Effect of texture preference on food texture perception: Exploring the role of matching food texture and preference.

This study aimed to investigate the influence of smooth texture preference on smoothness perception. An online questionnaire (Study 1, n = 464) and a sensory evaluation test (Study 2, n = 65) were administered to Japanese elderly participants (65-74 years), with common Japanese confectionery (Daifuku) as test foods. Through the online questionnaire, four distinct texture preference groups were formed based on the factors of preference for smoothness and firmness of the inner bean paste layer. Analysis of the food preference scale for imbalanced diet (FPSID) revealed that smooth-texture likers were more likely to be picky eaters than firm-texture likers. Furthermore, high (HiSm) and low smoothness preference groups (LoSm) were selected for the food sensory evaluation test to compare perceived textures (smoothness and firmness). Only the HiSm group exhibited a positive association between perceived smoothness and overall texture liking, perceiving smoothness significantly more intense than the LoSm group in situations where overall texture liking was high. This finding indicates that smooth texture preference does not act independently but rather interacts with food texture matching to affect perception. Our findings suggest that when food texture aligns with individuals' preferences, it elicits hedonic emotions and dynamically enhances food texture perception. This preference-involved perceptual process may contribute to the development of more explicit texture preferences.

Sparseness and Smoothness Regularized Imaging for improving the resolution of Cryo-EM single-particle reconstruction.

In this paper, we present a refinement method for cryo-electron microscopy (cryo-EM) single-particle reconstruction, termed as OPUS-SSRI (Sparseness and Smoothness Regularized Imaging). In OPUS-SSRI, spatially varying sparseness and smoothness priors are incorporated to improve the regularity of electron density map, and a type of real space penalty function is designed. Moreover, we define the back-projection step as a local kernel regression and propose a first-order method to solve the resulting optimization problem. On the seven cryo-EM datasets that we tested, the average improvement in resolution by OPUS-SSRI over that from RELION 3.0, the commonly used image-processing software for single-particle cryo-EM, was 0.64 Å, with the largest improvement being 1.25 Å. We expect OPUS-SSRI to be an invaluable tool to the broad field of cryo-EM single-particle analysis. The implementation of OPUS-SSRI can be found at https://github.com/alncat/cryoem.

Measurement properties of movement smoothness metrics for upper limb reaching movements in people with moderate to severe subacute stroke.

BACKGROUND: Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the measurement properties of available smoothness metrics have been poorly assessed in this group. We aimed to measure the reliability, responsiveness and construct validity of several smoothness metrics. METHODS: This ancillary study of the REM-AVC trial included 31 participants with hemiparesis in the subacute phase of stroke (median time since stroke: 38 days). Assessments performed at inclusion (Day 0, D0) and at the end of a rehabilitation program (Day 30, D30) included the UE Fugl Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and 3D motion analysis of the UE during three reach-to-point movements at a self-selected speed to a target located in front at shoulder height and at 90% of arm length. Four smoothness metrics were computed: a frequency domain smoothness metric, spectral arc length metric (SPARC); and three temporal domain smoothness metrics (TDSM): log dimensionless jerk (LDLJ); number of submovements (nSUB); and normalized average rectified jerk (NARJ). RESULTS: At D30, large clinical and kinematic improvements were observed. Only SPARC and LDLJ had an excellent reliability (intra-class correlation > 0.9) and a low measurement error (coefficient of variation < 10%). SPARC was responsive to changes in movement straightness (rSpearman=0.64) and to a lesser extent to changes in movement duration (rSpearman=0.51) while TDSM were very responsive to changes in movement duration (rSpearman>0.8) and not to changes in movement straightness (non-significant correlations). Most construct validity hypotheses tested were verified except for TDSM with low correlations with clinical metrics at D0 (rSpearman<0.5), ensuing low predictive validity with clinical metrics at D30 (non-significant correlations). CONCLUSIONS: Responsiveness and construct validity of TDSM were hindered by movement duration and/or noise-sensitivity. Based on the present results and concordant literature, we recommend using SPARC rather than TDSM in reaching movements of uncontrolled duration in individuals with spastic paresis after stroke. TRIAL REGISTRATION: NCT01383512, https://clinicaltrials.gov/ , June 27, 2011.

Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm.

Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions.

Development of Antimicrobial Surfaces Using Diamond-like Carbon or Diamond-like Carbon-Based Coatings.

