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740 results for “lower limbs”
Comparative Study of the Effects of Dry Needling and Botulinum Toxin Type A as a Treatment for Lower Limb Post-stroke Spasticity
ClinicalTrials.gov study NCT06296082. IPD Sharing: YES. Countries: 3. Publications: 0.
PROs in Chronic Low Back Pain Patients With Accompanying Lower Limb Pain (Neuropathic Component) Treated With Pregabalin
ClinicalTrials.gov study NCT02273908. IPD Sharing: NO. Countries: 1. Publications: 1.
BOTOX® Treatment in Adult Patients With Post-Stroke Lower Limb Spasticity
ClinicalTrials.gov study NCT01575054. IPD Sharing: Not stated. Countries: 9. Publications: 1.
Observational Study on JuniOrtho Plating System for Deformities and Fractures Treatment in Lower Limb
ClinicalTrials.gov study NCT05245617. IPD Sharing: NO. Countries: 1. Publications: 6.
Mechanical energy fluctuation in lower limbs during walking in participants with and without total hip replacement
Open the record for dataset details and reuse information.
Error Related Potential in motion to stop the gait with a lower limb exoskeleton
<h2>Description</h2> <p>This dataset contains EEG signals from experiments designed to evoke Error-Related Potentials (ErrP) in motion, during gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP is elicited using Tactile stimuli that activates while walking and, once it deactivates, the exoskeleton stops.</p> <p>The experiment consists in a circuit with color marks on the floor, with 4 regions: 2 Gait Regions, where the subject has to keep walking, and 2 Stop Regions, where the subject tries to stop the exoskeleton using motor imagination. At the beginning of each repetition, the exoskeleton activates automatically and, while walking, they must perform two mental tasks: Relax (R), at Gair Regions to keep walking, and Motor Imagery (I) of stop, at Stop Regions to deactivate the exoskeleton and stop. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). Since it is an open-loop experiment and the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE). </p> <p>For instance, one the subject enters in the Gait Region and maintains an idle state while walking to continue with the gait. In RC (Relax Correct) they cross the region without any stop, but in RE (Relax Error) the feedback activates and the exoskeleton erroneously stops. Conversely, in IC (Imagination Correct, the subject imagines the sensation of stop walking in their muscles, and the feeedback activates and the exoskeleton stops the gait, but during IE (Imagination Error), they walk through the region and the exoskeleton does not stop despite the motor imagery. Therefore, ErrP is elicited by the stimuli in RE and can be compared with the absence of ErrP (NoErrP) in IC, where the stimulus activates but should not evoke an error.</p> <p>Each subject participates in three sessions, consisting of 20 trials. In each trial, 4 mental tasks are performed, 2 Relax and 2 Imagination, interleaved. Thus, in each session, a total of 12 ErrP and 28 NoErrP signals are recorded in the dataset. Except subject R06, who only participated in 2 sessions. </p> <p> </p> <h2>Data information</h2> <p>A trial consists of a Matlab structure that stores all information related to the trial experiment. </p> <ul> <li><em>data_EEG</em>: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes).</li> <li><em>data_preprocessed_EEG</em>: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in <em>session.conf.info.preprocessingSteps.ListPreprocessingSteps</em>.</li> <li><em>trigger_EEG</em>: Information related to signal quality and missing data while recording. </li> <li><em>data_EXO</em>: Exoskeleton recorded data with a sampling rate of 250Hz.</li> <li><em>data_preprocessed_EXO: </em>The same data recorded by the exoskeleton in <em>data_EXO</em>, since it does not require the application of any filter.</li> <li><em>trigger_EXO</em>: Empty vector. </li> <li><em>data_Actuators</em>: Arduino response when activates (1) and deactivates (-1) the feedback. </li> <li><em>data_preprocessed_Actuators: </em>The same Arduino resposes recorded in <em>data_Actuators</em>, because it does not require any filter application. </li> <li><em>trigger_Actuators</em>: Empty vector. </li> <li><em>task_EEG</em>: Vector that associates a task to each signal sample.