14</td> <td>0.035</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-4.612</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>4.482</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.688</td> <td>14</td> <td>0.018</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR BETWEEN GROUPS</caption> <thead> <tr> <th> </th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-1.958</td> <td>28</td> <td>0.060</td> </tr> <tr> <td>Mann post HR</td> <td>-1.902</td> <td>28</td> <td>0.068</td> </tr> <tr> <td>VR pre HR</td> <td>-4.013</td> <td>28</td> <td>< .001</td> </tr> <tr> <td>VR post HR</td> <td>-2.344</td> <td>28</td> <td>0.026</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em> Independent samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the mannequin.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>128.333</td> <td>10.715</td> <td>SBP pre-simulator</td> <td>134.533</td> <td>11.819</td> <td>-1.870</td> <td>14</td> <td>0.083</td> </tr> <tr> <td>SBP post-mannequin</td> <td>125.600</td> <td>11.648</td> <td>SBP post-simulator</td> <td>131.467</td> <td>14.643</td> <td>-2.094</td> <td>14</td> <td>0.055</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>80.133</td> <td>5.527</td> <td>DBP pre-simulator</td> <td>81.533</td> <td>9.039</td> <td>-0.623</td> <td>14</td> <td>0.544</td> </tr> <tr> <td>DBP post-mannequin</td> <td>78.667</td> <td>6.956</td> <td>DBP post-simulator</td> <td>81.400</td> <td>8.475</td> <td>-2.073</td> <td>14</td> <td>0.057</td> </tr> <tr> <td>HR pre-mannequin</td> <td>92.133</td> <td>14.837</td> <td>HR pre-simulator</td> <td>75.733</td> <td>9.9625</td> <td>6.498</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>87.400</td> <td>9.132</td> <td>HR post-simulator</td> <td>91.400</td> <td>14.217</td> <td>-1.461</td> <td>29</td> <td>0.166</td> </tr> </tbody> </table> <p>SBP = Systolic blood pressure. DBP = Diastolic blood pressure. HR = Heart Rate.</p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the ParaVR simulator.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>119.067</td> <td>12.898</td> <td>SBP pre-simulator</td> <td>130.600</td> <td>12.188</td> <td>-3.799</td> <td>14</td> <td>0.002</td> </tr> <tr> <td>SBP post-mannequin</td> <td>117.533</td> <td>13.410</td> <td>SBP post-simulator</td> <td>128.200</td> <td>13.385</td> <td>-4.022</td> <td>14</td> <td>0.001</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>76.533</td> <td>8.943</td> <td>DBP pre-simulator</td> <td>80.200</td> <td>6.899</td> <td>-1.815</td> <td>14</td> <td>0.091</td> </tr> <tr> <td>DBP post-mannequin</td> <td>74.333</td> <td>8.541</td> <td>DBP post-simulator</td> <td>79.133</td> <td>7.864</td> <td>-2.003</td> <td>14</td> <td>0.065</td> </tr> <tr> <td>HR pre-mannequin</td> <td>102.067</td> <td>12.876</td> <td>HR pre-simulator</td> <td>91.533</td> <td>11.825</td> <td>4.482</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>95.867</td> <td>14.623</td> <td>HR post-simulator</td> <td>103.667</td> <td>14.450</td> <td>-2.688</td> <td>29</td> <td>0.018</td> </tr> </tbody> </table>
A haptic illusion created by gravity - Data and Codes
<p>Dataset and code related to the study <em>A haptic illusion created by gravity</em> by Opsomer L, Delhaye BP, Théate V, Thonnard J-L, and Lefèvre P.</p>
Enhancing two-dimensional control via single-channel haptic feedback: A multi-dimensional encoding strategy
Open the record for dataset details and reuse information.
Teleoperation with Baxter robot and haptic device: testing with experts user.
