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555 results for “upper extremity”
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity
<p>This dataset contains evaluation data of the paper "Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity".</p> <p><strong>Motivation</strong></p> <p>We address the question whether the assumptions of signal-dependent and constant motor noise in a full skeletal model of the human upper body, together with the objective of movement time minimization, can predict reaching movements.</p> <p>For evaluation of the learned policy, two tasks are defined in the paper: a Fitts' Law type task and an elliptic via-point task.</p> <p><strong>General description of the dataset</strong></p> <ul> <li><strong>Fitts' Law Type Task</strong> <ul> <li>This dataset incorporates detailed information of all 6500 synthesized movements generated in the Fitts' Law type task (following the ISO 9241-9 standard).</li> <li> <p>For each of the 10 task conditions differing in distance between targets ("dist<xxx>" in filename)<br> and ID ('ID<xxx>' in filename), there are two .csv-files:<br> - one with detailed trajectory information on a sample-to-sample basis ("ISO_SAMPLES" in filename), and<br> - one with aggregated movement information on an episode basis ("ISO_METRICS" in filename).</p> </li> <li> <p>In addition, for each task condition and each of the 13 movement directions in the Fitts' Law type task,<br> we include 6 figures: Position, Velocity, and Acceleration Profiles, as well as 3D movement path, Phasespace, and Hooke plots.<br> Apart from the 3D plots, all figures use centroid projections of the respective trajectory onto the vector between initial and target position.<br> The first integer in the file name denotes the movement direction number, starting with "0" for movements between the targets 1 and 2, "1" for movements between the targets 2 and 3 etc.<br> The file "6_distance0.35_ID2_policy2100000_phasespace.png", e.g., shows velocity plotted againt position for all 50 movements between the targets 7 and 8 in the task condition with ID 2 and 35cm diameter of the target circle (see Fig 2 in Paper).</p> </li> </ul> </li> <li> <p><strong>Elliptic Task</strong></p> <ul> <li> <p>This dataset also contains two CSV-files with data of the trajectory generated by the final policy in the elliptic task:<br> - one with detailed trajectory information on a sample-to-sample basis ("ELLIPSE_SAMPLES" in filename), and<br> - one with aggregated movement information on an episode basis, where a new episode starts<br> whenever the target on the ellipse given to the policy switches ("ELLIPSE_METRICS" in filename).</p> </li> </ul> </li> </ul> <p><strong>Description of the .csv-files</strong></p> <ul> <li><em>SAMPLES </em>Files <ul> <li>"time": time after reaching the initial target (target 1 in Fig 2) for the first time (in seconds)</li> <li>"elv_angle_pos" - "flexion_pos": angle of respective independent DOF (in radians) *</li> <li>"elv_angle_vel" - "flexion_vel": angular velocity of respective independent DOF (in radians/s) *</li> <li>"end-effector_xpos_x" - "end-effector_xpos_z": 3D position of end-effector in global coordinates (in meters) *</li> <li>"target_xpos_x" - "target_xpos_z": 3D position of target sphere in global coordinates (in meters) *</li> <li>"end-effector_xvelp_x" - "end-effector_xvelp_z": positional velocities of end-effector (in meters/s) *</li> <li>"target_xvelp_x" - "target_xvelp_z": positional velocities of target sphere (in meters/s) *</li> <li>"accsensor_end-effector_x" - "accsensor_end-effector_z": positional acceleration of end-effector (in meters/s^2) *</li> <li>"E_elv_angle" - "E_flexion": activation of respective independent DOF *</li> <li>"E_elv_angle_derivative" - "E_flexion_derivative": derivative of activation of respective independent DOF *</li> <li>"difference_vec_x" - "difference_vec_z": vector between the end-effector attached to the index finger and the target, pointing towards the target (in meters) *</li> <li>"centroid_vel_projection": projection of end-effector velocity towards target (in meters/s) *</li> <li>"target_width": radius (!) of the target sphere (in meters) *</li> <li>"A_elv_angle" - "A_flexion": action vector</li> <li>"thorax_tx_frc" - "wrist_hand_r3_frc": net external force at respective DOF (including dependent and fixed DOFs such as "thorax_tx" (thorax translation))</li> <li>"reward": reward obtained in this step</li> <li>"step_type": 0=initial step of episode, 1=intermediate step of episode, 2=terminal step of episode</li> <li>"target_switch": whether the target switched in this step</li> <li>"discount": internal value of tf-agents (does not correspond to the discount factor gamma, which is additionally applied!)