The medical device market is a high-growth sector expected to sustain an annual growth rate of over 5%, even in developed countries. Daily, numerous patients have medical devices implanted or inserted within their bodies. While medical devices have significantly improved patient outcomes, as foreign objects, their wider use can lead to an increase in device-related infections, thereby imposing a burden on healthcare systems. Multiple materials with significant societal impact have evolved over time: the 19th century was the age of iron, the 20th century was dominated by silicon, and the 21st century is often referred to as the era of carbon. In particular, the development of nanocarbon materials and their potential applications in medicine are being explored, although the scope of these applications remains limited. Technological innovations in carbon materials are remarkable, and their application in medicine is expected to advance greatly. For example, diamond-like carbon (DLC) has garnered considerable attention for the development of antimicrobial surfaces. Both DLC itself and its derivatives have been reported to exhibit anti-microbial properties. This review discusses the current state of DLC-based antimicrobial surface development.

Exploring the limits of MEG spatial resolution with multipolar expansions.

The advent of scalp magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) may represent a step change in the field of human electrophysiology. Compared to cryogenic MEG based on superconducting quantum interference devices (SQUIDs, placed 2-4 cm above scalp), scalp MEG promises significantly higher spatial resolution imaging but it also comes with numerous challenges regarding how to optimally design OPM arrays. In this context, we sought to provide a systematic description of MEG spatial resolution as a function of the number of sensors (allowing comparison of low- vs. high-density MEG), sensor-to-brain distance (cryogenic SQUIDs vs. scalp OPM), sensor type (magnetometers vs. gradiometers; single- vs. multi-component sensors), and signal-to-noise ratio. To that aim, we present an analytical theory based on MEG multipolar expansions that enables, once supplemented with experimental input and simulations, quantitative assessment of the limits of MEG spatial resolution in terms of two qualitatively distinct regimes. In the regime of asymptotically high-density MEG, we provide a mathematically rigorous description of how magnetic field smoothness constraints spatial resolution to a slow, logarithmic divergence. In the opposite regime of low-density MEG, it is sensor density that constraints spatial resolution to a faster increase following a square-root law. The transition between these two regimes controls how MEG spatial resolution saturates as sensors approach sources of neural activity. This two-regime model of MEG spatial resolution integrates known observations (e.g., the difficulty of improving spatial resolution by increasing sensor density, the gain brought by moving sensors on scalp, or the usefulness of multi-component sensors) and gathers them under a unifying theoretical framework that highlights the underlying physics and reveals properties inaccessible to simulations. We propose that this framework may find useful applications to benchmark the design of future OPM-based scalp MEG systems.

On interquantile smoothness of censored quantile regression with induced smoothing.

Quantile regression has emerged as a useful and effective tool in modeling survival data, especially for cases where noises demonstrate heterogeneity. Despite recent advancements, non-smooth components involved in censored quantile regression estimators may often yield numerically unstable results, which, in turn, lead to potentially self-contradicting conclusions. We propose an estimating equation-based approach to obtain consistent estimators of the regression coefficients of interest via the induced smoothing technique to circumvent the difficulty. Our proposed estimator can be shown to be asymptotically equivalent to its original unsmoothed version, whose consistency and asymptotic normality can be readily established. Extensions to handle functional covariate data and recurrent event data are also discussed. To alleviate the heavy computational burden of bootstrap-based variance estimation, we also propose an efficient resampling procedure that reduces the computational time considerably. Our numerical studies demonstrate that our proposed estimator provides substantially smoother model parameter estimates across different quantile levels and can achieve better statistical efficiency compared to a plain estimator under various finite-sample settings. The proposed method is also illustrated via four survival datasets, including the HMO (health maintenance organizations) HIV (human immunodeficiency virus) data, the primary biliary cirrhosis (PBC) data, and so forth.

Comparing high and low energy outcomes on day one for SmartSight myopic-astigmatism treatments with the SCHWIND ATOS: a retrospective case series.

