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89 results for “Kinematic data”
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 employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot"
<p>This file contains the raw data necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Luis D. Lledó, Jorge A. Díez, Jose M. Catalan, Nicolas Garcia-Aracil.</p> <p>Conference: EMBC 2015, IEEE 37th International Conference in Medicine and Biology Society, August 2015.</p> <p>Raw data acquired necessary to perform thee algorithm introduced in this paper.</p> <p>a) Robot Joints: Robot joints generated to develop the simulation, in radians (j1-j7 colums). This robot is referenced in the paper.<br> b) Direct Upper Limb Joints: Upper limb joints generated to develop the simulation, in radians (q1-q7 columns). This data is used to simulate the accelerometer value.</p>
Test Data of Passenger kinematics in Lane change and Lane change with Braking Manoeuvres from Ghaffari et al., 2018
<p>This data was extracted by the "authors" of this dataset from the publication</p> <p><strong>Ghaffari, G., Brolin, K., Bråse, D., Pipkorn, B., Svanberg, B., Jakobsson, L., & Davidsson, J. (2018). Passenger kinematics in Lane change and Lane change with Braking Manoeuvres using two belt configurations: standard and reversible pre-pretensioner. <em>2018 IRCOBI Conference Proceedings, </em>pp. 12-14, <a href="http://www.ircobi.org/wordpress/downloads/irc18/pdf-files/80.pdf">http://www.ircobi.org/wordpress/downloads/irc18/pdf-files/80.pdf</a>.</strong></p> <p>within the OSCCAR project and made publicly available for future validations of active Human Body Models.</p> <p><strong>If you use this data, please cite the original paper.</strong></p> <p>To use the data, a generic model of the vehicle environment is also openly available: <a href="https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3">https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3 </a></p>
Gait Kinematics Data from 2 minute walk test assessment
<p>The dataset consists of gait parameters collected from a single male participant (age: 29 years, height: 1.72 m, mass: 78.3 kg) at four time points: baseline, post-immobilization (post-IM), post-resistance training (post-RT), and 14 weeks post-RT (post-14). The participant underwent a 14-day single-leg immobilization followed by an 8-week resistance training (RT) program. Gait data were recorded during the Two-Minute Walk Test (2MWT) under two conditions: comfortable (COM) and fast (MAX) walking speeds. The dataset includes kinematic data captured using ten synchronized Opal inertial sensors (APDM Inc.) placed at specific anatomical locations, sampled at 128 Hz. The recorded signals were processed using the Mobility Lab™ software.</p>
HR-GNSS data used in Neuro-Fuzzy Kinematic Finite-Fault Inversion: 2. Application to the Mw6.2, 24/August/2016, Amatrice Earthquake
<p>Here are the high-rate GNSS data we used to infer the low-frequency components of seismic source radiation within the M 6.2, 24/August/2016, Amatrice Earthquake. In particular, the traces are used to constrain frequencies between 0.03-0.06 Hz. This data has been used to evaluate the performance of the method, in a train/test split procedure, described in the manuscript. We upload data here to comply with AGU Fair data policy (https://www.agu.org/Publish-with-AGU/Publish/Author-Resources/Policies/Data-policy)</p> <p>Please find the pre-print of the manuscript from the ESSOAR (<a href="https://doi.org/10.1002/essoar.10504341.1">https://doi.org/10.1002/essoar.10504341.1</a>).</p> <p>Notice that the complete set of data are reposited on INGV FTP server: ftp://gpsfree.gm.ingv.it/amatrice2016/</p> <p>The data is originally processed by Avallone et al. (2016), and the detailed analysis procedure has been explained there. In the case where you used this data, please cite the original articles: </p> <p>Avallone, A., Latorre, D., Serpelloni, E., Cavaliere, A., Herrero, A., Cecere, G., ... & Selvaggi, G. (2016). Coseismic displacement waveforms for the 2016 August 24 Mw 6.0 Amatrice earthquake (central Italy) carried out from High-Rate GPS data. Annals of Geophysics, 59. (<a href="https://doi.org/10.4401/ag-7275">https://doi.org/10.4401/ag-7275</a>)</p> <p>Avallone, A., Selvaggi, G., D'Anastasio, E., D'Agostino, N., Pietrantonio, G., Riguzzi, F., ... & Zarrilli, L. (2010). The RING network: improvement of a GPS velocity field in the central Mediterranean. Annals of Geophysics, 53(2), 39-54. (<a href="https://doi.org/10.4401/ag-4549">https://doi.org/10.4401/ag-4549</a>)</p> <p> </p>