</li> <li>"thorax_tx_pos" - "wrist_hand_r3_pos": angle of respective dependent DOF (in radians)</li> <li>"thorax_tx_vel" - "wrist_hand_r3_vel": angular velocity of respective dependent DOF (in radians)</li> </ul> </li> <li><em>METRICS </em>Files <ul> <li>Index: episode ID</li> <li>"Init_X" - "Init_Z": initial position in global coordinates (in meters)</li> <li>"Init_X" - "Init_Z": target position in global coordinates (in meters)</li> <li>"Init_Distance": distance between last target (i.e., desired initial position) and current target (in meters)</li> <li>"Target_Width_Diameter": target width diameter (in meters)</li> <li>"Movement_ID": Index of Difficulty of current movement (using the Shannon Formulation) (in bits)</li> <li>"target_accuracy": 1 - (<remaining distance to target center at the end of the episode>/<target radius>) if end-effector is inside target, 0 else</li> <li>"movement_time": duration of the episode (in seconds)</li> <li>"episode_successful": whether episode terminated successfully within the permitted 1.5 seconds</li> <li>"dist2target": remaining distance to target center at the end of the episode (in meters)</li> </ul> </li> </ul> <p>-------------------------------------<br> * included in state space</p>
Fig. 4. New Upper Turonian belemnite specimens from the Bohemian Cretaceous Basin. A–E in Extremely Rare Turonian Belemnites from the Bohemian Cretaceous Basin and Their Palaeogeographical Importance
Fig. 4. New Upper Turonian belemnite specimens from the Bohemian Cretaceous Basin. A–E. Praeactinocamax bohemicus (Stolley) A. IGP_Upo2009/5 in lateral (A1) and central (A2) views. B. IGP_Upo2009/3 in lateral (B1), ventral (B2), dorsal (B3) views, and alveolar end (B4). C. IGP_Upo2009/2 in ventral (C1), dorsal (C2), and lateral (C3) views, alveolar end (C4). D. IGP_Upo2009/1 in ventral (D1), lateral (D2), and dorsal (D3) views, alveolar end (D4). E. IGP_Upo2009/4 in ventral view (E1), alveolar end (E2). F. Praeactinocamax cf. strehlensis, IGP_Upo2009/8 in ventral (F1), dorsal (F2), and lateral (F3) views, pseudoalveolus (F4). Scale bars 10 mm.
Transhumeral Loading During Advanced Upper Extremity Activities of Daily Living
<p>Percutaneous osseointegrated (OI) implants for direct skeletal attachment of upper extremity prosthetics represent an alternative to traditional socket suspension that may yield improved patient function and satisfaction. This is especially true in high-level, transhumeral amputees where prosthetic fitting is challenging and abandonment rates remain high. However, maintaining mechanical integrity of the bone-implant interface is crucial for safe clinical introduction of this technology. The collection of population data on the transhumeral loading environment will aid in the design of compliance and overload protection devices that mitigate the risk of periprosthetic fracture. We collected marker-based upper extremity kinematic data from non-amputee volunteers during advanced activities of daily living (AADLs) that applied dynamic loading to the humerus. These kinematic data are available for download and will aid in the development of overload protection devices and appropriate post-operative rehabilitation protocols that balance return to an active lifestyle with patient safety.</p>
Informing upper extremity rehabilitation through endpoint kinematics of the functional reaching movement after stroke