BACKGROUND: Impact of low energy asymmetric spacings vs. high energy symmetric spacings on the immediate/early (postoperative day 1 (POD1)) outcomes of SmartSight lenticule extraction for myopic astigmatism with a new femtosecond laser system. METHODS: The first 112 eyes of 56 patients consecutively treated using low energy asymmetric spacings (Group A; Study group) were compared at POD1 to the last 112 eyes of 56 patients consecutively treated using high energy symmetric spacings (Group S; Controls). Mean age of the patients was 28 ± 5 years with a mean spherical equivalent of -4.41 ± 1.76 diopters (D) and a mean magnitude of refractive astigmatism of 0.89 ± 0.82 D. RESULTS: Laser Energy was -25 ± 1nJ lower for asymmetric treatments (p < .0001); Spot and Track distances were + 0.7 ± 0.1 µm larger and -0.8 ± 0.1 µm tighter for asymmetric treatments, respectively (p < .0001 for both). At POD1, astigmatism was -0.08 ± 0.02D lower for asymmetric treatments (p < .0003); uncorrected and corrected visual acuities (UDVA and CDVA, respectively) were -0.03 ± 0.01logMAR better for asymmetric treatments (p < .0007); differences between postop UDVA and preop CDVA along with change in CDVA were + 0.3 ± 0.1lines better for asymmetric treatments (p < .0003). CONCLUSIONS: Lenticule extraction treatment using SmartSight is safe and efficacious already at POD1. Findings suggest that low energy asymmetric spacings may further improve the immediate and short-term outcomes of SmartSight lenticule extraction in the treatment of myopic astigmatism compared to conventional settings (high energy symmetric spacings).

Instrumental evaluation of gait smoothness and history of falling in older persons: results from an exploratory case-control study.

Gait smoothness, perceived when a person walks continuously and uninterruptedly, is associated with an undisrupted gait pattern, good sensorimotor control, and a lower risk of falling. The spectral arc length (SPARC) is a quantitative metric proposed for the evaluation of movement smoothness from the signal obtained by wearable sensors. In this small exploratory case-control study, older persons with and without a history of injurious falls underwent a turn-test while wearing an accelerometer: gait smoothness was estimated by calculating SPARC during the straight and turning phases. Cases seemed to exhibit lower SPARC values during the turning phase, in comparison with control.

The influence of midsole horizontal and vertical deformation on soft tissue vibrations and bone acceleration during running.

Increased midsole deformation can limit exposure to high impact and vibration magnitudes while running. The aim of this study was to evaluate the effect of shoes eliciting different midsole deformation on ground reaction forces, heel impact, soft tissue vibrations and bone vibrations. Forty-eight runners performed a 5-min running task on an instrumented treadmill at a self-selected pace with four different shoes. Midsole horizontal and vertical deformations were quantified with relative displacement of seven reflective markers placed on the midsole of the shoe and tracked by eight optoelectronic cameras. Heel impacts, soft tissue and bone vibrations of lower leg muscle groups, sacrum and head were quantified with tri-axial accelerometers. Continuous wavelet transform was used to assess magnitude and frequency of the acceleration data. Linear mixed models and non-parametric one-dimensional regressions between the accelerometer data and shoe deformation were performed. Greater horizontal and vertical deformations decreased the magnitude (up to 4.6% per mm) and frequency (up to 0.6 Hz per mm) of soft tissue vibrations and bone accelerations. Accelerations of the heel, tibia, gastrocnemius medialis and vastus lateralis were more influenced than the sacrum and head. Increasing midsole deformation could therefore mitigate the risk of injury, while increasing running comfort and smoothness.

Comparison of Various Smoothness Metrics for Upper Limb Movements in Middle-Aged Healthy Subjects.

BACKGOUND: Metrics for movement smoothness include the number of zero-crossings on the acceleration profile (N0C), the log dimensionless jerk (LDLJ), the normalized averaged rectified jerk (NARJ) and the spectral arc length (SPARC). Sensitivity to the handedness and movement type of these four metrics was compared and correlations with other kinematic parameters were explored in healthy subjects. METHODS: Thirty-two healthy participants underwent 3D upper limb motion analysis during two sets of pointing movements on each side. They performed forward- and backward-pointing movements at a self-selected speed to a target located ahead at shoulder height and at 90% arm length, with and without a three-second pause between forward and backward movements. Kinematics were collected, and smoothness metrics were computed. RESULTS: LDLJ, NARJ and N0C found backward movements to be smoother, while SPARC found the opposite. Inter- and intra-subject coefficients of variation were lowest for SPARC. LDLJ, NARJ and N0C were correlated with each other and with movement time, unlike SPARC. CONCLUSION: There are major differences between smoothness metrics measured in the temporal domain (N0C, LDLJ, NARJ), which depend on movement time, and those measured in the frequency domain, the SPARC, which gave results opposite to the other metrics when comparing backward and forward movements.