Data for: Iceland Kinematics from InSAR
<p>These datasets are associated with the paper "Iceland Kinematics from InSAR" submitted to <em>JGR-solid earth</em> by Cao et al., 2022. Totally 7 types of datasets (~ 40 GB) are included: 1) time-series of displacements from six tracks of Sentinel-1 InSAR with ICAMS correction during 2015 to 2021; 2) Nationwide InSAR-derived East and vertical velocity maps; 3) Nationwide InSAR-based GIA and plate-spreading models; 4) 2) InSAR LOS velocity maps that estimated using NVCE-based weighted least-squares; 5) InSAR temporal coherence maps used for evaluating quality of InSAR-derived time-series solutions (e.g., displacements and velocity); 6) InSAR incidence angles; 7) GPS-based velocity measurements (LOS, East, and Up). Spatial resolution of the InSAR results are about 100 m by 100 m.</p>
Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"
<p>Written by: Paolo Mistretta<br> Contact information: paolo.mistretta@phd.unipd.it<br> Date: 24/01/2020</p> <p><br> This document contains supplementary material for the article<br> “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition”<br> (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone)<br> presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020.</p> <p><br> Data are contained in the file: “database_ISEA2020.mat”</p>
Kinematic data and mathematical modeling of sea star locomotion
<p>It is unclear how animals with radial symmetry control locomotion without a brain. Using a combination of experiments, mathematical modeling, and robotics, we tested the extent to which this control emerges in sea stars from the local control of their hundreds of feet and their mechanical interactions with the body. We discovered that these animals (<em>Protoreaster nodosus</em>) compensate for an experimental increase in their submerged weight by recruiting more feet that synchronize in the power stroke of the locomotor cycle. Mathematical modeling replicated this response to loading in the absence of nervous communication and demonstrated how the body weight serves as a regulator of recruitment. We built a robotic sea star with an array of independently-controlled actuators that were also recruited in greater numbers under higher loads due to their collective mechanics. These findings demonstrate that an array of actuators in biological and robotic systems are capable of cooperative transport with dynamic adjustments to loading. This form of distributed control contrasts the conventional view of animal locomotion as governed by the central nervous system and offers inspiration for the design of engineered devices with arrays of actuators.</p>
Data from: Twist and chew: three dimensional tongue kinematics during chewing in macaque primates
<p>Three-dimensional (3D) tongue movements are central to performance of feeding functions by mammals and other tetrapods, but 3D tongue kinematics during feeding are poorly understood. Tongue kinematics were recorded during grape chewing by macaque primates using biplanar videoradiography. Complex shape changes in the tongue during chewing are dominated by a combination of flexion in the tongue's sagittal planes and roll about its long axis. As hypothesized for humans, in macaques during tongue retraction the middle (molar region) of the tongue rolls to the chewing (working) side simultaneous with sagittal flexion, while the tongue tip flexes to the other (balancing) side. Twisting and flexion reach their maxima early in the fast close phase of chewing cycles, positioning the food bolus between the approaching teeth prior to the power stroke. Although 3D tongue kinematics undoubtedly vary with food type, the mechanical role of this movement—placing the food bolus on the post-canine teeth for breakdown—is likely to be a powerful constraint on tongue kinematics during this phase of the chewing cycle. The muscular drivers of these movements are likely to include a combination of intrinsic and extrinsic tongue muscles.</p>
Studying kinematic linkage of finger joints: Experiment data
