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opencc-by-4.0Jan 2020View details →

Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"

Written by: Paolo Mistretta Contact information: paolo.mistretta@phd.unipd.it Date: 24/01/2020 This document contains supplementary material for the article “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition” (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone) presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020. Data are contained in the file: “database_ISEA2020.mat”

opencc-by-4.0May 2020View details →

Multi-scale sea ice kinematics modeling dataset with a tripolar grid hierarchy (TS grids) in CESM

1. We design a new tripolar grid generation method for global ocean-sea ice modeling. The generated grid is orthogonal and compatible with ocean and sea ice models. A hierarchy of ocean-sea ice model grid is constructed and incorporated in CESM by using the grid generation method. The generated tripolar grids are regarded as TS045, TS015 and TS005, with the nominal resolution of 0.45o (800x560), 0.15o (2400x1680) and 0.05o (7200x5040), respectively. The resolution range for the grid hierarchy covers climate modeling to sub-mesoscale capable for ocean modeling. 2. Atmosphere forced simulations based on CESM D-type experiments are carried out for TS grid. Both TS045 and TS015 experiments start with no sea ice, integrate for 42 years. TS005 starts from the equilibrium state of TS015 result (36 year), runs for another 7 years. 3. TS0*.grid files include necessary grid information, such as grid latitude, longitude, etc. TS0*.kmt files denote the deepest level at each grid location. Bilinear interpolators from TS grid to atmosphere T62 grid are provided with map*.nc files. The model outputs at year 42 are provided with T*.nc files. The last two kinds of netcdf files are compressed with Linux command "gzip".

opencc-by-4.0Oct 2020View details →

Basic Considerations for the Observability of Kinematically Offset Binary AGN

KDE resampled massive black-hole (MBH) binary population used for the analysis in Kelley 2020, Considerations for the Observability of Kinematically Offset Binary AGN (2005.10255). We start with a population of MBH binaries derived from the Illustris cosmological simulations, and evolved using semi-analytic post-processing models.  These methods are described in Kelley et al. 2016 (1606.01900) and Kelley et al. 2017a,b (1702.02180 and 1711.00075).  We then use the kernel density estimation (KDE) technique to resample this population from the Illustris volume, into a sample of binaries from a full, simulated light-cone of observations.  The binaries are resampled in bins of observed orbital period.  The number of resampled binaries in a given period bin are restricted to 500,000.  When this occurs, the 'weight' of those binaries is assigned a value greater than one, while the 'weight' of binaries in unrestricted period bins is set to unity.  For example, if the 'true' number of binaries in a bin should have been 1e6, the number of samples would be restricted to 5e5, and the weight of each of those binaries would be set to 2.0.  If the number of binaries in a bin were instead 2e5, which is less than the limit, then each of those binaries is given a weight of 1.0.  Only a single realization of this sample is included, but additional realizations can be produced if needed and on request. The data are presented in hdf5 format and contain the following arrays: <pre><code># Meta-Data `periods`: shape: (201,) units: seconds descr: the edges of the bins in observed orbital period from which the population was sampled. `counts`: shape: (200,) units: None descr: The number of binaries produced in each period bin. This number for each bin is capped at 500,000. The sum of these values equals the total number of samples (70622013). # Data In this particular realization of data, the number of sampled binaries is `S = 70622013`. `mtot`: shape: (S,) units: grams descr: the total mass of each binary (`M = M1 + M2`). `mrat`: shape: (S,) units: None descr: The mass ratio of each binary, `q = M2/M1 <= 1.0`, where M2 is the mass of the secondary (lower mass) component. `pobs`: shape: (S,) units: seconds descr: The orbital period of each binary in the observer's rest frame (at redshift, `z = 0`). `redz`: shape: (S,) units: None descr: The redshift of each binary. `weights`: shape: (S,) units: None descr: The number of 'true' binaries (i.e. the number that are expected to occur in this realization of an observed universe) represented by this individual 'sample', and having similar binary parameters.</code></pre>

opencc-by-4.0Nov 2020View details →

Age-related humerothoracic, scapulothoracic, and glenohumeral kinematics during elevation and rotation motions

Age affects gross shoulder range of motion (ROM), but biomechanical changes over a lifetime are typically only characterized for the humerothoracic joint. Suitable age-related baselines for the scapulothoracic and glenohumeral contributions to humerothoracic motion are needed to advance understanding of shoulder injuries and pathology. Notably, biomechanical comparisons between younger or older populations may obscure detected differences in underlying shoulder motion. Kinematics derived from healthy subjects aged <35 years and >45 years using biplane fluoroscopy and skin-marker motion analysis quantified humerothoracic, scapulothoracic, and glenohumeral motion during 3 static poses (resting neutral, internal rotation to L4-L5, and internal rotation to maximum reach) and 2 dynamic activities (scapular plane abduction and external rotation in adduction). These data are available for download to aid researchers and clinicians in characterizing non-pathologic shoulder motion during common clinical ROM activities.