A Feature-Trajectory-Smoothed High-Speed Model for Video Anomaly Detection.

High-speed detection of abnormal frames in surveillance videos is essential for security. This paper proposes a new video anomaly-detection model, namely, feature trajectory-smoothed long short-term memory (FTS-LSTM). This model trains an LSTM autoencoder network to generate future frames on normal video streams, and uses the FTS detector and generation error (GE) detector to detect anomalies on testing video streams. FTS loss is a new indicator in the anomaly-detection area. In the training stage, the model applies a feature trajectory smoothness (FTS) loss to constrain the LSTM layer. This loss enables the LSTM layer to learn the temporal regularity of video streams more precisely. In the detection stage, the model utilizes the FTS loss and the GE loss as two detectors to detect anomalies. By cascading the FTS detector and the GE detector to detect anomalies, the model achieves a high speed and competitive anomaly-detection performance on multiple datasets.

Motion Smoothness-Based Assessment of Surgical Expertise: The Importance of Selecting Proper Metrics.

The smooth movement of hand/surgical instruments is considered an indicator of skilled, coordinated surgical performance. Jerky surgical instrument movements or hand tremors can cause unwanted damages to the surgical site. Different methods have been used in previous studies for assessing motion smoothness, causing conflicting results regarding the comparison among surgical skill levels. We recruited four attending surgeons, five surgical residents, and nine novices. The participants conducted three simulated laparoscopic tasks, including peg transfer, bimanual peg transfer, and rubber band translocation. Tooltip motion smoothness was computed using the mean tooltip motion jerk, logarithmic dimensionless tooltip motion jerk, and 95% tooltip motion frequency (originally proposed in this study) to evaluate their capability of surgical skill level differentiation. The results revealed that logarithmic dimensionless motion jerk and 95% motion frequency were capable of distinguishing skill levels, indicated by smoother tooltip movements observed in high compared to low skill levels. Contrarily, mean motion jerk was not able to distinguish the skill levels. Additionally, 95% motion frequency was less affected by the measurement noise since it did not require the calculation of motion jerk, and 95% motion frequency and logarithmic dimensionless motion jerk yielded a better motion smoothness assessment outcome in distinguishing skill levels than mean motion jerk.

Research on Key Technologies for the Static Measurement of Railway Track Smoothness.

In this study, a static railway track smoothness detection system based on laser reference, which can measure various track smoothness parameters by using multiple sensors, is proposed. Furthermore, in order to improve the measurement accuracy and stability of the system, this paper also conducted three key analyses based on the static track measurement system. By using a liquid double-wedge automatic compensation device to compensate the horizontal angle of the beam, a mathematical model of liquid double-wedge automatic compensation was established. Then, by using an optical ring grating system to ring-grate and characterize the laser spot, the collimation efficiency of the system was improved when measuring at long distances. For the special ring grating spot image, an adaptive image processing algorithm was proposed, which can achieve sub-pixel-level positioning accuracy. This study also conducted a field measurement experiment, comparing the experimental data obtained via the static track measurement system with the results of existing track measurement products, and verifying that the static track measurement system has high measurement accuracy and stability.

Vestibular Rehabilitation Improves Gait Quality and Activities of Daily Living in People with Severe Traumatic Brain Injury: A Randomized Clinical Trial.

Neurorehabilitation research in patients with traumatic brain injury (TBI) showed how vestibular rehabilitation (VR) treatments positively affect concussion-related symptoms, but no studies have been carried out in patients with severe TBI (sTBI) during post-acute intensive neurorehabilitation. We aimed at testing this effect by combining sensor-based gait analysis and clinical scales assessment. We hypothesized that integrating VR in post-acute neurorehabilitation training might improve gait quality and activity of daily living (ADL) in sTBI patients. A two-arm, single-blind randomized controlled trial with 8 weeks of follow-up was performed including thirty sTBI inpatients that underwent an 8-week rehabilitation program including either a VR or a conventional program. Gait quality parameters were obtained using body-mounted magneto-inertial sensors during instrumented linear and curvilinear walking tests. A 4X2 mixed model ANOVA was used to investigate session−group interactions and main effects. Patients undergoing VR exhibited improvements in ADL, showing early improvements in clinical scores. Sensor-based assessment of curvilinear pathways highlighted significant VR-related improvements in gait smoothness over time (p < 0.05), whereas both treatments exhibited distinct improvements in gait quality. Integrating VR in conventional neurorehabilitation is a suitable strategy to improve gait smoothness and ADL in sTBI patients. Instrumented protocols are further promoted as an additional measure to quantify the efficacy of neurorehabilitation treatments.

Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing.

Hyperspectral unmixing (HU) is a technique for estimating a set of pure source signals (end members) and their proportions (abundances) from each pixel of the hyperspectral image. Non-negative matrix factorization (NMF) can decompose the observation matrix into the product of two non-negative matrices simultaneously and can be used in HU. Unfortunately, a limitation of many traditional NMF-based methods, i.e., the non-convexity of the objective function, may lead to a sub-optimal solution. Thus, we put forward a new unmixing method based on NMF under smoothing and sparse constraints to obtain a better solution. First, considering the sparseness of the abundance matrix, a weight sparse regularization is introduced into the NMF model to ensure the sparseness of the abundance matrix. Second, according to the similarity prior of the same feature in the adjacent pixels, a Total Variation regularization is further added to the NMF model to improve the smoothness of the abundance map. Finally, the signatures of each end member are modified smoothly in spectral space. Moreover, it is noticed that discontinuities may emerge due to the removal of noisy bands. Therefore, the spectral data are piecewise smooth in spectral space. Then, in this paper, a piecewise smoothness constraint is further applied to each column of the end-member matrix. Experiments are conducted to evaluate the effectiveness of the proposed method based on two different datasets, including a synthetic dataset and the real-life Cuprite dataset, respectively. Experimental results show that the proposed method outperforms several state-of-the-art HU methods. In the Cuprite hyperspectral dataset, the proposed method's Spectral Angle Distance is 0.1694, compared to the TV-RSNMF method's 0.1703, L1/2NMF method's 0.1925, and VCA-FCLS method's 0.1872.

In situ oral lubrication and smoothness sensory perception influenced by tongue surface roughness.

BACKGROUND: The human tongue is important in the oral processing of food and in sensory perception. Tongue topography could influence delicate differences in sensory perception. It is hypothesized that tongue surface roughness could alter oral lubrication status and affect perception of smoothness. Fifteen participants with varying levels of tongue surface roughness were recruited and tested. Participants' in situ oral lubrication status without and after consumption of fluid food (milk with varying fat content and maltodextrin solutions with different shear viscosities) was measured. Participants' smoothness sensory scores were also recorded. RESULTS: The in situ friction coefficient (0.299-1.505) was significantly positively correlated with tongue-surface roughness (54.6-140.0 µm) in all types of test fluid samples across participants. Oral lubrication was significantly decreased when participants consumed the test fluid samples compared with no liquid food consumption, for all test fluid sample types (P < 0.05). No significant differences in in situ friction coefficient were found after participants consumed different test fluid samples, and this was mainly attributed to the limited quantities of fluid residuals in the oral cavity after expectoration. Participants whose tongue surface roughness differed did not exhibit significant differences in smoothness perception with different test fluid samples. CONCLUSION: Tongue surface roughness has a strong impact on in situ oral lubrication, and fluid food intake reduces in situ oral lubrication significantly. Saliva film and tongue surface roughness might play greater roles in oral lubrication and smoothness sensory perception if fluid is expectorated after consumption. The association between oral physiology and texture perception still needs further elucidation. © 2021 Society of Chemical Industry.

Categorical Smoothness of 4-Manifolds from Quantum Symmetries and the Information Loss Paradox.

In this paper, we focus on some aspects of the relation of spacetime and quantum mechanics and the study counterparts (in Set) of the categorical local symmetries of smooth 4-manifolds. In the set-theoretic limit, there emerge some exotic smoothness structures on R4 (hence the Riemannian nonvanishing curvature), which fit well with the quantum mechanical lattice of projections on infinite-dimensional Hilbert spaces. The method we follow is formalization localized on the open covers of the spacetime manifold. We discuss our findings in the context of the information paradox assigned to evaporating black holes. A black hole can evaporate entirely, but the smoothness structure of spacetime will be altered and, in this way, the missing information about the initial states of matter forming the black hole will be encoded. Thus, the possible global geometric remnant of black holes in spacetime is recognized as exotic 4-smoothness. The full-fledged verification of this proposal will presumably be possible within the scope of future quantum gravity theory research.

