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312 results for “Touch”
Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering"
<p>Experimental study dataset: "Enhancing Touch Sensibility by Sensory Retraining in a Sensory Discrimination Task via Haptic Rendering."</p> <p>This research was supported by the Swiss National Science Foundation through the grant PP00P2 163800. This work was also supported by SENACYT and IFARHU, the Panamanian Government.</p> <p>Files and data to upload:</p> <ol> <li>Description of variables </li> <li>Continue robot data </li> <li>Discontinue robot data </li> <li>Questionnaire data </li> </ol> <p> </p>
Landslide Removal Experiment vegetation cover and pole touches
The purpose of this data set is to document recovery of vegetation in removal plots in landslides in the Luquillo Experimental Forest (LEF.) Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Somatosensory phase-encoded bilateral full-body light touch stimulation
Open the record for dataset details and reuse information.
SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Skin Vibrations Across the Upper Limb
<p>The repository contains the data for the toolbox released as part of the publication “SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb.” The toolbox and installation and usage instructions can be found on GitHub here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>. If you use these data or our toolbox please cite our publication: <a href="https://doi.org/10.1109/HAPTICS59260.2024.10520852">https://doi.org/10.1109/HAPTICS59260.2024.10520852</a>.</p> <p>Full citation: “Tummala, N., Reardon, G., Fani, S., Goetz, D., Bianchi, M., and Visell, Y. (2024) SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb. IEEE Haptics Symposium 2024. DOI: 10.1109/HAPTICS59260.2024.10520852” </p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.</p> <p><strong> </strong></p> <p><strong>Dataset Description</strong></p> <p>This dataset comprises experimental data of 3-axis surface acceleration at 72 locations on the skin in response to unit impulsive forces supplied at 20 different input locations on the palmar hand surface. For details on our experimental procedure, please see our publication. This data is intended to be used as part of the SkinSource toolbox, which can be found here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>.</p> <p><strong> </strong></p> <p><strong>Data Fields</strong></p> <p>The data is provided as a .mat file. This file contains a single variable “dataTable” of variable type “table.” The table contains 80 rows, each corresponding to a unique experimental condition (4 participants x 20 input locations), and contains the following fields:</p> <p><strong>Data </strong>(522x72x3) - 3D array containing the 3-axis skin acceleration at 522 time points (impulse responses) for each of 72 accelerometers. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for the accelerometer locations on the dorsal surface of the upper limb.</p> <p><strong>Model </strong>- The upper limb model number. This number specifies the participant that data was taken on.</p> <p><strong>Location</strong> -<strong> </strong>Number designating which input location on the palmar hand surface the data corresponds to. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for input location number mapping.</p>
Experimental study dataset: "Enhancing Touch Sensibility with Sensory Electrical Stimulation and Sensory Retraining"
<p>Experimental study dataset: "Enhancing Touch Sensibility with Sensory Electrical Stimulation and Sensory Retraining".</p> <p>This research was supported by the Swiss National Science Foundation through the grant PP00P2 163800. This work was also supported by SENACYT and IFARHU, the Panamanian Government.</p> <p>Files and data to upload:</p> <ol> <li>Description of variables </li> <li>Continue robot data (pickles)</li> <li>Discontinue robot data (task performance)</li> <li>EEG data</li> </ol>
Neural Touch-Screen Ensemble Performance 2017-07-03
<p>A studio performance of an RNN-controlled Touch Screen Ensemble from 2017-07-03 at the University of Oslo.</p> <p>In this performance, a touch-screen musician improvises with a computer-controlled ensemble of three artificial performers. A recurrent neural network tracks the touch gestures of the human performer and predicts musically appropriate gestural responses for the three artificial musicians. The performances on the three 'AI' iPads are then constructed from matching snippets of previous human recordings. A plot of the whole ensemble's touch gestures are shown on the projected screen.</p> <p>This performance uses Metatone Classifier (https://doi.org/10.5281/zenodo.51712) to track touch gestures and Gesture-RNN (https://github.com/cpmpercussion/gesture-rnn) to predict gestural states for the ensemble. The touch-screen app used in this performance was PhaseRings (https://doi.org/10.5281/zenodo.50860).</p>
