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793 results for “upper limb”
Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)
<p>These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing).</p> <p>Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the upper limb joints in planar robot-aided therapies
<p>These files contain the raw data (acquired from different users) necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the upper limb joints in planar robot-aided therapies</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Jorge A. Díez, Luis D. Lledó, Francisco J. Badesa, Nicolas Garcia-Aracil</p> <p>Conference: ICORR 2015, IEEE 14th International Conference on Rehabilitation Robotics, August 2015</p> <p><br> All the orientations are expressed regarding the origin of the robot.</p> <p>a) Robot Joints: Planar robot joints acquired during the experiment, in radians (j1-j3 columns). This robot is referenced in the paper.<br> b) Quaternion IMU shoulder: Unit quatenion acquired through a 9DoFs Inertial Measurement Unit (IMU) developed by Shimmer (qw1-qz columns).<br> c) Upper arm acceleration: Acceleration acquired from a 3-axial accelerometer developed by Shimmer (X-Z columns). It is normalized regarding the gravity (9.81m/s^2).<br> d) Quaternion Tracker onto Shoulder: unit quaternion of the tracker placed onto the shoulder acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).<br> e) Quaternion Tracker onto Upper Arm: unit quaternion of the tracker placed onto the upper arm acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).</p>
Raw data corresponding to the scientific paper: "A modular telerehabilitation architecture for upper limb robotic therapy" (Advances in Mechanical Engineering 2017, Vol. 9(1) 1-13)
<p>Acquired raw data necessary to implement the adaptive control strategy grounded on multimodal information.<br> In addition, raw data for the computation of the communication parameters needed for the assessment of the implemented telerehabilitation architecture are provided.</p> <p>a) End-effector positions and velocities (x, y, vx, vy) in three conditions: healthy (Fig 9) and constraint simulated stroke behaviour (Fig 10) without robotic assistance and simulated stroke behavior with robotic assistance (Fig 11)</p> <p>b) Performance indicators and control parameters for all the recruited subjects in both conditions healthy behaviour and simulated stroke behaviour (Fig 12a and Fig 12b)</p> <p>c) Computational values for evaluating telerehabilitation performance (Table 1)</p> <p> </p> <p> </p>
Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)
<p>There are eight files with the following information:<br> - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment<br> - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest</p> <p> </p>
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>
Transcutaneous Kilohertz High-Frequency Alternating Current at 10 kHz for Upper-Limb Tremor in People with Parkinson's Disease: A double-blind, randomized, crossover study.
<p><strong><span>Abstract: <span>Background/Objectives:</span></span></strong><span> Preclinical studies have evidenced a peripheral nerve blockade with kilohertz high-frequency alternating current (KHFAC) stimulation. It could have a potential effect on aberrant nerve hyperactivity, such as tremor in people with Parkinson’s disease (PwPD). The objective was to investigate the effects of transcutaneous KHFAC at 10 kHz compared with sham intervention on tremor modulation, upper limb motor function, and adverse events in PwPD. <strong>Methods:</strong> This randomized, double-blind, crossover trial included PwPD, who received transcutaneous KHFAC and sham interventions, within a 48h washout period. Measurements were taken pre-intervention, during, immediately after, and 10 minutes post-intervention. The main outcomes were rest, postural, and kinetic tremor acceleration. Secondary outcomes were handgrip strength, nine-hole peg test (NHPT), movement onset time, and adverse events.<strong> </strong></span></p>
The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training
<p>This is the minimal dataset underlying the paper:</p> <p>"The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training"</p>
Text-fig. 8. Lanfrancia subglobosa E.REID et M.CHANDLER. a–c, e–g: Holotype V. 23014. a: reflected light. b, c: Surface renderings from micro-CT data. a, b: Lateral views with dorsal surface of locule facing forward and locule casts protruding in upper part. c: Apical view. d: Fruit showing two locule casts the dorsal surfaces of which face to the left and the right, V. 30417(1). e–g: Successive digital transverse sections showing four u to v to c-shaped locules from micro-CT data. h: Physical transverse section of specimen in (d). i–k: Physical transverse section, V. 30419 from Herne Bay, blue lines in K indicating limits of fibre layer lining the locule. l: Detail from (h), showing sclerenchyma composing the septa and central axis. m: Transverse section, enlargement from (i), showing anatomy of tissues adjacent to the dorsal infold. Blue lines indicate limits of the fibre layer lining the locule. n: Part of (m) recut, tangential section transecting the dorsal infold (central), both limbs of the locule cast, and peripheral parts of the pericarp on either side. o: Detail from (n), showing anatomy of the infold. Scale bars 5 mm in (a–h) (a–g share the same bar), 3 mm in (i), 1 mm in (j–m), 0.5 mm in (n), 0.2 mm in (o). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 8. Lanfrancia subglobosa E.REID et M.CHANDLER. a–c, e–g: Holotype V. 23014. a: reflected light. b, c: Surface renderings from micro-CT data. a, b: Lateral views with dorsal surface of locule facing forward and locule casts protruding in upper part. c: Apical view. d: Fruit showing two locule casts the dorsal surfaces of which face to the left and the right, V. 30417(1). e–g: Successive digital transverse sections showing four u to v to c-shaped locules from micro-CT data. h: Physical transverse section of specimen in (d). i–k: Physical transverse section, V. 30419 from Herne Bay, blue lines in K indicating limits of fibre layer lining the locule. l: Detail from (h), showing sclerenchyma composing the septa and central axis. m: Transverse section, enlargement from (i), showing anatomy of tissues adjacent to the dorsal infold. Blue lines indicate limits of the fibre layer lining the locule. n: Part of (m) recut, tangential section transecting the dorsal infold (central), both limbs of the locule cast, and peripheral parts of the pericarp on either side. o: Detail from (n), showing anatomy of the infold. Scale bars 5 mm in (a–h) (a–g share the same bar), 3 mm in (i), 1 mm in (j–m), 0.5 mm in (n), 0.2 mm in (o).
