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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.


(A) Overall adaptation to perturbation. Bars show the mean trajectory area (TA) calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line between the start and end COM positions. The pink bars represent the unperturbed blocks; the first block of the experimental procedure (block 1) which served as the baseline and the last concluding block that shows that the experimental procedure did not alter the unperturbed motion of the subjects. The grey bars show that the subjects adapted to the perturbation in the first three perturbed blocks. The insignificant difference between the last two perturbed blocks shows that the adaptation stabilized and can be considered as settled. In spite of the settled adaptation, the COM trajectories stayed significantly different from the unperturbed trajectories. The error bars indicate standard error. Significant differences are indicated (*p < .05). (B) Average number of failed trials during adaptation to perturbation. The average number of failed trials decreased significantly during first two perturbed blocks 2 and 3 and stabilized during the subsequent blocks 4 and 5. The decrease followed a Power law function depicted as a red curve. The error bars indicate standard deviation.
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f3: (A) Overall adaptation to perturbation. Bars show the mean trajectory area (TA) calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line between the start and end COM positions. The pink bars represent the unperturbed blocks; the first block of the experimental procedure (block 1) which served as the baseline and the last concluding block that shows that the experimental procedure did not alter the unperturbed motion of the subjects. The grey bars show that the subjects adapted to the perturbation in the first three perturbed blocks. The insignificant difference between the last two perturbed blocks shows that the adaptation stabilized and can be considered as settled. In spite of the settled adaptation, the COM trajectories stayed significantly different from the unperturbed trajectories. The error bars indicate standard error. Significant differences are indicated (*p < .05). (B) Average number of failed trials during adaptation to perturbation. The average number of failed trials decreased significantly during first two perturbed blocks 2 and 3 and stabilized during the subsequent blocks 4 and 5. The decrease followed a Power law function depicted as a red curve. The error bars indicate standard deviation.

Mentions: Analysis of variance showed significant adaptation effect of squat-to-stand repetitions with perturbations in reducing the deviation of the COM trajectory from the unperturbed trajectories, F(3, 21) = 5.96, p = .004, . Subjects rapidly adapted to platform-induced perturbations reducing the deviation of the COM trajectory (Fig. 3A). Post-hoc analyses showed that the reduction was significant from P blocks 2 to 4, t(7) = 3.75, p = .007. Trend analysis revealed a significant linear trend, F(1, 7) = 6.36, p = .04, , indicating that adaptation happened in the first three P blocks. Besides, there was also a significant quadratic trend, F(1, 7) = 8.71, p = .021, , reflecting that the adaptation settled at the last two P blocks. In spite of significant reduction of the trajectory area (TA) during adaptation (* on Fig. 3A), the trajectories retained the curvature in the anterior direction and stayed significantly different from the unperturbed trajectories, t(7) = 3.71, p = .008. There was no statistically significant difference between the COM trajectories of the first and the last unperturbed blocks (blocks 1 and 8), t(7) = 1.486, p = .181, which indicates that the experimental procedure had no influence on the unperturbed motion of the subjects.


Human motor adaptation in whole body motion
(A) Overall adaptation to perturbation. Bars show the mean trajectory area (TA) calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line between the start and end COM positions. The pink bars represent the unperturbed blocks; the first block of the experimental procedure (block 1) which served as the baseline and the last concluding block that shows that the experimental procedure did not alter the unperturbed motion of the subjects. The grey bars show that the subjects adapted to the perturbation in the first three perturbed blocks. The insignificant difference between the last two perturbed blocks shows that the adaptation stabilized and can be considered as settled. In spite of the settled adaptation, the COM trajectories stayed significantly different from the unperturbed trajectories. The error bars indicate standard error. Significant differences are indicated (*p < .05). (B) Average number of failed trials during adaptation to perturbation. The average number of failed trials decreased significantly during first two perturbed blocks 2 and 3 and stabilized during the subsequent blocks 4 and 5. The decrease followed a Power law function depicted as a red curve. The error bars indicate standard deviation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: (A) Overall adaptation to perturbation. Bars show the mean trajectory area (TA) calculated as the total deviation of the center-of-mass (COM) trajectory with respect to the straight line between the start and end COM positions. The pink bars represent the unperturbed blocks; the first block of the experimental procedure (block 1) which served as the baseline and the last concluding block that shows that the experimental procedure did not alter the unperturbed motion of the subjects. The grey bars show that the subjects adapted to the perturbation in the first three perturbed blocks. The insignificant difference between the last two perturbed blocks shows that the adaptation stabilized and can be considered as settled. In spite of the settled adaptation, the COM trajectories stayed significantly different from the unperturbed trajectories. The error bars indicate standard error. Significant differences are indicated (*p < .05). (B) Average number of failed trials during adaptation to perturbation. The average number of failed trials decreased significantly during first two perturbed blocks 2 and 3 and stabilized during the subsequent blocks 4 and 5. The decrease followed a Power law function depicted as a red curve. The error bars indicate standard deviation.
Mentions: Analysis of variance showed significant adaptation effect of squat-to-stand repetitions with perturbations in reducing the deviation of the COM trajectory from the unperturbed trajectories, F(3, 21) = 5.96, p = .004, . Subjects rapidly adapted to platform-induced perturbations reducing the deviation of the COM trajectory (Fig. 3A). Post-hoc analyses showed that the reduction was significant from P blocks 2 to 4, t(7) = 3.75, p = .007. Trend analysis revealed a significant linear trend, F(1, 7) = 6.36, p = .04, , indicating that adaptation happened in the first three P blocks. Besides, there was also a significant quadratic trend, F(1, 7) = 8.71, p = .021, , reflecting that the adaptation settled at the last two P blocks. In spite of significant reduction of the trajectory area (TA) during adaptation (* on Fig. 3A), the trajectories retained the curvature in the anterior direction and stayed significantly different from the unperturbed trajectories, t(7) = 3.71, p = .008. There was no statistically significant difference between the COM trajectories of the first and the last unperturbed blocks (blocks 1 and 8), t(7) = 1.486, p = .181, which indicates that the experimental procedure had no influence on the unperturbed motion of the subjects.

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.