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Human motor adaptation in whole body motion

View Article: PubMed Central - PubMed

ABSTRACT

The main role of the sensorimotor system of an organism is to increase the survival of the species. Therefore, to understand the adaptation and optimality mechanisms of motor control, it is necessary to study the sensorimotor system in terms of ecological fitness. We designed an experimental paradigm that exposed sensorimotor system to risk of injury. We studied human subjects performing uncon- strained squat-to-stand movements that were systematically subjected to non-trivial perturbation. We found that subjects adapted by actively compensating the perturbations, converging to movements that were different from their normal unperturbed squat-to-stand movements. Furthermore, the adapted movements had clear intrinsic inter-subject differences which could be explained by different adapta- tion strategies employed by the subjects. These results suggest that classical optimality measures of physical energy and task satisfaction should be seen as part of a hierarchical organization of optimality with safety being at the highest level. Therefore, in addition to physical energy and task fulfillment, the risk of injury and other possible costs such as neural computational overhead have to be considered when analyzing human movement.

No MeSH data available.


Related in: MedlinePlus

Individual evolution of adaptations to perturbations.Data points show trajectory area (TA) of the trials calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line for each individual subject. The leftmost pink points represent the first unperturbed block while the rightmost pink points represent the last unperturbed block to show that the experimental procedure did not alter the unperturbed motion of the individual subject. The grey points represent the trials during the perturbation blocks 2, 3, 4, and 5. The superimposed exponential decay curves with the corresponding r values model individual adaptations to perturbations as functions of trial number.
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f4: Individual evolution of adaptations to perturbations.Data points show trajectory area (TA) of the trials calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line for each individual subject. The leftmost pink points represent the first unperturbed block while the rightmost pink points represent the last unperturbed block to show that the experimental procedure did not alter the unperturbed motion of the individual subject. The grey points represent the trials during the perturbation blocks 2, 3, 4, and 5. The superimposed exponential decay curves with the corresponding r values model individual adaptations to perturbations as functions of trial number.

Mentions: Adaptations for individual subjects are shown in Fig. 4. To model individual evolution of adaptations to perturbations, exponential decay curves were fitted to the trajectory area of the trials using robust least-squares regression with bisquare weight method.


Human motor adaptation in whole body motion
Individual evolution of adaptations to perturbations.Data points show trajectory area (TA) of the trials calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line for each individual subject. The leftmost pink points represent the first unperturbed block while the rightmost pink points represent the last unperturbed block to show that the experimental procedure did not alter the unperturbed motion of the individual subject. The grey points represent the trials during the perturbation blocks 2, 3, 4, and 5. The superimposed exponential decay curves with the corresponding r values model individual adaptations to perturbations as functions of trial number.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5016968&req=5

f4: Individual evolution of adaptations to perturbations.Data points show trajectory area (TA) of the trials calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line for each individual subject. The leftmost pink points represent the first unperturbed block while the rightmost pink points represent the last unperturbed block to show that the experimental procedure did not alter the unperturbed motion of the individual subject. The grey points represent the trials during the perturbation blocks 2, 3, 4, and 5. The superimposed exponential decay curves with the corresponding r values model individual adaptations to perturbations as functions of trial number.
Mentions: Adaptations for individual subjects are shown in Fig. 4. To model individual evolution of adaptations to perturbations, exponential decay curves were fitted to the trajectory area of the trials using robust least-squares regression with bisquare weight method.

View Article: PubMed Central - PubMed

ABSTRACT

The main role of the sensorimotor system of an organism is to increase the survival of the species. Therefore, to understand the adaptation and optimality mechanisms of motor control, it is necessary to study the sensorimotor system in terms of ecological fitness. We designed an experimental paradigm that exposed sensorimotor system to risk of injury. We studied human subjects performing uncon- strained squat-to-stand movements that were systematically subjected to non-trivial perturbation. We found that subjects adapted by actively compensating the perturbations, converging to movements that were different from their normal unperturbed squat-to-stand movements. Furthermore, the adapted movements had clear intrinsic inter-subject differences which could be explained by different adapta- tion strategies employed by the subjects. These results suggest that classical optimality measures of physical energy and task satisfaction should be seen as part of a hierarchical organization of optimality with safety being at the highest level. Therefore, in addition to physical energy and task fulfillment, the risk of injury and other possible costs such as neural computational overhead have to be considered when analyzing human movement.

No MeSH data available.


Related in: MedlinePlus