<p>Experimental data from <em>"<a href="https://doi.org/10.7717/peerj.14051">Studying kinematic linkage of finger joints: estimation of kinematics of distal interphalangeal joints during manipulation</a>", </em>available at PeerJ.</p> <p>Experiment data containing both hands joint angles of 9 subjects while performing 20 activities of daily living (ADLs) and free motion tasks.</p> <ul> <li>ADL_M.xlsx: Jont angles during manipulation phase of ADLs.</li> <li>ADL_R.xlsx: Jont angles during reaching and release phases of ADLs.</li> <li>FMT1-FMT2.xlsx: Joint angles during free motion tasks.</li> <li>LATERALITY.txt: Laterality of participants.</li> </ul> <p>Jont angles and sign criteria considered as in <a href="http://doi.org/10.1038/s41597-019-0175-6">Human hand kinematic data during feeding and cooking tasks</a></p> <p> </p>
Problems Using Data Gloves with Strain Gauges to Measure Distal Interphalangeal Joints' Kinematics (Experimental data)
<p>Experimental data from <em>"Problems Using Data Gloves with Strain Gauges to Measure Distal Interphalangeal Joints’ Kinematics", </em>available in Sensors.</p> <p> </p> <p><strong>"PHASE I - STATIC POSTURES, FREE MOTION AND GRASPING TASKS.xlsx" </strong> contains raw data of CyberGlove data glove of 22DoF while performing experiments detailed in Phase I.</p> <p>Jonts labelled as in <a href="https://www.nature.com/articles/s41597-019-0175-6">Human hand kinematic data during feeding and cooking tasks</a>. </p> <p>Task order detailed in "PHASE I TASK ORDER.txt".</p> <p>Subjects' hand length detailed in "PHASE I SUBJECT DATA.txt".</p> <p> </p> <p><strong>"PHASE II - SOLLERMAN HAND FUNCTION TEST.xlsx" </strong> contains joint angles recorded using CyberGlove data glove of 22DoF while performing experiments detailed in Phase II.</p> <p>Jont angles and sign criteria considered as in <a href="https://www.nature.com/articles/s41597-019-0175-6">Human hand kinematic data during feeding and cooking tasks</a>.</p> <p>Subjects' hand length and laterality detailed in "PHASE II SUBJECT DATA.txt".</p> <p> </p> <p>For further information please contact authors (rodaa@uji.es).</p>
Synchronously recorded gait kinematic data with Inertial Measurement Units and a photogrammetry system for a validation assessment
<h3>Description</h3> <p>A gait database of 32 healthy adult subjects was built , volunteers were between 20 and 63 y.o. (33.64 ± 12.44) and 71.88% were females. Every individual underwent a barefoot walking test recorded simultaneously with Inertial Measurement Units (IMUs) and the photogrammetry system Vicon. The dataset contains the kinematic gait information of the hip, knee, and ankle joints in the three planes of motion: sagittal, frontal, and transversal. </p> <p>The signals recorded by the IMUs are referred to as I(t) and were captured with a sampling frequency of 50 Hz, and those recorded by the photogrammetry system are called V(t) and were captured with a sampling frequency of 100 Hz. To perform a comparative study of both systems, the V(t) signals must be resampled to 50 Hz. Then, the delay between the two signals must be corrected to align them. Finally, gait cycles can be extracted for each pair of trials following the data information provided, obtaining a pair of waveforms for each gait cycle [I(t), V(t)]. A total of 268 synchronous gait cycles [I(t), V(t)] can be recovered and analyzed in the three planes of motion per limb.</p> <h3>Data information</h3> <ul> <li><em>raw_data</em>: folder containing the 32 subjects raw kinematic signals recorded with IMUs (sampling frequency 50 Hz) and photogrammetry system (sampling frequency 100 Hz) synchronously.<br> <ul> <li>For IMUs records: <ul> <li>Z: sagittal plane.</li> <li>X: frontal plane.</li> <li>Y: transversal plane.</li> </ul> </li> <li>For photogrammetry system records: <ul> <li>X: sagittal plane.</li> <li>Y: frontal plane.</li> <li>Z: transversal plane.</li> </ul> </li> </ul> </li> </ul> <ul> <li><em>captures_information.xlsx</em>: table containing the delay correction and the samples corresponding to the events of the gait cycles. The delay correction is the number of samples for which each photogrammetry signal V(t), after being resampled to 50 Hz, must be moved to be completely aligned with its synchronous IMUs signal couple I(t). <ul> <li>If the delay is positive (+) the V(t) signal must be delayed by adding zeros at the beginning.</li> <li>If the delay is negative (-) the V(t) signal must be moved forward by removing zeros at the beginning.</li> </ul> </li> </ul>
