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47 results for “hand movements”
Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions
<p>This repository contains raw surface Electromyography signals termed surface Electromyograms (<a href="https://en.wikipedia.org/wiki/Electromyography">sEMG</a>) recorded with 8 circular surface Ag/AgCl pairs of electrodes placed circumferentially around the forearm of the dominant arm in 10 able-bodied individuals (5 Females and 5 Males). The proposed method for processing sEMG data with subjects' characteristics and protocol can be found in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>For each subject, sEMG was recorded from <strong>three recording electrode array positions</strong> termed P1, P2, and P3 for 9 hand movements. We provide a compressed .7z folder with 10 sub-folders for each subject named by <strong>subject ID</strong> (ID1, ID2, ... ID10). Each sub-folder contains 27 .txt data files (for 9 movements × 3 electrode array positions), except for subject ID7 (there are 24 .txt records, since three records for wrist extension EX in P1, P2, and P3 positions got corrupted in subject ID7). Average size of 10 sub-folders is 167.50 ± 27.02 MB with maximum of 194 MB and minimum of 117 MB.</p> <p>The subjects performed following hand movements from the reference resting position –relaxation, R (explained in-detail in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): (1) spherical power grasp, PS, (2) three finger sphere grasp, 3F, (3) two finger prismatic grasp, PP, (4) wrist flexion, FL, (5) wrist extension, EX, (6) radial deviation, RD, (7) ulnar deviation, UD, and then forearm rotation i.e. (8) pronation, PR, and (9) supination, SU. PS, 3F, PP, FL, EX, RD, UD, PR, and SU correspond to <strong>type of hand movement</strong> in naming convention for .txt data files.</p> <p><a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">Hand movements YoutTube playlist</a> contains explanatory videos for 9 hand movements recorded in this study, and we also provide corresponding .wmv here in the "movies hand movements.7z". Naming convention for .wmv files is <strong>type of hand movement</strong> with both full name and abbreviation for the movement (for example "radialDeviation-RD.wmv").</p> <p>Naming convention for .txt data files within 10 sub-folders is: <strong>subjects ID _ type of hand movement _ recording electrode array position</strong> (for example: "ID1_3F_P1.txt" in sub-folder ID1, "ID9_RD_P3.txt" in sub-folder ID9).</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/record/4039550/files/EMG%20dataset.7z?download=1">EMG dataset.7z</a>, 267 .txt data files, text format</li> <li><a href="https://zenodo.org/record/4039550/files/movies%20hand%20movements.7z?download=1">movies hand movements.7z</a>, 9 .wmv files, explanatory hand movement videos (also available on <a href="https://www.youtube.com/playlist?list=PLI3SYeiSufnBo6UDAZt9NJO9ecb-InJqb">YouTube</a>)</li> <li><a href="https://zenodo.org/record/4039550/files/README.txt?download=1">README.txt</a>, metadata for data files, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point* according to the following structure</strong></p> <ol> <li>column - CH1** (recorded samples from channel 1)</li> <li>column - CH2** (recorded samples from channel 2)</li> <li>column - CH3** (recorded samples from channel 3)</li> <li>column - CH4** (recorded samples from channel 4)</li> <li>column - CH5** (recorded samples from channel 5)</li> <li>column - CH6** (recorded samples from channel 6)</li> <li>column - CH7** (recorded samples from channel 7)</li> <li>column - CH8** (recorded samples from channel 8)</li> </ol> <p>* For subjects ID1 and ID2 three decimal places are provided, while for other subjects 6 decimal places in .txt data files are provided.</p> <p>** Each data file contains at least 10 repetitions of the corresponding movement. In cases where file contains >10 repetitions (overall 162 .txt data files), we used the first or the last ten for the analysis (except for two files where short and strong artifact appeared during the measurement procedure, and corresponding movement repetitions were discarded) presented in <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>Sample rate was set at 1000 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. For more in-detail explanations of electrode array assemble and positioning for sEMG channels CH1, CH2, ... CH8, please refer to <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant preprint and dataset as:</p> <ol> <li> <p>Miljković, N. and Isaković, M.S., 2021. Effect of the sEMG electrode (re) placement and feature set size on the hand movement recognition. <em>Biomedical signal processing and control</em>, 64:102292. <em><a href="https://doi.org/10.1016/j.bspc.2020.102292">10.1016/j.bspc.2020.102292</a></em></p> </li> <li> <p>Miljković, N. and Isaković, M.S., 2020. Surface electromyogram (sEMG) dataset recorded from forearm for 9 hand movements and three electrode array positions. [Data set]. <em>Zenodo</em> <em><a href="https://zenodo.org/record/4039550">10.5281/zenodo.4039550</a></em>.</p> </li> </ol> <p><strong>ACKNOWLEDGEMENTS</strong> (from <a href="https://doi.org/10.1016/j.bspc.2020.102292">Miljković & Isaković 2021</a>): "Special appreciation the authors owe to Professor Mirjana B. Popović from the University of Belgrade for her kind support,precious guidance, and advice regarding this research which significantly improved the manuscript. Also, the authors would like to thank Dr Matija Štrbac from Tecnalia Serbia Ltd. for providing advice throughout the study.The authors thank all volunteers for their participation."</p>
Raw data of: "Controlling Hand Movements Relying on Tactile Illusions: A Model Predictive Control Framework"
<p>in Fig4_a.txt: raw the data for the plot of Fig4_a (x and y of the first simulated trajectory from trajectory 1 to 50)</p> <p>in Fig4_b.txt: raw the data for the plot of Fig4_b </p> <p>in Fig4_c.txt raw the data for the plot of Fig4_b. Each column corresponds to the optimal angle of the plate for each of the 50 trajectories simulated in Fig4_a</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4b. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>d et al., 2011). Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue.</p>
Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury
<p>We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).</p>
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>
Calibrated kinematic Ninapro hand movements data
<p>The kinematic data of the Ninapro (Non Invasive Adaptive Prosthetics) database includes calibrated kinematic data from 77 subjects, performing 40 hand movements and grasps. The whole database aims at allowing worldwide research groups to study hand kinematics. As well the dataset allows also to study the relationship between hand kinematics and muscle activity, since it is linked to sEMG datasets. The final goal of this work is to aid the progress in rehabilitation, physiotherapy, medicine, neuroscience and prosthetics. Therefore, the kinematic database improves the state of the art, being the most accurate, comprehensive and advanced reference for the largest kinematic database existing at the time of writing. <br> <br> <strong>Acquisition protocol</strong>:<br> Hand kinematics was measured using a 22-sensor CyberGlove II dataglove (CyberGlove Systems LLC, <a href="http://www.cyberglovesystems.com/">www.cyberglovesystems.com</a>). The CyberGlove is a motion capture data glove, instrumented with joint-angle measurements. It uses proprietary resistive bend-sensing technology to transform hand and finger motions into real-time digital joint-angle data.<br> <br> The calibrated kinematic Ninapro database includes 40 different movements of 77 intact subjects.<br> The subjects have to repeat several movement represented by movies that are shown on the screen of a laptop.<br> The experiment is divided in two exercises:<br> • Isometric, isotonic hand configurations and basic wrist movements<br> • Grasping and functional movements<br> Joint angles from raw data were calculated according to a very detailed calibration protocol. The protocol was previously applied to 10 different intact subjects and consists in recording 64 different poses or guided movements to obtain the gains and also some corrections because of cross-coupling effects for specific anatomical angles.</p> <p>Gracia-Ibáñez, V., Vergara, M., Buffi, J. H., Murray, W. M. & Sancho-Bru, J. L. Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes. C. Comput. Methods Biomech. Biomed. Eng. 20, 587–597 (2017).<br> <br> <strong>Data Sets</strong>:<br> For each subject and exercise, the database contains one file in Matlab format (<a href="http://www.mathworks.com/">www.mathworks.com</a>) with synchronized variables. The variables included are the following ones:<br> • subject: subject number;<br> • exercise: exercise number;<br> • glove (22 columns): uncalibrated signal from the 22 sensors of the Cyberglove. Details on the location of the sensors are available at the link: <a href="http://ninapro.hevs.ch/node/123">ninapro.hevs.ch/node/123</a>;<br> • angles(22 columns): calibrated signal from the 22 sensors of the Cyberglove by applying an across-subject calibration<br> • re-stimulus (1 column): the a-posteriori refined label of the movement;<br> • re-repetition (1 column): re-stimulus repetition index;</p> <p> • stimulus (1 column): the label of the movement;<br> • repetition (1 column): stimulus repetition index;<br> • order of angles (22 columns) : name of the angles corresponding to variable “angles”.</p> <p>Raw glove data are also included in the folder "Raw data".</p>
Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions
<p>Dataset relative to the following publication:</p> <p>Pazen, M., Uhlmann, L., van Kemenade, B.M., Steinsträter, O., Straube, B., Kircher, T. Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions. <em>NeuroImage</em>. DOI: <a href="https://doi.org/10.1016/j.neuroimage.2019.116309">10.1016/j.neuroimage.2019.116309</a></p> <p>Details are specified in the "readme.docx" file.</p>
Post Stroke Hand Functions: Bilateral Movements and Electrical Stimulation Treatments
ClinicalTrials.gov study NCT00369668. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Evaluating Neuromuscular Stimulation for Restoring Hand Movements
ClinicalTrials.gov study NCT03385005. IPD Sharing: YES. Countries: 1. Publications: 3.
Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates - Data and sample code
<p>This repository contains the processed data shown in the figures of the paper "<em>Intrafascicular peripheral nerve stimulation produces fine functional hand movements in primates", </em><em>Science Translational Medicine. </em>It also contains sample code for reference as a guideline to reproduce the analysis performed in the paper. </p>
Is Pericapsular Nerve Group Block Better Than Interscalene Nerve Block Regarding Effect on Hand Movement in Shoulder Scope Surgeries?
ClinicalTrials.gov study NCT07057947. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Children With Hemiparesis Arm and Hand Movement Project (CHAMP Study)
ClinicalTrials.gov study NCT01895660. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Robot Therapy for Rehabilitation of Hand Movement After Stroke
ClinicalTrials.gov study NCT04536987. IPD Sharing: NO. Countries: 1. Publications: 3.
CONSTRAINT-INDUCED MOVEMENT THERAPY (CIMT) VS. MIRROR THERAPY (MT) ON HAND FUNCTION AND SPASTICITY IN PATIENTS WITH HEMIPARESIS
ClinicalTrials.gov study NCT06910904. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Improving Hand Movements in Kids With One-Sided Arm Stiffness Cerebral Palsy Through Motion Minder Therapy (MoMT)
ClinicalTrials.gov study NCT06560281. IPD Sharing: NO. Countries: 1. Publications: 4.
Brunnstrom Movement Therapy Versus Mirror Therapy on Hand Function in Stroke
ClinicalTrials.gov study NCT05392543. IPD Sharing: NO. Countries: 1. Publications: 4.
Rehabilitation for Arm Coordination and Hand Movement in Systemic Sclerosis
ClinicalTrials.gov study NCT03482219. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of Short-intensity Modified Constraint-induced Movement Therapy on Hand Function in Stroke Patients.
ClinicalTrials.gov study NCT05916885. IPD Sharing: NO. Countries: 1. Publications: 3.
Movement-Related Brain Networks Involved in Hand Dystonia
ClinicalTrials.gov study NCT00137384. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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