Kinematic Information Research Articles

Three Virtual Reality Environments for the Assessment of Executive Functioning Using Performance Scores and Kinematics: An Embodied and Ecological Approach to Cognition.

Virtual reality (VR) is an attractive technology for cognitive assessment, as it provides a more embodied experience compared with typical test situations, such as those using paper and pencil. In addition, VR can immerse individuals in complex situations similar to real-life ones, thereby improving the ecological validity (i.e., face validity) of the assessment. VR also offers improved scoring of tests as it facilitates the tracking of kinematic information and the temporal tracking of activities. This study assesses the correlation between scores on executive function assessments using standard neuropsychological tasks in paper-and-pencil format, on a tablet, and in three immersive VR environments, each designed to involve specific aspects of executive function. This study also aims to assess the correlation between these performance scores and a set of kinematic measures (speed, duration, and distance traveled by the hand) collected in VR. The outcomes, including performance scores and kinematic measures, correlate both with traditional assessment methods (such as paper and pencil, and computerized 2D tests) and with each other, suggesting their potential usefulness in clinical and research contexts. The discussion focuses on the advantages of embodied, situated, and spatialized tests for cognitive assessment and the benefits of kinematic tracking in VR tests for the quality of this assessment.

The APO-K2 Catalog. I. ∼7500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology

We present a catalog of fundamental stellar properties for ∼7500 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment, part of the Sloan Digital Sky Survey IV, and asteroseismology from K2. The resulting APO-K2 catalog provides spectroscopically derived temperatures and metallicities, asteroseismic global parameters, evolutionary states, and asteroseismically derived masses and radii. Additionally, we include kinematic information from Gaia. We investigate the multidimensional space of abundance, stellar mass, and velocity with an eye toward applications in Galactic archaeology. The APO-K2 sample has a large population of low-metallicity stars (∼288 with [M/H] ≤ −1), and their asteroseismic masses are larger than astrophysical estimates. We argue that this may reflect offsets in the adopted fundamental temperature scale for metal-poor stars rather than metallicity-dependent issues with interpreting asteroseismic data. We characterize the kinematic properties of the population as a function of α enhancement and position in the disk and identify those stars in the sample that are candidate components of the Gaia-Enceladus merger. Importantly, we characterize the selection function for the APO-K2 sample as a function of metallicity, radius, mass, νmax , color, and magnitude referencing Galactic simulations and target selection criteria to enable robust statistical inferences with the catalog.

Neural ordinary differential equation for irregular human motion prediction

Human motion prediction often assumes that the input sequence is of fixed frame rates. However, in real-world applications, the motion capture system may work unstably sometimes and miss some frames, which leads to inferior performance. To solve this problem, this paper leverages neural Ordinary Differential Equations and proposes a human Motion Prediction method named MP-ODE to handle irregular-time human pose series. First, a Difference Operator and a Positional Encoding are proposed to explicitly provide the kinematic and time information for the model. Second, we construct the encoder–decoder model with ODE-GRU unit, which enables us to learn continuous-time dynamics of human motion. Third, a Quaternion Loss transforms exponential maps to quaternion to train MP-ODE. The Quaternion Loss can avoid the discontinuities and singularities of exponential maps, boosting the convergence of the model. Comprehensive experiments on Human3.6 m and CMU-Mocap datasets demonstrate that the proposed MP-ODE achieves promising performance in both normal and irregular-time conditions.