Analysis code and data for the morphometrics and kinematics of tube feet
<p>Hydrostatic skeletons, such as an elephant trunk or a squid tentacle, permit the transmission of mechanical work through a soft body. Despite the ubiquity of these structures among animals, we generally do not understand how differences in their morphology affect their mechanical properties. Therefore, the present study used mathematical modeling, morphometrics, and kinematics to understand the transmission of force and displacement in the tube feet of the juvenile six-rayed star <em>Leptasterias</em> <em>sp.</em> An inverse-dynamic analysis revealed that the forces generated by the feet during crawling primarily serve to overcome the submerged weight of the body. This load was disproportionately generated by the feet at more proximal positions along each ray, which were used more frequently for crawling. Due to a combination of mechanical advantage and muscle mass, these proximal feet exhibited a greater capacity for force generation than the distal feet. However, the higher displacement advantage of the more elongated distal feet offer a superior ability to extend the feet into the environment. Therefore, the morphology of tube feet demonstrates a gradient in gearing along each ray that matches their role in behavior.</p>
Evaluating a Kinematic Data Glove with Pressure Sensors to Automatically Differentiate Free Motion from Product Manipulation (Experimental Data)
<p>Experimental data from <em>"Evaluating a kinematic data glove with pressure sensors to automatically differentiate free motion from product manipulation", </em>available at Applied Sciences.</p> <p>"DATA.zip" contains raw data collected using VMG30 and CyberGlove data gloves, in txt format. </p> <p>For further information please see the details in the manuscript or contact the corresponding author Alba Roda-Sales (rodaa@uji.es).</p>
Coseismic Kinematics of the 2023 Kahramanmaras, Turkey Earthquake Sequence from InSAR and Optical Data
<p>We upload the supporting information and four high-resolution images Figures 1-4 in the manuscript accepted by the GRL journal (2023.07.31).</p>
Kinematic Evolution of the Tangra Yumco Rift, South-Central Tibet: Supplementary Data Tables
<h4>We investigate rifting during continental collision in southern Tibet by testing kinematic models for two classes of rifts: Tibetan rifts are defined as >150 km in length and crosscut the Lhasa Terrane, and Gangdese rifts are <150 km long and isolated within the high topography of the Gangdese Range. Discerning rift kinematics is a crucial step towards understanding rift behavior and evolution that has been historically limited. We evaluate spatiotemporal trends in fault displacement and extension onset in the Tangra Yumco (TYC) rift and several nearby Gangdese rifts, and examine how contraction and rift exhumation relate to evolution of the Gangdese drainage divide. Igneous U-Pb and zircon (U-Th)/He (ZHe) results indicate rift footwall crystallization between ~59-49 Ma and cooling between ~60-4 Ma, respectively, with ZHe ages correlating with sample latitude. Samples from Gangdese latitudes (~29.4-29.8°N) yield predominantly Oligocene-early Miocene ages, whereas samples north of ~29.8°N yield both late Miocene-Pliocene ages and Paleocene-Eocene ages. Thermal history models indicate two-stage cooling, with initially slow cooling followed by accelerated cooling during late Miocene-Pliocene time. From spatial distributions of ZHe ages we interpret: (1) ~28-16 Ma ages from Gangdese latitudes reflect exhumation along contractional structures, (2) ~8-4 Ma ages reflect rift-related exhumation, and (3) ~60-48 Ma ages indicate these samples experienced lesser rift exhumation. Our data are consistent with a segment linkage evolution model for the TYC rift, withinteractions between rifts and contractional structures likely influencing the evolution of topography and location of the Gangdese drainage divide since Miocene time.</h4>
Analysis code and data for the morphometrics and kinematics of tube feet
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Data from: Twist and chew: three dimensional tongue kinematics during chewing in macaque primates
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Kinematic data and mathematical modeling of sea star locomotion
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