Upper limb movements can be decoded from the time-domain of low-frequency EEG
<p>How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.</p>
Qualitative Interview Data: Users and therapists perceptions of myoelectric multi-function upper limb prostheses with direct and pattern recognition control
<p>The data uploaded here were collected in 2016/2017 through semi-structured interviews with prosthesis users and hand therapists. Participants were mainly asked about satisfaction with their prosthetic device and about activities which they perform with the prosthesis. Interviews were conducted in Dutch and German language.</p> <p>All interview data are made publicly available, except for data of prosthesis users who were experienced with pattern recognition control (n=4). Due to the small number of these participants, their interview data is only available upon reasonable request to not compromise participant privacy. </p> <p> </p>
TAM survey questionnaire responses of a head-mounted assistive mouse controller for people with upper limb disability
<p>This dataset contains TAM survey questionnaire and the corresponding responses of a head-mounted assistive mouse controller for people with upper limb disability.</p>
A Study to Compare the Safety and Efficacy of Dysport® and Botox® in Adults With Upper Limb Spasticity.
ClinicalTrials.gov study NCT04936542. IPD Sharing: YES. Countries: 4. Publications: 1.
Efficacy And Safety Of Dysport In The Treatment Of Upper Limb Spasticity In Children
ClinicalTrials.gov study NCT02106351. IPD Sharing: YES. Countries: 8. Publications: 2.
Study to Evaluate Effects of DYSPORT® Injected in Lower and Upper Limb Combined With Guided Self-Rehabilitation Contract (GSC)
ClinicalTrials.gov study NCT02969356. IPD Sharing: YES. Countries: 4. Publications: 1.
Dysport® Adult Upper Limb Spasticity Extension Study
ClinicalTrials.gov study NCT01313312. IPD Sharing: YES. Countries: 9. Publications: 1.
Study to Assess Impact of Dysport Injections Early After Stroke on Upper Limb Spasticity Progression
ClinicalTrials.gov study NCT02321436. IPD Sharing: YES. Countries: 4. Publications: 1.
Dysport® Adult Upper Limb Spasticity
ClinicalTrials.gov study NCT01313299. IPD Sharing: YES. Countries: 9. Publications: 4.
Post-stroke upper limb kinematics of a set of daily living tasks
<p>This dataset contains upper limb kinematics of twenty chronic stroke-subjects and five healthy control subjects that were collected within a cross-sectional, observational study (Clinicaltrials.gov Identifier: NCT03135093). Participants were measured when performing a set of 30 daily living activities, including gesture movements, grasping actions and tool-mediated upper limb activities. Kinematic parameters were captured by use of inertial sensing and stored in software specific XML file format (.mvnx) allowing import to programs such as MATLAB and Microsoft Excel. Each mvnx-file represents one trial execution and is named according to the participant ID, task number, tested upper limb and repetition.</p> <p>This data collection is part of a large multimodal dataset collected and shared between collaborators of a European project under grant agreement No.688857.</p>
Dataset of Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
<p>MATLAB Dataset for the paper. </p> <p>Paper Abstract:</p> <p>Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).</p>
Data about upper limb kinematics to test the efficacy of Action Observation Treatment
<p>The dataset contains the upper-limb kinematics collected in 40 participants before and after a short-term immobilization of the right arm. Three reach-to-grasp movements were required, targeting an object placed a) frontally at the level of the shoulders (A_low), b) frontally above the head (A_high), or c) laterally at the level of the shoulders (L_low). These movements were intended to test different degrees of freedom of the shoulder joint.</p> <p>Half of the participants underwent a virtual-reality action observation treatment (see Rizzolatti et al, Neuroscience and Biobehabioral Reviews, 2021), while the control group underwent a non-motor VR stimulation.</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.