PROCEEDINGS IN UAS-ASSISTED BRIDGE INSPECTIONS: RTK-BASED PHOTOGRAMMETRIC RECONSTRUCTION AND SPATIAL FILTERING

Abstract. Traditional bridge inspections present considerable challenges in terms of efficiency and accuracy. However, recent advancements in Unmanned Aerial Systems (UASs) and deep learning for object detection have opened up new avenues for automatic bridge damage detection. We present a comprehensive framework leveraging these technologies for automated damage detection in UAS imagery, followed by accurate mapping of the damage predictions on photogrammetric models. In this work, we propose a photogrammetric procedure to retrieve geolocated bridge models solely based on Real-Time Kinematics (RTK) information. Within the damage detection step, we conduct extensive testing and optimization of model hyperparameters using YOLOv8 and Slicing Aided Hyper Interference (SAHI). Next, we map the predictions onto the 3D model using ray casting, allowing to group and filter the predictions by their area and position. Finally, a Graphical User Interface (GUI) allows bridge inspectors to identify false positive predictions, generate new training data, and directly measure damage dimensions in the images. Validation on a concrete box girder bridge resulted in a photogrammetric model with a mean error of 1.3 cm, negating the need for ground control points. Our model training process revealed substantial performance variations between training and test datasets, underscoring the importance of evaluating optimal hyperparameters on UAS inspection images rather than relying on the validation metrics. Lastly, we successfully map the detected damage and create new training data from the UAS inspection images. This framework significantly enhances bridge inspection accuracy and efficiency, providing a strong foundation for future developments in automated bridge inspections.

Influence of conflicting prior information on action anticipation in soccer players: an ERP study.

Prior probability information and visual kinematic information are essential for action anticipation in athletes. The aims of this study were to examine how conflicting prior information influences anticipatory judgment in athletes vs. non-athletes and to explore the underlying cognitive mechanisms. The aim of Experiment 1 was to determine the moment when prior information influenced action anticipation in athletes vs. non-athletes. To that end, 17 semi-elite soccer goalkeepers and 18 non-athletes received prior information about the probability of the direction that a player on a video would kick a ball into the goal. Participants then anticipated the trajectory of the ball when the action of the player's kick on the video was truncated at the moment the foot contacted the ball (time T) or one frame (T-1; 50 ms) or two frames (T-2; 100 ms) before the foot-ball contact. The aim of Experiment 2 was to elucidate the adaptive cognitive-motor behavior exhibited by highly trained soccer players at the moment when their anticipatory performance was most influenced by prior information. Experiment 2 included 27 different semi-elite soccer players with many years of experience as a goalkeeper and 27 different non-athletes. Participants anticipated the direction of the kick when the kinematic action of the kicker at the moment the anticipatory performance of the participants was most influenced by prior information (as determined in Experiment 1) was congruent, incongruent, or neutral. Action anticipation accuracy and response time were evaluated for both experiments, whereas event-related potential components N1, N2, and P3 were assessed only in Experiment 2. The results of Experiment 1 showed that anticipatory accuracy was significantly higher among athletes than non-athletes and that anticipatory accuracy with directional information given was significantly higher than that when no prior information was given or when prior information without directional information was given (p < 0.001) for both T-1 (p's ≤ 0.034) and T-2 (p's < 0.001) occlusion points. In Experiment 2 using those two video occlusion times, the amplitude of the N1 component, which reflects selective attention to stimulus properties, was significantly higher in athletes than in non-athletes (p < 0.001). The amplitude of the N2 component, which has been associated with conflict monitoring, for the incongruent condition was significantly higher than that for both neutral (p < 0.001) and congruent (p < 0.001) conditions in athletes. Non-athletes exhibited no significant N2 amplitude differences for any prior information condition. Integrating prior information enhanced action anticipation in semi-elite soccer players, particularly 50 and 100 ms before the foot-ball contact. Semi-elite soccer players prioritized early selective attention and conflict monitoring of kinematic information, facilitating action anticipation using the prior information.

Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression

The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine learning methods have been popularly used to predict joint torque with surface electromyography (sEMG) signals and kinematic information as inputs. This study aims to predict lower limb joint torque in the sagittal plane during walking, using a long short-term memory (LSTM) model and Gaussian process regression (GPR) model, respectively, with seven characteristics extracted from the sEMG signals of five muscles and three joint angles as inputs. The majority of the normalized root mean squared error (NRMSE) values in both models are below 15%, most Pearson correlation coefficient (R) values exceed 0.85, and most decisive factor (R2) values surpass 0.75. These results indicate that the joint prediction of torque is feasible using machine learning methods with sEMG signals and joint angles as inputs.