<p>Background. Stroke is a leading contributor to upper limb disability. Current assessments of motor ability are at best subjective estimates. Rehabilitation could be more effective when informed by an individual's specific capacities rather than diagnostic observation. This study characterizes sub-movements of interest during functional reach-to-target tasks with endpoint analysis.\newline<br> <br> Methods. Using Fugl-Meyer (FM) scores and reach capacities, stroke survivors were placed in either mild or severe impairment groups. Individual completed sets of reach-to-target tasks over two visits. The averaged reach curve for each subject was analyzed for angular orientation and velocity profile. The Procrustes method of statistical shape analysis was modified to identify reaching sub-movements that were incongruous to control sub-movements.\newline</p> <p>Findings. Reach-to-target curves were investigated for 15 individuals with mild impairment (FM <span class="math-tex">\(52 \pm 9\)</span>) and 14 individuals with severe impairment (FM <span class="math-tex">\(16 \pm 7\)</span>). The endpoint kinematic analysis found that individuals with severe impairment demonstrate lower peak velocities (centimeters per second) [<span class="math-tex">\(28.5 \pm 17.3 , 80.3 \pm 29.5\)</span>] which occurred sooner in the movement (milliseconds) [<span class="math-tex">\(36.81 \pm 27.0, 59.6 \pm 14.8\)</span>]. The mild group demonstrated variability in orientation in the medial-lateral space, and movement initiation that was congruous to healthy movement. The severe impairment group demonstrated variability in orientation in every axis and showed incongruous initiation and completion behaviors, with some comparable movement in between. \newline<br> <br> Interpretation. Endpoint analysis is a valuable option for clinicians to inform physiotherapy. In addition to kinematic metrics such as orientation and velocity, sub-movements that are comparable to healthy movement can be identified in both mild and severe impairment.</p>
Rehabilitation of the Upper Extremity With Enhanced Proprioceptive Feedback Following Incomplete Spinal Cord Injury
ClinicalTrials.gov study NCT00833105. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Bilateral Priming for Upper Extremity Hemiparesis in Older Adults
ClinicalTrials.gov study NCT02277028. IPD Sharing: NO. Countries: 1. Publications: 2.
Investigation of the Effectiveness of Visual Feedback Training on Upper Extremity Functions in Cerebral Palsy
ClinicalTrials.gov study NCT03726385. IPD Sharing: UNDECIDED. Countries: 1. Publications: 26.
Different Treatment Methods Effect on Upper Extremity Spasticity and Decreased Functionality After Stroke
ClinicalTrials.gov study NCT05747950. IPD Sharing: YES. Countries: 1. Publications: 3.
Upper Extremity Function in Multiple Sclerosis Patients With Advanced Disability Treated With Ocrevus
ClinicalTrials.gov study NCT03562975. IPD Sharing: NO. Countries: 1. Publications: 14.
Mechanisms of Upper-Extremity Motor Recovery in Post-stroke Hemiparesis
ClinicalTrials.gov study NCT00125658. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Application of a Reimbursable Form of Constraint-Induced Movement Therapy for Upper Extremity
ClinicalTrials.gov study NCT05311384. IPD Sharing: NO. Countries: 1. Publications: 19.
Safety and Efficacy Study of Photodynamic Therapy With Levulan Kerastick + Blue Light for Actinic Keratoses on the Upper Extremities
ClinicalTrials.gov study NCT02137785. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Electrical Stimulation of the Dentate Nucleus for Upper Extremity Hemiparesis Due to Ischemic Stroke
ClinicalTrials.gov study NCT02835443. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of a Compliant Arm Support on Post-stroke Upper Extremity Range of Motion
ClinicalTrials.gov study NCT03867838. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Virtual Reality Applications on Upper Extremity Functions in Patients With Duchenne Muscular Dystrophy
ClinicalTrials.gov study NCT07039799. IPD Sharing: NO. Countries: 0. Publications: 2.
Functional Electrical Stimulation (FES) for Upper Extremity Recovery in Stroke
ClinicalTrials.gov study NCT00142792. IPD Sharing: NO. Countries: 1. Publications: 12.
The Efficacy of Additional Motor Training Dosage During the Early Stages Post Stroke on the Upper Extremity Recovery
ClinicalTrials.gov study NCT07056049. IPD Sharing: YES. Countries: 1. Publications: 4.
Efficacy of Functional Electrical Stimulation (FES) in Persons Receiving Botulinum Neurotoxin for Upper Extremity Spasticity
ClinicalTrials.gov study NCT00462449. IPD Sharing: Not stated. Countries: 1. Publications: 25.
Patient Targeted Upper Extremity Rehabilitation After Stroke
ClinicalTrials.gov study NCT01819506. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Feasibility of Home-based Virtual Reality Rehabilitation for the Upper Extremity in Subacute and Chronic Stroke
ClinicalTrials.gov study NCT03559829. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.