</li> <li><em>task_index_EEG</em>: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task. </li> <li><em>task_order_EEG</em>: Vector that increments a unit with each task change. </li> <li><em>event_EEG</em>: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino. </li> <li><em>conf</em>: Configuration employed for data acquisition and preprocessing. <ul> <li><em>acquisition</em>: User and signals acquisition information. <ul> <li><em>user_code</em>: User code name.</li> <li><em>feedback</em>: Trial in openloop (User do not have control of the system).</li> <li><em>feedbackErrP</em>: Feedback type employed during the trial.</li> <li><em>readfile</em>: Path to read files after its acquisition.</li> <li><em>saveSession_Script</em>: Script used to save the recorded data.</li> <li><em>writeResults</em>: Path to save the recorded data.</li> <li><em>device</em>: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc. </li> <li><em>task</em>: Information about tasks occurring during the trial. <ul> <li><em>task_list</em>: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task).</li> <li><em>sequence_tasks</em>: List of tasks in order of execution.</li> <li><em>sequence_times</em>: List with the duration of each task in the sequence.</li> </ul> </li> <li><em>deviceOutput</em>: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off.</li> <li><em>eye_index</em>: Indexes of EOG electrodes.</li> <li><em>EEG_index</em>: Indexes of EEG electrodes.</li> <li><em>inertial_index</em>: Indexes of inertial electrodes.</li> <li><em>file_name</em>: Trial name.</li> <li><em>num_epochs</em>: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples. </li> </ul> </li> <li><em>preadjustment</em>: Empty list. </li> <li><em>preprocessing</em>: Information of the preprocessing filters, parameters and order of application.</li> <li><em>processing</em>: Not necessary for this analysis. </li> <li><em>static</em>: Information used internally by the architecture for its correct operation.</li> <li><em>info</em>: Important information about filters, their order and indexes in <em>data_processed_EEG</em>.</li> </ul> </li> <li><em>times</em>: Struct with information of the devices synchronization and preprocessing times.</li> <li><em>times_processing</em>: Processing duration times. </li> </ul>
Developing an in-depth understanding of the prevalence, risk factors and treatment recommendations for phantom limb pain, and patient-generated care priorities for people who have undergone lower limb amputations.
<p>The file holds data collected for a series of four studies on phantom limb pain. </p>
A Sensory Neuroprosthesis Enhances Recovery from Treadmill-Induced Stumbles for Individuals with Lower Limb Loss
<p>Dataset of treadmill-induced stumble recovery for three participants with lower limb loss who received a sensory neuroprosthesis that restores plantar somatosensory feedback corresponding to prosthesis foot-foor interactions. </p> <ul> <li><strong>Results.zip</strong> contains the metrics calculated for analysis of stumble recovery, which includes: trunk angular sway, peak trunk flexion angular velocity, and peak ground reaction force magntidues. </li> <li><strong>Raw.zip</strong> contains raw data collected during the experiments. <ul> <li>LLX - Data for a particular participant <ul> <li>SessionX - Data collected during a single session of the experiment, with each session being one day. <ul> <li>DFlow - Contains treadmill speed data. </li> <li>Visual3D - Contains biomechanical and force plate data. <ul> <li>StaticX - Data collected during a static trial where the participant is standing in a T-pose. This data was used to calculate body weight during each session. Data on which blocks correspond to which static trials can be found in LLX_ProcessData.m This has the same directory structure as TrialX. </li> <li>TrialX - Data collected during one block of walking with multiple perturbations. Data on which blocks had the SNP active or inactive can be found in the <code>FILE_DATA</code> variable in LLX_Analysis.m files in Code.zip. <ul> <li>Analog - Contains force plate data collected from the instrumented treadmill, which was used to calculated ground reaction force metrics. </li> <li>ForcePlate - Contains force plate data calculated from the instrumented treadmill and interpolated to the Marker data.