Kinematic Primitives in Action Similarity Judgments: A Human-Centered Computational Model

This paper investigates the role that kinematic features play in human action similarity judgments. The results of three experiments with human participants are compared with the computational model that solves the same task. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The comparative experimental results show that both model and human participants can reliably identify whether two actions are the same or not. Specifically, most of the given actions could be similarity judged based on very limited information from a single feature domain (velocity or spatial). Both velocity and spatial features were however necessary to reach a level of human performance on evaluated actions. The experimental results also show that human performance on an action identification task indicated that they clearly relied on kinematic information rather than on action semantics. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features, which can provide an essential basis for classifying human actions.

Chemo-dynamical tagging in the outskirts: The origins of stellar substructures in the Magellanic Clouds

We present the first detailed chemical analysis from APOGEE-2S observations of stars in six regions of recently discovered substructures in the outskirts of the Magellanic Clouds extending to 20° from the Large Magellanic Cloud (LMC) center. We also present, for the first time, the metallicity and α-abundance radial gradients of the LMC and the Small Magellanic Cloud (SMC) out to 11° and 6°, respectively. Our chemical tagging includes 13 species including light, α-, and Fe-peak elements. We find that the abundances of all of these chemical elements in stars populating two regions in the northern periphery, along the northern “stream-like” feature, show good agreement with the chemical patterns of the LMC, and thus likely have an LMC origin. For substructures located in the southern periphery of the LMC we find more complex chemical and kinematical signatures, indicative of a mix of LMC-like and SMC-like populations. The southern region closest to the LMC shows better agreement with the LMC, whereas that closest to the SMC shows a much better agreement with the SMC chemical pattern. When combining this information with 3D kinematical information for these stars, we conclude that the southern region closest to the LMC likely has an LMC origin, whereas that closest to the SMC has an SMC origin and the other two southern regions have a mix of LMC and SMC origins. Our results add to the evidence that the southern substructures of the LMC periphery are the product of close interactions between the LMC and SMC, and thus likely hold important clues that can constrain models of their detailed dynamical histories.

Early dextral shear motion during the breakup of East Gondwana documented in clastic dykes in the Tethys Himalaya

Much of the geological record of the northern Indian passive continental margin has been lost due to erosion or obscured due to tectonic burial associated with the India-Eurasia collision. Consequently, tectonic models of India's breakup from East Gondwana incorporate limited data from the northern margin of Greater India. We present evidence of the Late Jurassic clastic dykes crosscutting contemporaneous sedimentary sequences in the Tethys Himalaya. We propose that the original orientation of the clastic dykes provides kinematic information about Greater India's breakup from East Gondwana. The clastic dykes are meters to hundreds of meters long and centimeters to meters wide. They taper downwards and crosscut country rocks at high angles. The clastic dykes and their host strata are quartzose sandstones and share similar textures and detrital zircon age populations (dominantly 600–500 Ma and 1000–800 Ma zircon grains). These similarities indicate the clastic dykes and their host rocks share a common source as well as comparable transport and deposition dynamics. Notably, the clastic dykes lack distinct signs of ptygmatic folds, shale/mudrock clasts, surface orientated structures, or bifurcations on dyke margins. Decompaction and structural restoration of the sequences show that the sandstone dykes/fractures and en échelon dyke arrays can be classified into three distinct orientation groups NW (349° − 295°), SW (∼210°) and NE (∼40°). There is no apparent difference in the age or provenance of the clastic material within the different orientation groups. We propose that the clastic dykes signify passively infilled tensile fractures formed within a NW-SE dextral strike-slip megashear system during the Late Jurassic. This system likely developed in a transform zone within Greater India prior to the separation of the Indian Plate from East Gondwana.