<p>In this video you can see the experiment with expert users developed by Robotics Group of the Universidad de León, as part of a research project that aims to demonstrate the effectiveness of the use of haptic devices in teleoperation environments.</p>
The effect of haptic feedback in the balance of a bicycle
<p>Roll_data_perturbation folder includes the data of all 20 participants. The signals are organized in structures and contain both pure measurement data (labeled data) and the outputs of the Finite Impulse Response Model (labeled black_box).</p>
Haptic Zoom: An interaction model for desktop haptic devices with limited workspace - Evaluation data
<p>Raw data collected in the evaluation of the new interaction technique for force feedback desktop haptic devices called Haptic Zoom.<br><br></p> <p>Gutiérrez-Fernández, A., Fernández-Llamas, C., Esteban, G. & Conde, Miguel A. (2023). Haptic Zoom: An Interaction Model for Desktop Haptic Devices with Limited Workspace. <em>International Journal of Human–Computer Interaction, 39</em>(4), 851-862. <a href="https://doi.org/10.1080/10447318.2022.2049140">https://doi.org/10.1080/10447318.2022.2049140</a></p>
Haptic Reality - Evaluation data
<p>Raw data collected in the evaluation of the new interaction technique for force feedback desktop haptic devices called Haptic Reality.</p>
Processing of haptic texture information over sequential exploration movements
<p>Where textures are defined by repetitive small spatial structures, exploration covering a greater extent will lead to signal repetition. We investigated how sensory estimates derived from these signals are integrated. In Experiment 1 participants stroked with the index finger one to eight times across two virtual gratings. Half of the participants discriminated according to ridge amplitude, the other half according to ridge spatial period. In both tasks just noticeable differences (JNDs) decreased with an increasing number of strokes. Those gains from additional exploration were over 3 times smaller than predicted for optimal observers who have access to equally reliable, and therefore equally weighted estimates for the entire exploration. We assume that the sequential nature of the exploration leads to memory decay of sensory estimates. Thus, participants compare an overall estimate of the first stimulus, which is affected by memory decay, to stroke-specific estimates during the exploration of the second stimulus. This was tested in Experiments 2 & 3. The spatial period of one stroke across either the first or second of two sequentially presented gratings was slightly discrepant from periods in all other strokes. This allowed calculating weights of stroke-specific estimates in the overall percept. As predicted, weights were approximately equal for all strokes in the first stimulus, while weights decreased during the exploration of the second stimulus. A quantitative Kalman filter model of our assumptions was consistent with the data. Hence, our results support an optimal integration model for sequential information given that memory decay affects comparison processes.</p>
dataset for Biases in the Visual and Haptic Subjective Vertical Reveal the Role of Proprioceptive/Vestibular Priors in Child Development
<p>dataset for "Biases in the Visual and Haptic Subjective Vertical Reveal the Role of Proprioceptive/Vestibular Priors in Child Development" publication. </p>
"Haptic aesthetics and bodily properties of Ori Gersht's digital art: a behavioral and eye-tracking study." - datasets and script
<p>"Haptic aesthetics and bodily properties of Ori Gersht’s digital art: a behavioral and eye-tracking study."</p> <p>Datasets and R script for analyses of behavioural scores and visual parameter.</p>
Data supporting the publication "Optimized Sandwich and Topological Structures for Enhanced Haptic Transparency"
<p>This dataset includes experimental modal responses of sandwich and topological structures, which are designed to enhance haptic transparency. </p>
Target search and inspection strategies in haptic search