Smoothness metrics for reaching performance after stroke. Part 1: which one to choose?

BACKGROUND: Smoothness is commonly used for measuring movement quality of the upper paretic limb during reaching tasks after stroke. Many different smoothness metrics have been used in stroke research, but a 'valid' metric has not been identified. A systematic review and subsequent rigorous analysis of smoothness metrics used in stroke research, in terms of their mathematical definitions and response to simulated perturbations, is needed to conclude whether they are valid for measuring smoothness. Our objective was to provide a recommendation for metrics that reflect smoothness after stroke based on: (1) a systematic review of smoothness metrics for reaching used in stroke research, (2) the mathematical description of the metrics, and (3) the response of metrics to simulated changes associated with smoothness deficits in the reaching profile. METHODS: The systematic review was performed by screening electronic databases using combined keyword groups Stroke, Reaching and Smoothness. Subsequently, each metric identified was assessed with mathematical criteria regarding smoothness: (a) being dimensionless, (b) being reproducible, (c) being based on rate of change of position, and (d) not being a linear transform of other smoothness metrics. The resulting metrics were tested for their response to simulated changes in reaching using models of velocity profiles with varying reaching distances and durations, harmonic disturbances, noise, and sub-movements. Two reaching tasks were simulated; reach-to-point and reach-to-grasp. The metrics that responded as expected in all simulation analyses were considered to be valid. RESULTS: The systematic review identified 32 different smoothness metrics, 17 of which were excluded based on mathematical criteria, and 13 more as they did not respond as expected in all simulation analyses. Eventually, we found that, for reach-to-point and reach-to-grasp movements, only Spectral Arc Length (SPARC) was found to be a valid metric. CONCLUSIONS: Based on this systematic review and simulation analyses, we recommend the use of SPARC as a valid smoothness metric in both reach-to-point and reach-to-grasp tasks of the upper limb after stroke. However, further research is needed to understand the time course of smoothness measured with SPARC for the upper limb early post stroke, preferably in longitudinal studies.

Smoothness metric during reach-to-grasp after stroke: part 2. longitudinal association with motor impairment.

BACKGROUND: The cause of smoothness deficits as a proxy for quality of movement post stroke is currently unclear. Previous simulation analyses showed that spectral arc length (SPARC) is a valid metric for investigating smoothness during a multi-joint goal-directed reaching task. The goal of this observational study was to investigate how SPARC values change over time, and whether SPARC is longitudinally associated with the recovery from motor impairments reflected by the Fugl-Meyer motor assessment of the upper extremity (FM-UE) in the first 6 months after stroke. METHODS: Forty patients who suffered a first-ever unilateral ischemic stroke (22 males, aged 58.6 ± 12.5 years) with upper extremity paresis underwent kinematic and clinical measurements in weeks 1, 2, 3, 4, 5, 8, 12, and 26 post stroke. Clinical measures included amongst others FM-UE. SPARC was obtained by three-dimensional kinematic measurements using an electromagnetic motion tracking system during a reach-to-grasp movement. Kinematic assessments of 12 healthy, age-matched individuals served as reference. Longitudinal linear mixed model analyses were performed to determine SPARC change over time, compare smoothness in patients with reference values of healthy individuals, and establish the longitudinal association between SPARC and FM-UE scores. RESULTS: SPARC showed a significant positive longitudinal association with FM-UE (B: 31.73, 95%-CI: [27.27 36.20], P < 0.001), which encompassed significant within- and between-subject effects (B: 30.85, 95%-CI: [26.28 35.41], P < 0.001 and B: 50.59, 95%-CI: [29.97 71.21], P < 0.001, respectively). Until 5 weeks post stroke, progress of time contributed significantly to the increase in SPARC and FM-UE scores (P < 0.05), whereafter they levelled off. At group level, smoothness was lower in patients who suffered a stroke compared to healthy subjects at all time points (P < 0.05). CONCLUSIONS: The present findings show that, after stroke, recovery of smoothness in a multi-joint reaching task and recovery from motor impairments are longitudinally associated and follow a similar time course. This suggests that the reduction of smoothness deficits quantified by SPARC is a proper objective reflection of recovery from motor impairment, as reflected by FM-UE, probably driven by a common underlying process of spontaneous neurological recovery early post stroke.