Hierarchical Bayesian Inference of Globular Cluster Properties

Abstract We present a hierarchical Bayesian inference approach to estimating the structural properties and the phase space center of a globular cluster (GC) given the spatial and kinematic information of its stars based on lowered isothermal cluster models. As a first step towards more realistic modelling of GCs, we built a differentiable, accurate emulator of the lowered isothermal distribution function using interpolation. The reliable gradient information provided by the emulator allows the use of Hamiltonian Monte Carlo methods to sample large Bayesian models with hundreds of parameters, thereby enabling inference on hierarchical models. We explore the use of hierarchical Bayesian modelling to address several issues encountered in observations of GC including an unknown GC center, incomplete data, and measurement errors. Our approach not only avoids the common technique of radial binning but also incorporates the aforementioned uncertainties in a robust and statistically consistent way. Through demonstrating the reliability of our hierarchical Bayesian model on simulations, our work lays out the foundation for more realistic and complex modelling of real GC data.

Cubic time-spline fitting and interpolation for five-axis CNC machining

Abstract In CNC machining, G01 codes are widely used to represent the tool path. Directly interpolating these G01 codes is time-consuming and may cause discontinuities. In this paper, we propose a time-spline curve fitting method that combines tool path fitting and feedrate scheduling into a single step for five-axis CNC machining. The input for this method consists of a three-dimensional linear path of the tool tip in the workpiece coordinate system and two-dimensional tool orientations in the machine coordinate system (MCS). The output is a fitted tool path in the MCS represented by a five-dimensional smooth time-parametric B-spline curve, simply referred to as the time-spline curve. The time-spline curve provides not only position information but also kinematic information, including velocity, acceleration, and jerk for each axis, directly derived from the first, second, and third derivatives of the curve. To meet fitting error constraints and axial kinematic constraints, our objective is to find the time-spline curve that is time-optimal. We formulate the optimization problem as a nonlinear optimization model and design a recursive algorithm to solve it. The resulting time-spline curve demonstrates high accuracy and fully utilizes the machine’s kinematic capabilities. Along the tool path defined by the time-spline curve, exact interpolation points can be straightforwardly obtained according to the interpolation period. Simulations and experimental results indicate that the proposed method yields a time-optimal time-spline curve with the desired precision and kinematic constraints.

Exploring visual representations of computer mouse movements for bot detection using deep learning approaches

Nowadays, bots can be seen everywhere on the Internet and are responsible for a large percentage of website traffic. The problem of bot detection has increasingly gained attention since more and more bots have been abused from click fraud in online advertisements to launching credential stuffing attacks for harvesting user accounts at a large scale. In this work, we present an end-to-end deep framework for bot detection based on computer mouse movements. Specifically, we propose a novel visual representation scheme that can simultaneously encode spatial and kinematic information in mouse movements into an image which can then be used as the input to Convolutional Neural Networks (CNN). Various strategies to encode kinematic features into images are investigated to obtain a better scheme of visual representation. Experimental results show that the proposed representation scheme in combination with CNN outperforms several baseline models with a TPR of 99.34% in detecting known bots and can be generalized to unknown bots with a highest accuracy of 99.20%. We also demonstrate that the proposed approach can reach acceptable performance levels even for models trained with a small number of training samples. This makes the deployment of our approach easier in real-world scenarios.

Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematic analyses

Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, H0. Nevertheless, inference of such parameters from images of lensing systems is not trivial as parameter degeneracies can limit the precision in the measured lens mass and cosmological results. External information on the mass of the lens, in the form of kinematic measurements, is needed to ensure a precise and unbiased inference. Traditionally, such kinematic information has been included in the inference after the image modeling, using spherical Jeans approximations to match the measured velocity dispersion integrated within an aperture. However, as spatially resolved kinematic measurements become available via IFU data, more sophisticated dynamical modeling is necessary. Such kinematic modeling is expensive, and constitutes a computational bottleneck that we aim to overcome with our Stellar Kinematics Neural Network (SKiNN). SKiNN emulates axisymmetric modeling using a neural network, quickly synthesizing from a given mass model a kinematic map that can be compared to the observations to evaluate a likelihood. With a joint lensing plus kinematic framework, this likelihood constrains the mass model at the same time as the imaging data. We show that SKiNN’s emulation of a kinematic map is accurate to a considerably better precision than can be measured (better than 1% in almost all cases). Using SKiNN speeds up the likelihood evaluation by a factor of ~200. This speedup makes dynamical modeling economical, and enables lens modelers to make effective use of modern data quality in the JWST era.

