Related Experiment Video
Updated: May 20, 2026
10:52
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
Published on: April 13, 2016
Movement variability resulting from different noise sources: a simulation study
1School of Biological and Health Systems Engineering, Arizona State University, P.O. Box 879709, Tempe, AZ 85287, USA. yshi7@asu.edu
Human Movement Science
|July 17, 2012
Summary
Movement variability stems from noise in sensing limb position and planning movement trajectories. This study simulated these noise sources, revealing complex interactions influencing arm movement across the workspace.
Area of Science:
- Motor control
- Biomechanics
- Computational neuroscience
Background:
- Limb movements exhibit significant variability due to noise in sensory and motor processes.
- Understanding the sources of this noise is crucial for explaining motor variability and its alterations in neurological conditions.
Purpose of the Study:
- To predict the impact of position sensing noise and trajectory planning noise on arm movement variability using simulations.
- To compare the effects of sensing and planning noise with previously modeled execution noise.
- To investigate the complex interactions between sensing and planning noise across different movement conditions.
Main Methods:
- A simulation approach was employed to model the effects of noise on limb movement.
- Noise sources included position sensing noise and trajectory planning noise.
- Simulated movement variability was analyzed across the workspace and compared to existing models.
Main Results:
- Noise in limb position sensing and trajectory planning significantly affects arm movement variability.
- The impact of sensing and planning noise is dependent on movement direction and initial arm configuration.
- Sensing and planning noise interact in complex ways, differing from the effects of execution noise.
Conclusions:
- Sensing and planning noise play critical roles in determining motor variability.
- These findings offer insights into neurological disorders characterized by exaggerated movement variability.
- The results aid in interpreting neurophysiological studies linking neural and behavioral variability.
More Related Videos
06:22Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
Published on: August 28, 2019
09:46MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
Published on: May 10, 2012
Related Concept Videos
01:11Variability: Analysis
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
01:20Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
01:20Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:15Elastic Collisions: Case Study
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...