scholarly journals Fatigue-related reductions in movement smoothness during a lateral shuffle and side-cutting movement

Author(s):  
Maurice Mohr ◽  
Peter Federolf
Keyword(s):  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vikram Jakkamsetti ◽  
William Scudder ◽  
Gauri Kathote ◽  
Qian Ma ◽  
Gustavo Angulo ◽  
...  

AbstractTime-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our goal was to characterize normal variation or motor impairment more robustly than by using time-to-fall. We also hypothesized that measures (or features) early in the sub-structure could anticipate the learning expected of a mouse undergoing serial trials. Using normal untreated and baclofen-treated movement-impaired mice, we defined these features and automated their analysis using paw video-tracking in three consecutive trials, including paw location, speed, acceleration, variance and approximate entropy. Spectral arc length yielded speed and acceleration uniformity. We found that, in normal mice, paw movement smoothness inversely correlated with rotarod time-to-fall for the three trials. Greater approximate entropy in vertical movements, and opposite changes in horizontal movements, correlated with greater first-trial time-to-fall. First-trial horizontal approximate entropy in the first few seconds predicted subsequent time-to-fall. This allowed for the separation, after only one rotarod trial, of different-weight, untreated mouse groups, and for the detection of mice otherwise unimpaired after baclofen, which displayed a time-to-fall similar to control. A machine-learning support vector machine classifier corroborated these findings. In conclusion, time-to-fall off a rotarod correlated well with several measures, including some obtained during the first few seconds of a trial, and some responsive to learning over the first two trials, allowing for predictions or preemptive experimental manipulations before learning completion.


2012 ◽  
Vol 120 (3) ◽  
pp. 195-200 ◽  
Author(s):  
Ichiro Minami ◽  
Rahena Akhter ◽  
Julien Luraschi ◽  
Kazuhiro Oogai ◽  
Tetsu Nemoto ◽  
...  

2010 ◽  
Vol 31 (1) ◽  
pp. 27-31 ◽  
Author(s):  
Kiyoshi Sakata ◽  
Akira Kogure ◽  
Masataka Hosoda ◽  
Koji Isozaki ◽  
Tadashi Masuda ◽  
...  

2017 ◽  
Vol 117 (3) ◽  
pp. 1239-1257 ◽  
Author(s):  
Layne H. Salmond ◽  
Andrew D. Davidson ◽  
Steven K. Charles

Smoothness is a hallmark of healthy movement. Past research indicates that smoothness may be a side product of a control strategy that minimizes error. However, this is not the only reason for smooth movements. Our musculoskeletal system itself contributes to movement smoothness: the mechanical impedance (inertia, damping, and stiffness) of our limbs and joints resists sudden change, resulting in a natural smoothing effect. How the biomechanics and neural control interact to result in an observed level of smoothness is not clear. The purpose of this study is to 1) characterize the smoothness of wrist rotations, 2) compare it with the smoothness of planar shoulder-elbow (reaching) movements, and 3) determine the cause of observed differences in smoothness. Ten healthy subjects performed wrist and reaching movements involving different targets, directions, and speeds. We found wrist movements to be significantly less smooth than reaching movements and to vary in smoothness with movement direction. To identify the causes underlying these observations, we tested a number of hypotheses involving differences in bandwidth, signal-dependent noise, speed, impedance anisotropy, and movement duration. Our simulations revealed that proximal-distal differences in smoothness reflect proximal-distal differences in biomechanics: the greater impedance of the shoulder-elbow filters neural noise more than the wrist. In contrast, differences in signal-dependent noise and speed were not sufficiently large to recreate the observed differences in smoothness. We also found that the variation in wrist movement smoothness with direction appear to be caused by, or at least correlated with, differences in movement duration, not impedance anisotropy. NEW & NOTEWORTHY This article presents the first thorough characterization of the smoothness of wrist rotations (flexion-extension and radial-ulnar deviation) and comparison with the smoothness of reaching (shoulder-elbow) movements. We found wrist rotations to be significantly less smooth than reaching movements and determined that this difference reflects proximal-distal differences in biomechanics: the greater impedance (inertia, damping, stiffness) of the shoulder-elbow filters noise in the command signal more than the impedance of the wrist.


Author(s):  
Shivam Pandey ◽  
Michael D. Byrne ◽  
William H. Jantscher ◽  
Marcia K. O’Malley ◽  
Priyanshu Agarwal

Surgery is a challenging domain for motor skill acquisition. A critical contributing factor in this difficulty is that feedback is often delayed from performance and qualitative in nature. Collection of highdensity motion information may offer a solution. Metrics derived from this motion capture, in particular indices of movement smoothness, have been shown to correlate with task outcomes in multiple domains, including endovascular surgery. The open question is whether providing feedback based on these metrics can be used to accelerate learning. In pursuit of that goal, we examined the relationship between a motion metric that is computationally simple to compute—spectral arc length—and performance on a simple but challenging motor task, mirror tracing. We were able to replicate previous results showing that movement smoothness measures are linked to overall performance, and now have performance thresholds to use in subsequent work on using these metrics for training.


2017 ◽  
Vol 23 (3) ◽  
pp. 262-267
Author(s):  
Svilen Spirdonov ◽  
Svilen Stefanov

Abstract The following report shows methods for determination of the coefficients of stiffness and damping, for softening the vibrations, which ensue in an armoured vehicle’s suspension from the shooting of a mounted machine-gun. The methods are based on a model of the movement smoothness, in which is additionally included the signal of the machine gun’s recoil.


Author(s):  
Alejandro Melendez-Calderon ◽  
Camila Shirota ◽  
Sivakumar Balasubramanian

Inertial measurement units (IMUs) are increasingly used to estimate movement quality and quantity to the infer the nature of motor behavior. The current literature contains several attempts to estimate movement smoothness using data from IMUs, many of which assume that the translational and rotational kinematics measured by IMUs can be directly used with the smoothness measures spectral arc length (SPARC) and log dimensionless jerk (LDLJ-V). However, there has been no investigation of the validity of these approaches. In this paper, we systematically evaluate the use of these measures on the kinematics measured by IMUs. We show that: (a) SPARC and LDLJ-V are valid measures of smoothness only when used with velocity; (b) SPARC and LDLJ-V applied on translational velocity reconstructed from IMU is highly error prone due to drift caused by integration of reconstruction errors; (c) SPARC can be applied directly on rotational velocities measured by a gyroscope, but LDLJ-V can be error prone. For discrete translational movements, we propose a modified version of the LDLJ-V measure, which can be applied to acceleration data (LDLJ-A). We evaluate the performance of these measures using simulated and experimental data. We demonstrate that the accuracy of LDLJ-A depends on the time profile of IMU orientation reconstruction error. Finally, we provide recommendations for how to appropriately apply these measures in practice under different scenarios, and highlight various factors to be aware of when performing smoothness analysis using IMU data.


2021 ◽  
Vol 57 ◽  
pp. 553-561
Author(s):  
Sergei Repin ◽  
Roman Bukirov ◽  
Ivan Vorontsov ◽  
Valeriy Gordienko ◽  
Pawel Rajczyk

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