Precision-Dependent Changes in Motor Variability During Sustained Bimanual Reaching

Motor Control ◽  
2018 ◽  
Vol 22 (1) ◽  
pp. 28-44 ◽  
Author(s):  
Alessia Longo ◽  
Ruud Meulenbroek
Keyword(s):  
Motor Variability ◽  
Bimanual Reaching

scholarly journals Goal-Selection and Movement-Related Conflict during Bimanual Reaching Movements

2005 ◽  
Vol 16 (12) ◽  
pp. 1729-1738 ◽  
Author(s):  
J. Diedrichsen ◽  
S. Grafton ◽  
N. Albert ◽  
E. Hazeltine ◽  
R. B. Ivry
Keyword(s):  
Reaching Movements ◽  
Goal Selection ◽  
Bimanual Reaching

scholarly journals Deciphering the relationship between perceptual and motor variability

2011 ◽  
Vol 11 (11) ◽  
pp. 548-548
Author(s):  
D. Liston ◽  
L. Stone
Keyword(s):  
Motor Variability ◽  
The Relationship

scholarly journals Validity and sensitivity of an inertial measurement unit-driven biomechanical model of motor variability for gait

2021 ◽  
Author(s):  
Christopher Bailey ◽  
Thomas Uchida ◽  
Julie Nantel ◽  
Ryan Graham
Keyword(s):  
Inertial Measurement Unit ◽  
Intraclass Correlation ◽  
Biomechanical Model ◽  
Measurement Unit ◽  
Local Dynamic ◽  
Joint Angles ◽  
Motor Variability ◽  
Arm Swing ◽  
Inertial Measurement ◽  
Local Dynamic Stability

Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim. We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λmax), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (e.g., RMSD: root mean square difference), consistency (ICC2,1: intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of Speed and Swing). RMSDs of joint angles were 1.7–7.5° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good–excellent in the primary plane of motion for ROM and in all planes for meanSD and λmax, but were poor–moderate for DFAα and SaEn. Modeled Speed and Swing responses for ROM, meanSD, and λmax were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angles and several motor variability features.

Motor variability prior to learning does not facilitate the ability to adopt new movement solutions

Neuroscience ◽  
2021 ◽  
Author(s):  
Rajiv Ranganathan ◽  
Marco Lin ◽  
Samuel Carey ◽  
Rakshith Lokesh ◽  
Mei-Hua Lee ◽  
...  
Keyword(s):  
Motor Variability

scholarly journals High variability impairs motor learning regardless of whether it affects task performance

2018 ◽  
Vol 119 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Marco Cardis ◽  
Maura Casadio ◽  
Rajiv Ranganathan
Keyword(s):  
Motor Learning ◽  
Task Performance ◽  
Null Space ◽  
Motor Task ◽  
Task Space ◽  
Movement Variability ◽  
Motor Variability ◽  
Different Dimensions ◽  
Left And Right

Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning are not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance) or in the null space (where it has no effect on task performance). We examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible toward a target. Critically, the distance traveled by the puck was determined by the sum of the left- and right-hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups, based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared with groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution.NEW & NOTEWORTHY We examined the role of introducing variability during motor learning in a redundant task. The presence of redundancy allows variability to be introduced in different dimensions: the task space (where it affects task performance) or the null space (where it does not affect task performance). We found that introducing variability affected learning adversely, but the amount of variability was more critical than the dimension in which variability was introduced.

scholarly journals The Role of Motor Learning on Measures of Physical Requirements and Motor Variability During Repetitive Screwing

2019 ◽  
Vol 16 (7) ◽  
pp. 1231 ◽  
Author(s):  
Tessy Luger ◽  
Robert Seibt ◽  
Monika Rieger ◽  
Benjamin Steinhilber
Keyword(s):  
Muscle Activation ◽  
Biceps Brachii ◽  
Extensor Digitorum ◽  
Body Parts ◽  
Triceps Brachii ◽  
Experimental Conditions ◽  
Motor Variability ◽  
Physical Requirement ◽  
Flexor Carpi Radialis ◽  
Arm Muscles

We investigated whether physical requirements and motor variability decreased over days in novices during a repetitive screwing task. Fifty-seven subjects performed one hour of repetitive screwing and fastening on three days, separated by 2–7 days. The average physical requirement and relative cycle-to-cycle variability (coefficient of variation, i.e., CV) were calculated from continuous recordings of electromyography of four arm muscles (biceps brachii, triceps brachii, flexor carpi radialis, extensor digitorum), forearm acceleration, and electrocardiography. Muscle activity levels, heart rate, and forearm acceleration decreased from day 1 to day 2 (range: ~4% to ~20%) and/or 3 (range: ~4% to ~28%). Not all muscles showed a similar pattern. Activity of the extensor digitorum and biceps brachii decreased already between days 1 and 2 (range: ~6% to ~13%), whereas activity of the flexor carpi radialis and triceps brachii decreased between days 1 and 3 (range: ~13% to ~20%). No changes in physical requirement were detected between days 2 and 3. Relative motor variability did not change across days, except that variability of forearm acceleration increased from day 1 to 3 (~5%). This study found consistent changes in physical requirements and indicated that several arm muscles show earlier decreases of muscular activity, like the extensor digitorum, compared to other body parts, like the flexor carpi radialis. Moreover, movement strategies may develop differently than muscle activation strategies, based on the different developments of physical requirements and motor variability. The development of physical requirements in industrial tasks is part of daily living and starts at task onset, highlighting the importance of task familiarization and the randomization of experimental conditions in scientific studies.

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