<p>Haptic search is a common everyday task, usually consisting of two processes: target search and target analysis. During target search we need to know where our fingers are in space, remember the already completed path and the outline of the remaining space. During target analysis we need to understand whether the detected potential target is the desired one. Here we characterized dynamics of exploratory movements in these two processes. In our experiments participants searched for a particular configuration of symbols on a rectangular tactile display. We observed that participants preferentially moved the hand parallel to the edges of the tactile display during target<br> search, which possibly eased orientation within the search space. After a potential target was detected by any of the fingers, there was higher probability that subsequent exploration was performed by the index or the middle finger. At the same time, these fingers dramatically slowed down. Being in contact with the potential target, the index and the middle finger moved within a smaller area than the other fingers, which rather seemed to move away to leave them space. These results suggest that the middle and the index finger are specialized for fine analysis in haptic search.</p> <p> </p> <p>The zip file contains all data relative to the publication. The structure of the data of all experiments is described in the README file.</p>
User Study Data for "Enhancing Ultrasound Haptics with Parametric Audio Effects"
<p>Data from an experiment investigation the perception of ultrasound haptic and parametric audio stimuli, as described in a paper at ACM ICMI 2021.</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Vibrometry Measurements with Elastomer Plate
<p><strong>Vibrometry Measurements with Elastomer Plate</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" <a href="http://www.science.org/doi/10.1126/sciadv.adf2037">(Reardon et al., 2023)</a>. If you use these data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>).</p> <p>This dataset contains the response of a tissue phantom (Gelatin #2, Humimic) to focused ultrasound (UHEV1, Ultrahaptics). The data is provided as .mat files. The files are separated by scanning path (linear and zigzag trajectories) and scanning speed. Details about our experimental procedure can be found in our publication.</p> <p>IMPORTANT - The data provided is the unprocessed output from a laser doppler vibrometer (Ometron, model 8330). The data is NOT time-aligned and must be reconstructed using the reference signal and the map of the measurement locations.</p> <p> </p> <p><strong>Line Paths</strong> - Focused ultrasound scanned along a linear path on the surface of the tissue phantom at one of 5 speeds - 2 m/s, 4 m/s, 7 m/s, 11 m/s, and 15 m/s. The scanning speed is designated in the filename.</p> <p><strong>Zigzag Paths</strong> - Focused ultrasound scanned along a zigzag path on the surface of the tissue phantom with longitudinal scanning speed <em>v<sub>l</sub></em> = 3, 5 m/s. At both speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>mod</sub></em>, of +-2.5 m/s yielding a zigzag path (2 cm path width). The longitudinal scanning speed is designated in the filename.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>y</strong> (MxN) - 2D array containing the tissue phantom velocity normal to the laser doppler vibrometer (in mm/s) for N measurement locations. All repetitions are included as a single time-domain signal of M timepoints</p> <p><strong>ref</strong> (MxN) - 2D array containing a reference voltage signal taken from the ultrasound phased array. Each of the N measured locations has a reference signal. The beginning of the reference signal identifies the start of a new repetition.</p> <p><strong>nRepetitions</strong> - Number of stimulus repetitions (reps can be averaged to improve the measurement signal-to-noise ratio).</p> <p><strong>fs</strong> - Laser doppler vibrometer sampling rate (in Hz)</p> <p><strong>measurementLocations</strong> (Nx2) - 2D measurement locations on the gelatin surface (x,y; in m) for each of N measurement locations</p>
Usability Testing of Affordable Haptic Robots for Stroke Therapy
ClinicalTrials.gov study NCT02772809. IPD Sharing: NO. Countries: 1. Publications: 3.
Towards human-resolution haptics: A high bandwidth, high density, wearable tactile display
Open the record for dataset details and reuse information.
Haptic artificial muscle skin for extended reality
Open the record for dataset details and reuse information.
Test of the haptic knob using a pilot software interface
<p>Additional material related to the paper "<em>A LOW-COST ENDLESS KNOB CONTROLLER WITH PROGRAMMABLE RESISTIVE FORCE FEEDBACK FOR MULTIMEDIA PRODUCTION</em>"</p> <p>This video contains an example of a hapitc knob device used for supporting basic audio editing operations in a pilot software environment developed in Processing. In particular, the five effects shown are amplitude transient detection, time window navigation, volume control, multiple choice selector and variable resistance.</p>
An Immersive Haptic-enabled Training Simulation for Paramedics - Questionnaire
<p>Questionnaire filled out by participants in the initial evaluation of ParaVR.<br> (Pending publication)</p>
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