Neural control of lexical tone production in human laryngeal motor cortex

In tonal languages, which are spoken by nearly one-third of the world’s population, speakers precisely control the tension of vocal folds in the larynx to modulate pitch in order to distinguish words with completely different meanings. The specific pitch trajectories for a given tonal language are called lexical tones. Here, we used high-density direct cortical recordings to determine the neural basis of lexical tone production in native Mandarin-speaking participants. We found that instead of a tone category-selective coding, local populations in the bilateral laryngeal motor cortex (LMC) encode articulatory kinematic information to generate the pitch dynamics of lexical tones. Using a computational model of tone production, we discovered two distinct patterns of population activity in LMC commanding pitch rising and lowering. Finally, we showed that direct electrocortical stimulation of different local populations in LMC evoked pitch rising and lowering during tone production, respectively. Together, these results reveal the neural basis of vocal pitch control of lexical tones in tonal languages.

Multi-modal synergistic quantitative analysis and rehabilitation assessment of lower limbs for exoskeleton

In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.

Real-Time Reconstruction of Fluid Flow under Unknown Disturbance

We present a framework that captures sparse Lagrangian flow information from a volume of real liquid and reconstructs its detailed kinematic information in real time. Our framework can perform flow reconstruction even when the liquid is disturbed by an object of unknown movement and shape. Through a large dataset of liquid moving under external disturbance, an agent is trained using reinforcement learning to reproduce the target flow kinematics with only the captured sparse information as inputs while remaining oblivious to the movement and the shape of the disturbance sources. To ensure that the underlying simulation model faithfully obeys physical reality, we also optimize the viscosity parameters in Smoothed Particle Hydrodynamics (SPH) using classical fluid dynamics knowledge and gradient-based optimization. By quantitatively comparing the reconstruction results against real-world and simulated ground truth, we verified that our reconstruction method is resilient to different agitation patterns.

Reinforcement learning-based active flow control of oscillating cylinder for drag reduction

This study explores the use of a reinforcement learning (RL)-based active flow control strategy to reduce the drag of a transversely oscillating cylinder confined between two walls. We incorporate wake kinematic information of the oscillating cylinder from direct numerical solution into the agent to actively adjust the oscillating amplitude. Our findings reveal two strategies that significantly improve drag reduction. First, the oscillating frequency should be chosen within the lock-in region. Second, placing probes in the active region of turbulent kinetic energy enables the full utilization of physical information for achieving more efficient and stable control. The results show that an effective control strategy can reduce the drag by 8.4% compared to the case of stationary cylinder at Re = 200. We identify three characteristics of the controlled flow that contribute to drag reduction: an elongated recirculation zone, an increased energy transport coefficient, and asymmetric cylinder oscillation. We observed that this well-behaved controlled flow can be obtained through real-time RL-based control, as it consistently attenuates the asymmetric dynamic mode decomposition modes of the flow. Furthermore, we find that the asymmetry cylinder oscillation synchronizes with the vortex shedding, indicating that the RL-based control can expand the lock-in region.