opencc-by-4.0Aug 2020View details →

GaiaDR2 RR Lyrae dataset from "Chemo-kinematics of the GaiaRR Lyrae: the halo and the disc" (Iorio&Belokurov,2020)

**V2 - UPDATED VERSION** The V1 table missed the ra and random_index columns. These have been added to the new version, no other modifications have been made on the file ***************************** The catalogue contains all the stars classified as RR Lyrae in GaiaDR2. In addition to the columns from the Gaia tables, there are additional columns related to the Iorio&Belokurov20 paper. The zip file contains: - Fits file with the dataset - Ascii file containing the description of the columns in the fits file  If you have any question, write to me at giuliano.iorio.astro@gmail.con

opencc-zeroFeb 2020View details →

Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system

In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays.

opencc-by-4.0Nov 2024View details →

ExoMove - Kinematics of Daily Activities with Lower-Limb Exoskeletons

This dataset reports the lower-limb kinematics of healthy individuals during various daily activities (sitting, walking, stair ascending and descending, and transitions between them) while using two distinct lower-limb exoskeletons, eWalk and Autonomyo. The dataset captures the biomechanical differences between the exoskeletons, offering a rich resource for advancing exoskeleton design and control for assistive and rehabilitative applications.

opencc-by-4.0May 2024View details →

RPC-Net Dataset. Simultaneous HD-sEMG Recordings on the Forearm and angles of a 29-DOF Hand Kinematic Model

The dataset in this repository comprises data acquired during the doctoral research project of Giovanni Rolandino at the Nuffield Department of Surgical Sciences, University of Oxford. Five sub-datasets make up the repository: DS1: Simultaneous acquisition of high-density surface electromyography (HD-sEMG) signals from the forearm and hand position kinematics. Data were recorded from 12 healthy subjects while they cycled through 16 hand poses. HD-sEMG was acquired with traditional gel electrode arrays. DS2: A similar protocol to DS1 was followed, but the HD-sEMG was acquired using a novel dry-electrode array. This dataset includes 16 subjects. Whereas DS1 included data from a single session for each subject, DS2 includes two sessions, acquired hours to days apart; these sessions are identified as s1 and s2. DS3: This subset consists of two parts. DS3.a repeats the protocol used in DS2 with 4 subjects, introducing repositioning between trials. DS3.b includes the results of the real-time assessment of RPC-Net, a shallow neural network trained with data from DS3.a to estimate hand position from HD-sEMG activity. DS3.b contains the real-time output recorded during prompt-matching tasks and the corresponding targets. DS4: This subset includes data related to the assessment of RFC-Net, a shallow neural network designed to estimate hand position from neck muscle activation. Experiments were performed on 8 healthy participants and 8 participants with tetraplegia. DS4.a includes the data used for training the network, while DS4.b includes data from the testing phase of the algorithm. DS4.b.s1 includes results from a cursor control task, and DS4.b.s2 includes results from a virtual hand control task. AD1: Additional data related to the electrical validation of the dry-electrode array. Code for processing the data in this repository is available on Dropbox: https://www.dropbox.com/scl/fo/nkvbse7evo0k8ou1utn7i/AMDh_MOZQJ6gCwDXGPadmZ0?rlkey=ynoix3anpc81v24hogn3fymb4&st=xrqx07q2&dl=0 For additional information, readers are referred to the original papers detailing acquisition protocols and processing procedures: 1) G. Rolandino, M. Gagliardi, T. Martins, G. L. Cerone, B. Andrews, J. J. FitzGerald. Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation. IEEE Transactions on Biomedical Engineering, 71(5):1617-1627, May 2024. doi:10.1109/TBME.2023.3346192. 2) G. Rolandino, C. Zangrandi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32:4004-4013, 2024. doi:10.1109/TNSRE.2024.3490796. 3) G. Rolandino, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Performance of a ML-Based 3-DoF Kinematic Model in Estimating Hand Position from High-Density EMG. Presented at IFESS Conference, Bath, UK, September 2024. 4) G. Rolandino, L. Lion, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Artificial Neural Networks for HD-sEMG-Based Hand Position Estimation: Addressing Inter- and Intra-Subject Variability. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025. 5) G. Rolandino, G. Parisi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Real-Time Hand Kinematic Estimation with HD-sEMG and Artificial Neural Networks: Feasibility and Effects of Multi-Subject Training and Visual Feedback. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025. 6) G. Rolandino, V. Taboni Lisboa, T. Vieira, A. Cliquet Jr., B. Andrews, J. J. FitzGerald. HD-sEMG-Based Control Using Neck Muscles and Shallow Neural Networks: Assessing Performance in Rehabilitation-Oriented Tasks. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025. This dataset benefited from the support of all listed authors and arose from collaborations between the Oxford Neural Interfacing Group; LISiN (Politecnico di Torino, Turin, Italy); the Department of Orthopedics, Rheumatology and Traumatology (University of Campinas, SP, Brazil); and the Oxford Robotics Institute (University of Oxford, Oxford, UK). Part of this work was funded by the John Fell Oxford University Press Research Fund. The corresponding author is available for questions or clarification at g.rolandino@protonmail.com.

opencc-zeroNov 2023View details →

Kinematic data and mathematical modeling of sea star locomotion

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 (Protoreaster nodosus) 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.