UAV test touch of 400 kV power-line
<p>Test flight of a UAV with and without protective ESD shielding touching a 400 kV power-line.</p>
Top-down modulation of shape and roughness discrimination in active touch by covert attention
<p>Due to limitations in perceptual processing, information relevant to momentary task goals is selected from the vast amount of available sensory information by top-down mechanisms (e.g. attention) that can increase perceptual performance. We investigated how covert attention affects perception of 3D objects in active touch. In our experiment, participants simultaneously explored the shape and roughness of two objects in sequence, and were told afterwards to compare the two objects with regard to one of the two features. To direct the focus of covert attention to the different features we manipulated the expectation of a shape or roughness judgment by varying the frequency of trials for each task (20%, 50%, 80%), then we measured discrimination thresholds. We found higher discrimination thresholds for both shape and roughness perception when the task was unexpected, compared to the conditions in which the task was expected (or both tasks were expected equally). Our results suggest that active touch perception is modulated by expectations about the task. This implies that despite fundamental differences, active and passive touch are affected by feature selective covert attention in a similar way.</p> <p> </p> <p>There are zip files for the main experiment and the two pilot experiments, which contain all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file with the participant's answers for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) for each session in separate folders.</p> <p>Variables of the main experiment are described in the file VARIABLE_CODES_MainExp.txt and the variables of the pilot experiments are described in the files VARIABLE_CODES_PilotRoughness.txt and VARIABLE_CODES_PilotShape.txt.</p>
High Tech and High Touch (HT2): Transforming Patient Engagement Through Portal Technology at the Bedside
ClinicalTrials.gov study NCT02943109. IPD Sharing: YES. Countries: 1. Publications: 3.
Spermathecae touching in front and behind concave median part (f3) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Spermathecae touching in front and behind concave median part (f3)
Spermathecae not touching (f1) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Spermathecae not touching (f1)
Figure 1. Iporangaia pustulosa male touching the metatarsal gland IV in Mode of use of sexually dimorphic glands in a Neotropical harvestman (Arachnida: Opiliones) with paternal care
Figure 1. Iporangaia pustulosa male touching the metatarsal gland IV on a leaf (seta).
Grid-type transparent conductive thin films of carbon nanotubes as capacitive touch sensors
<p>This dataset contains the measurement data for figures (graphs) published in journal article:</p><p>Grid-type transparent conductive thin films of carbon nanotubes as capacitive touch sensors</p><p>by Ronja Valasma, Eva Bozo, Olli Pitkänen, Topias Järvinen, Aron Dombovari, Melinda Mohl, Gabriela Simone Lorite, Janos Kiss, Zoltan Konya and Krisztian Kordas</p><p>Published 11 May 2020 • © 2020 The Author(s). Published by IOP Publishing Ltd</p><p>Nanotechnology, Volume 31, Number 30</p><p>Citation: Ronja Valasma et al 2020 Nanotechnology 31 305303</p><p>DOI 10.1088/1361-6528/ab8590</p>
Buddha Earth Touching Gesture
Buddha in the earth-touching gesture, Thailand, 1500, National Museum (Copenhagen, Denmark). Made with Memento Beta Source: Objaverse 1.0 / Sketchfab
Touch sensation requires the mechanically-gated ion channel Elkin1
<p>The extraordinary speed of touch perception is enabled by mechanically-activated ion channels, the opening of which excites cutaneous sensory endings to initiate sensation. We identify Elkin1(1) as an ion channel likely gated by mechanical force necessary for normal behavioral touch sensitivity in mice. Touch insensitivity in Elkin1-/- mice was caused by a loss of mechanically-activated currents (MA-currents) in around half of all sensory neurons that are activated by light touch (low threshold mechanoreceptors, LTMRs). Reintroduction of Elkin1 into sensory neurons from Elkin1-/- mice acutely restored MA-currents. Additionally, siRNA mediated knockdown of Elkin1 from induced human sensory neurons substantially reduced indentation-induced MA-currents supporting a conserved role for Elkin1 in human touch. Our data identify Elkin1 as a novel core component of touch transduction in mammals.</p>