“What we talk about is creating a probability”: Exploring the interaction between the anticipation and decision-making processes of professional bowlers and batters in Twenty20 cricket

Expert performers in time constrained sports use a range of information sources to facilitate anticipatory and decision-making processes. However, research has often focused on responders such as batters, goalkeepers, defenders, and returners of serve, and failed to capture the complex interaction between opponents, where responders can also manipulate probabilities in their favour. This investigation aimed to explore the interaction between top order batters and fast or medium paced bowlers in cricket and the information they use to inform their anticipatory and decision-making skills in Twenty20 competition. Eleven professional cricketers were interviewed (8 batters and 3 bowlers) using semi-structured questions and scenarios from Twenty20 matches. An inductive and deductive thematic analysis was conducted using the overarching themes of Situation Awareness (SA) and Option Awareness (OA). Within SA, the sub-themes identified related to information sources used by bowlers and batters (i.e., stable contextual information, dynamic contextual information, kinematic information). Within OA, the sub-themes identified highlighted how cricketers use these information sources to understand the options available and the likelihood of success associated with each option (e.g., risk and reward, personal strengths). A sub-theme of ‘responder manipulation’ was also identified within OA to provide insight into how batters and bowlers interact in a cat-and-mouse like manner to generate options that manipulate one another throughout the competition. A schematic has been developed based on the study findings to illustrate the complex interaction between the anticipation and decision-making processes of professional top order batters and fast or medium paced bowlers in Twenty20 cricket.

Revisiting Galactic Disk and Spiral Arms Using Open Clusters

We use the largest catalog of open clusters in the post-Gaia era to provide an observational view of the Galactic disk. By compiling physical parameters such as age, distance, and kinematic information, we investigate the spatial distribution of open clusters and revisit the spiral arms and other asymmetries in the Galactic disk. Using young open clusters as a tracer of spiral arms, we map the spiral structure of the Galaxy and find that most of the clusters start migrating away from the spiral arms in about 10–20 Myr and fill the interarm regions as they age. Using the 3D kinematic information on 371 open star clusters, we derive different individual pattern speeds for spiral arms that closely follow the rotation curve of the Milky Way, hence favoring the transient nature of spiral arms in the Milky Way. The pattern rotation speeds of each spiral arm suggest that the spiral arms have not accelerated in the last 80 Myr. Based on the distribution of open clusters younger than 700 Myr above or below the Galactic plane, we found a solar offset of z ⊙ = 17.0 ± 0.9 pc north of the Galactic plane and estimated the scale height z h = 91.7 ± 1.9 pc from the Galactic plane.

The BE-UJI hand function activity set: a reduced set of activities for the evaluation of the healthy and pathological hand

BackgroundHand kinematics during hand function tests based on the performance of activities of daily living (ADLs) can provide objective data to determine patients’ functional loss. However, they are rarely used during clinical assessments because of their long duration. Starting with the 20 Sollerman Hand Function Test (SHFT) tasks, we propose identifying a reduced set of ADLs that provides similar kinematic information to the original full set in terms of synergies, ranges of motion and velocities.MethodsWe followed an iterative method with the kinematics of 16 hand joints while performing the 20 ADLs of the SHFT. For each subject, ADLs were ordered according to their influence on the synergies obtained by means of a principal component analysis, the minimum number of ADLs that represented the original kinematic synergies (maximum angle of 30° between synergies), and the maintained ranges of joint movements (85% of the original ones) were selected for each subject. The set of the most frequently selected ADLs was verified to be representative of the SHFT ADLs in terms of motion strategies, ranges of motion and joint velocities when considering healthy subjects and Hand Osteoarthritis patients.ResultsA set of 10 tasks, the BE-UJI activity set, was identified by ensuring a certain (minimum) similarity in synergy (maximum mean angle between synergies of 25.5°), functional joint ranges (maximum differences of 10°) and joint velocities (maximum differences of 15°/s). The obtained tasks were: pick up coins from purses, lift wooden cubes, pick up nuts and turn them, write with a pen, cut with a knife, lift a telephone, unscrew jar lids and pour water from a cup, a jar and a Pure-Pak. These activities guarantee using the seven commonest handgrips in ADLs.ConclusionThe BE-UJI activity set for the hand function assessment can be used to obtain quantitative data in clinics as an alternative to the SHFT. It reduces the test time and allows clinicians to obtain objective kinematic data of the motor strategies, ranges of motion and joint velocities used by patients.