FIGURE 2 in Achieving kinematic identity across shape diversity in musculoskeletal modeling

FIGURE 2. The top images show an overlay of the reduced-asymmetry australopithecine pelvis (beige) with the ADL australopithecine pelvis (dark green). The bottom color-coded distance map pelvis show the distance between the reduced-asymmetry australopithecine pelvis with the ADL australopithecine pelvis.

FIGURE 4 in Achieving kinematic identity across shape diversity in musculoskeletal modeling

FIGURE 4. AnyBody australopithecine musculoskeletal model without (left) and with (right) muscle model visualization.

FIGURE 1 in Achieving kinematic identity across shape diversity in musculoskeletal modeling

FIGURE 1. The flowchart shows the major steps required to build the ADL australopithecine model. In the blue boxes, the ADL human model is driven with the Schreiber and Moissenet (2019) human locomotion data. From these ADL human simulations, the dimension of the pelvis and femur can be extracted as well as model motion profiles used at later stages of the process (Figure 5). The gray boxes show the major steps in transforming (TPS-based morphing) the ADL human pelvis to match the australopithecine morphology (A.L. 288-1 reduced-asymmetry pelvis; Australopithecus afarensis), thus creating the ADL australopithecine pelvis. The green boxes show the steps necessary to create the ADL australopithecine (hybrid) femur from the ADL human femur.

FIGURE 5 in Achieving kinematic identity across shape diversity in musculoskeletal modeling

FIGURE 5. This flowchart shows the major steps required to generate the C3D motion file to drive the walking simulations with an australopithecine hip. Blue, light blue, and blue/grey and blue/green dashed boxes are the same boxes from Figure 1. The original ADL human model (blue box) is morphed based on the australopithecine pelvis (blue/grey dashed box) and femur (blue/green dashed box) to create the ADL australopithecine model (orange box). The results from the human walking simulation (blue box) are combined with the L5-sacral offset translation (light blue box) to generate new "experimental marker data" that are combined with the original ground reaction force data from Schreiber and Moissenet (2019) (purple box). The ADL australopithecine model and new motion data are then used to drive the simulations of walking with an australopithecine hip.

FIGURE 6 in Achieving kinematic identity across shape diversity in musculoskeletal modeling

FIGURE 6. Motion of the pelvis and lower limb joints for one individual walking simulation with both human (red lines) and australopithecine (black circles) shaped hips. A. Pelvic rotation (transverse plane), tilt (sagittal plane) and drop (coronal plane). B. Hip flexion-extension, abduction-adduction, and internal-external rotation. C. Knee flexion-extension, ankle dorsi-plantar flexion, subtalar eversion-inversion.

opencc-zeroNov 2021View details →

Convergence of undulatory swimming kinematics across a diversity of fishes

Fishes exhibit an astounding diversity of locomotor behaviors, from classic swimming with their body and fins to jumping, flying, walking, and burrowing. Fishes that use their body and caudal fin (BCF) during undulatory swimming have been traditionally divided into modes based on the length of the propulsive body wave and the ratio of head:tail oscillation amplitude: anguilliform, sub-carangiform, carangiform and thunniform. This classification was first proposed based on key morphological traits, such as body stiffness and elongation, to group fishes based on their expected swimming mechanics. Here, we present a comparative study of 44 diverse species quantifying kinematics and morphology of BCF-swimming fishes. Our results reveal that most species we studied share similar oscillation amplitude during steady locomotion that can be modeled using a second-degree order polynomial. The length of the propulsive body wave was shorter for species classified as anguilliform and longer for those classified as thunniform, although substantial variability existed both within and among species. Moreover, there was no decrease in head:tail amplitude from anguilliform to thunniform mode of locomotion as we expected from the traditional classification. While the expected swimming modes correlated with morphological traits, they did not accurately represent the kinematics of BCF locomotion. These results indicate that even fish species differing as substantially in morphology as tuna and eel exhibit statistically similar two-dimensional midline kinematics and point toward unifying locomotor hydrodynamic mechanisms that can serve as the basis for understanding aquatic locomotion and controlling biomimetic aquatic robots.

openother-openDec 2021View details →

Erratum: Constraints on dark matter-nucleon effective couplings in the presence of kinematically distinct halo substructures using the DEAP-3600 detector [Phys. Rev. D 102, 082001 (2020)]

Corrections to the results from O3 operator in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.102.082001">Phys. Rev. D 102, 082001</a>. "Constraints on dark matter-nucleon effective couplings in the presence of kinematically distinct halo substructures using the DEAP-3600 detector".