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Motor Variability (motor + variability)

Distribution by Scientific Domains

Life Sciences	100%

Selected Abstracts

On functional motor adaptations: from the quantification of motor strategies to the prevention of musculoskeletal disorders in the neck,shoulder region

ACTA PHYSIOLOGICA, Issue 2010
P. Madeleine
Abstract Background:, Occupations characterized by a static low load and by repetitive actions show a high prevalence of work-related musculoskeletal disorders (WMSD) in the neck,shoulder region. Moreover, muscle fatigue and discomfort are reported to play a relevant initiating role in WMSD. Aims: To investigate relationships between altered sensory information, i.e. localized muscle fatigue, discomfort and pain and their associations to changes in motor control patterns. Materials & Methods:, In total 101 subjects participated. Questionnaires, subjective assessments of perceived exertion and pain intensity as well as surface electromyography (SEMG), mechanomyography (MMG), force and kinematics recordings were performed. Results:, Multi-channel SEMG and MMG revealed that the degree of heterogeneity of the trapezius muscle activation increased with fatigue. Further, the spatial organization of trapezius muscle activity changed in a dynamic manner during sustained contraction with acute experimental pain. A graduation of the motor changes in relation to the pain stage (acute, subchronic and chronic) and work experience were also found. The duration of the work task was shorter in presence of acute and chronic pain. Acute pain resulted in decreased activity of the painful muscle while in subchronic and chronic pain, a more static muscle activation was found. Posture and movement changed in the presence of neck,shoulder pain. Larger and smaller sizes of arm and trunk movement variability were respectively found in acute pain and subchronic/chronic pain. The size and structure of kinematics variability decreased also in the region of discomfort. Motor variability was higher in workers with high experience. Moreover, the pattern of activation of the upper trapezius muscle changed when receiving SEMG/MMG biofeedback during computer work. Discussion:, SEMG and MMG changes underlie functional mechanisms for the maintenance of force during fatiguing contraction and acute pain that may lead to the widespread pain seen in WMSD. A lack of harmonious muscle recruitment/derecruitment may play a role in pain transition. Motor behavior changed in shoulder pain conditions underlining that motor variability may play a role in the WMSD development as corroborated by the changes in kinematics variability seen with discomfort. This prognostic hypothesis was further, supported by the increased motor variability among workers with high experience. Conclusion:, Quantitative assessments of the functional motor adaptations can be a way to benchmark the pain status and help to indentify signs indicating WMSD development. Motor variability is an important characteristic in ergonomic situations. Future studies will investigate the potential benefit of inducing motor variability in occupational settings. [source]

Computational motor control: feedback and accuracy

EUROPEAN JOURNAL OF NEUROSCIENCE, Issue 4 2008
Emmanuel Guigon
Abstract Speed/accuracy trade-off is a ubiquitous phenomenon in motor behaviour, which has been ascribed to the presence of signal-dependent noise (SDN) in motor commands. Although this explanation can provide a quantitative account of many aspects of motor variability, including Fitts' law, the fact that this law is frequently violated, e.g. during the acquisition of new motor skills, remains unexplained. Here, we describe a principled approach to the influence of noise on motor behaviour, in which motor variability results from the interplay between sensory and motor execution noises in an optimal feedback-controlled system. In this framework, we first show that Fitts' law arises due to signal-dependent motor noise (SDNm) when sensory (proprioceptive) noise is low, e.g. under visual feedback. Then we show that the terminal variability of non-visually guided movement can be explained by the presence of signal-dependent proprioceptive noise. Finally, we show that movement accuracy can be controlled by opposite changes in signal-dependent sensory (SDNs) and SDNm, a phenomenon that could be ascribed to muscular co-contraction. As the model also explains kinematics, kinetics, muscular and neural characteristics of reaching movements, it provides a unified framework to address motor variability. [source]