Signal interference for 400 kV power line touch with drone
<p>This data is a high-speed logic level data log representing the interferences on communication signal while a drone is approaching a 400kV power line cable. The log has been created using HEIST (see below).</p> <p>Fell free to contact me if you need a script for reading these files.</p> <p>M. Skriver, A. Stengaard, and U. P. Schultz, “Heist: A hardware signal fault injection methodology enabling feasible software robustness testing,” in 2021 24th International Symposium on Design and Diagnostics of Electronic Circuits Systems (DDECS), 2021, pp. 123–126.</p>
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Dataset of: "The Relativity of Reaching: Motion of the touched surface alters the trajectory of hand movements"
<p>The repository contains the data of the paper:</p> <p>The Relativity of Reaching: Motion of the touched surface alters the trajectory of hand movementsAuthors: Colleen P. Ryan, Simone Ciotti, Priscilla Balestrucci, Antonio Bicchi, Francesco Lacquaniti, Matteo Bianchi, Alessandro Moscatelli</p>
The joint representation of self-motion and touch in superior colliculus neurons supporting active sensation
<p><span><span>Localizing</span><span> objects using active </span><span>sensing</span><span> requires </span><span>the</span><span> brain </span><span>to</span> <span>integrate</span> <span>a </span><span>map </span><span>sensory</span><span> space against</span><span> an ongoing </span><span>estimate</span><span> of </span><span>body position</span><span>. </span><span>While </span><span>such computations </span><span>are</span><span> often</span> <span>ascribed to </span><span>the </span><span>cerebral </span><span>corte</span><span>x</span><span>, we examined the </span><span>midbrain</span><span> superior colliculus (SC), due to its </span><span>close relationship</span><span> with</span> <span>the sensory periphery </span><span>as well as</span><span> higher, motor-related brain regions. </span><span>Using high-density electrophysiology and movement tracking, w</span><span>e discovered tha</span><span>t</span> <span>the </span><span>on-going </span><span>kinematics of </span><span>whisker motion</span> <span>and locomotion </span><span>speed </span><span>accurately predict </span><span>the firing rate of</span><span> mouse</span><span> SC neurons</span><span>. </span><span>Neural activity was best predicted by m</span><span>ovement</span><span>s </span><span>occurring</span> <span>either </span><span>in the past, present, or future, </span><span>revealing</span><span> that the SC population continuously </span><span>estimates</span> <span>a</span> <span>trajector</span><span>y of self-motion</span><span>. </span><span>Neural </span><span>selectivity</span><span> for </span><span>combinations </span><span>of kinematic features built </span><span>an accurate</span><span> representation of</span><span> whisker </span><span>position</span> <span>with</span><span> high temporal resolution.</span> <span>Half of all self-motion encoding</span> <span>neurons displayed a </span><span>touch</span><span> response </span><span>as</span><span> an object entered the active whisking field. </span><span>Trial-to-trial v</span><span>ariation in the size of this response was explained by the </span><span>position</span><span> of </span><span>the whisker</span><span> upon touch.</span><span> Taken together, these data </span><span>indicate</span><span> that SC neurons linearly combine an internal estimate of </span><span>body </span><span>movements</span><span> with </span><span>external</span><span> stimulation to </span><span>enable active tactile localization</span><span>.</span></span><span> </span></p>
What's in a name? Role of verbal context in touch
<p>Can a name (i.e., verbal context) change how we react to and perceive an object? This question has been addressed several times for chemosensory objects, but never for touch. To address this, two studies were run. In each, we allocated participants to a Positive, Neutral or Negative Group, and asked them to touch the same four objects, twice – first, named by the experimenter according to their Group-name, and second, named by the participant. Participants were timed as they touched and rated the objects on pleasantness and disgust. Negative-named objects were touched for shorter durations, and rated more negatively, than neutral-named objects, and positive-named objects were touched for the longest and rated most positively. In the second presentation, most objects (>90%) were named by participants in accord with their assigned Group-names. The similarity of these findings to chemosensory verbal context effects and their mechanistic basis is discussed.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.