</li> <li>LinkModel - Contains data from the biomechanical model, including trunk angle. </li> <li>Markers - Contains marker data from motion capture. </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Code.zip </strong>contains the code used to process the raw data in Raw.zip and extract the metrics found in Results.zip. To run this, follow the steps below.<br> <ol> <li>Copy files from Projects/TreadmillPerturbations/Run/ to directory where processed data will be stored. </li> <li>In this same folder from (1), create the following directories: <ul> <li>Processed/LL1/Session1</li> <li>Processed/LL1/Session2</li> <li>Processed/LL1/Session3</li> <li>Processed/LL1/Session4</li> <li>Processed/LL1/Session5</li> <li>Processed/LL2/Session1</li> <li>Processed/LL2/Session2</li> <li>Processed/LL2/Session3</li> <li>Processed/LL2/Session4</li> <li>Processed/LL2/Session5</li> <li>Processed/LL3/Session1</li> <li>Processed/LL3/Session2</li> <li>Processed/LL3/Session3</li> <li>Processed/LL3/Session4</li> <li>Processed/LL3/Session5</li> </ul> </li> <li>In LLX_ProcessData.m, edit <code>raw_data_all</code> variable to path where raw data is stored (where data from Raw.zip has been extracted to).</li> <li>Run LLX_ProcessData.m. Processed data will appear as .mat files in directories created in (2).</li> <li>Run LLX_Analysis.m. This will reproduce the results found in Results.zip.</li> </ol> </li> </ul>
Atropine Versus Glycopyrrolate in Preventing Spinal Anesthesia Induced Hypotension in Lower Limb Surgeries
ClinicalTrials.gov study NCT03580889. IPD Sharing: NO. Countries: 1. Publications: 10.
A Study on the Biomechanical Mechanisms of Orthotic/Physical Training Correction of Hallux Valgus and Its Impact on the Lower Limbs
ClinicalTrials.gov study NCT07036120. IPD Sharing: NO. Countries: 1. Publications: 1.
Efficacy and Safety of Diosmin 600mg Versus Placebo on Painful Symptomatology in Patients With Chronic Venous Disease of Lower Limbs
ClinicalTrials.gov study NCT01532882. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Stellate Ganglion Block for Preserving Arteriovenous Fistula in Hemodialysis Patients Undergoing Major Lower Limb Orthopedic Surgery
ClinicalTrials.gov study NCT06300658. IPD Sharing: YES. Countries: 1. Publications: 1.
Kinematics of Lower Limb, Pain and Function of the Women With Patellofemoral Pain Syndrome
ClinicalTrials.gov study NCT01804608. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Skin Microcirculation in Patients With Lower Limb Atherosclerosis
ClinicalTrials.gov study NCT07312383. IPD Sharing: YES. Countries: 1. Publications: 4.
The Effect of Prophylactic Antibiotics on Surgical Site Infection Lower Limb Skin Excisions
ClinicalTrials.gov study NCT03357419. IPD Sharing: NO. Countries: 1. Publications: 9.
Chronic Wound Care of Lower Limb in M@diCICAT Center at CHU de Martinique
ClinicalTrials.gov study NCT05491291. IPD Sharing: NO. Countries: 1. Publications: 2.
Bioequipotency Study of Idrabiotaparinux and Idraparinux in Patients With Deep Venous Thrombosis of the Lower Limbs
ClinicalTrials.gov study NCT00311090. IPD Sharing: Not stated. Countries: 20. Publications: 3.
Adherence and Perspiration While Wearing Lower Limb Prostheses
ClinicalTrials.gov study NCT03900845. IPD Sharing: YES. Countries: 1. Publications: 0.
Resisted Sprint and Plyometric Training on Lower Limb Functional Performance in Young Adult Male Football Players.
ClinicalTrials.gov study NCT04837300. IPD Sharing: NO. Countries: 1. Publications: 1.
Hydrophobic Tubes for the Treament of Lower and Upper Limb Lymphedema
ClinicalTrials.gov study NCT05970068. IPD Sharing: NO. Countries: 1. Publications: 2.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.