PW_thesis_defense

PowerPoint Presentation

Adaptation of Inter-limb Control During Robot-simulated Human Standing Balance

MASc thesis by Philip Wang

Supervisors: Drs. Elizabeth Croft,Machiel Van der Loos, Jean-Sbastien Blouin

February 25, 2016

10

1

Outline

Motivation, Research Question, Background

Study I

Study II

Summary, Contributions, Future Work

8/24/2016


2

30; 0:40

First and second human-participant studies

2

Motivation

balance: a fundamental skill

stroke: weakened left/right side

center of pressure biofeedback

reduces weight asymmetry1

does not improve functional balance1

reducing asymmetry of lower limbs contributions to balance (e.g., torque activity)

Barclay-Goddard et al. (2004). Force platform feedback for standing balance training after stroke. The Cochrane Database of Systematic Reviews, (4), CD004129.

8/24/2016


3

www.sehealth.org

www.deltason.com

60; 1:40

Balance: fundamental motor skill

Normally automatic

Some populations have difficulty

Stroke survivors have difficulty due to hemiparesisweakening of

Balance therapy focuses on restoring WBA, sometimes guided by CoP biofeedback devices

Reduces WBA

Does not improve functional balance measures

A more appropriate approach may be to reduce asymmetries in lower limbs contributions to balance (e.g. torque modulation)

3

Research question

8/24/2016


4

Based on predictions by optimal control theory, can a robotic balance simulator evoke shifts of anterior-posterior balance contributions between limbs in healthy participants?

TO DO: INSERT

RISER VIDEO

Main ideas

robot to manipulate dynamics of standing balance

optimal adaptations of inter-limb balance coordination

AP := anterior-posterior (forward-backward)

ML := medial-lateral (left-right)

40; 2:20

this thesis examines two ideasthat can improve future post-stroke balance therapies,

Use of a robot to manipulate the dynamics of standing balance

Optimal Adaptations of inter-limb coordination

From these ideas, this thesis poses the question:

To motivate the research of these ideas

background on therapy robots and optimal human motor control

4

Background: therapy robots

8/24/2016


5

Howard et al. 2009

Dynamics-manipulated reaching

Balance robot

www.motekmedical.com

Huang et al. 2010

80; 3:40

Balance therapy robots, like the one shown here

train balance by shaking the support platform or encouraging weight shifting through games

Two shortcomings

perturbations and voluntary weight shifting involves more processing from cortical brain regions, which is not typically used in quiet balance control

These balance robots not apply motor learning principles

alternative robot-based approach is manipulating the dynamics of a motor task

Often used in reaching tasks to apply force fields

For both motor learning and post-stroke rehabilitation research

Extending this method to standing balance tasks can be used to research balance adaptations while promoting processing from the subcortical brain regions that primarily control quiet stance

5

Background: optimal human motor control

8/24/2016


6

Redundant actuation

Balance costs

centre of mass motion1

joint motion1

torque-change2

energy(muscle activity)3

Kuo, A. D. (1995). An optimal control model for analyzing human postural balance. IEEE Transactions on Bio-Medical Engineering, 42(1), 87-101.

Martin, L. et al. (2006). Optimization model predictions for postural coordination modes. Journal of Biomechanics, 39(1), 170-6.

Kiemel, T. et al. (2011). Identification of neural feedback for upright stance in humans: stabilization rather than sway minimization. The Journal of Neuroscience, 31(42), 15144-53.

70; 4:50

Optimal control: producing motion

Studies suggest that

Optimal control can solve a problem

How to select a singleankle-only AP balance has this problem

If the inter-limb coordination of balance

indirectly manipulating costs may

Optimal control: producing motion control signals that minimize performance costs

Studies suggest that balance may be minimizing either CoM motion, joint motion, torque-change or, most convincingly, muscle activity

Optimal control can solve a problem that arises from controlling motion using redundant actuators

How to select a single coordination pattern from the infinite patterns available

ankle-only AP balance has this problem

If inter-limb coordination of balance also adapts by minimizing performance costs

indirectly manipulating costs may induce changes in inter-limb balance coordination. (shifts of relative balance contribution, in particular)

6

outline


Study I

Study II


8/24/2016


7

0; 4:50

Now, Ill present the first human-participant study

Tests two hypotheses

7

Study I: hypotheses

8/24/2016


8

Van Asseldonk, E. H. F. et al. (2006). Disentangling the contribution of the paretic and non-paretic ankle to balance control in stroke patients. Experimental Neurology, 201(2), 441451.

Kiemel, T. et al. (2011). Identification of neural feedback for upright stance in humans: stabilization rather than sway minimization. The Journal of Neuroscience, 31(42), 1514453.

Basis

balance is optimal, minimizes energy2

distributions of weight and balance contributions have a one-to-one relationship1

Limbs anterior-posterior contribution to balance will shift toward a targeted limb if the limb is

Virtually strengthened in the anterior-posterior direction

Virtually weakened in the medial-lateral direction

AP := anterior-posterior (forward-backward)

ML := medial-lateral (left-right)

65; 5:55

first, main hypothesis

based on energy-minimizing adaptive balance control

is that virtually strengthening a targeted limb in the anterior-posterior direction will increase its relative torque contribution.

If the inter-limb control of balance is adaptive and minimizes energy, it would prefer using the strengthened limb.

The secondary hypothesis

Tests previous findings that the distributions of weight and balance contributions

have a one-to-one relationship

The hypothesis is that virtually weakening a targeted limb in the medial lateral direction will cause the shift in torque contribution.

Participants will likely shift their weight to the weakened limb to prevent falling

which would increase its relative contribution

8

Study I: methods

8/24/2016


9

Participants

10 healthy people (6 male, 4 female; 25.9 3.0 years)

Apparatus

85; 7:20

10 healthy people volunteered for this study

One participant had difficulty balancing with the robot so his data was excluded.

Rather than balancing their body, participants used a robotic platform to balance a real-time simulated inverted pendulum model of their bodies. The model represented ankle-only balance in the anterior-posterior and medial-lateral directions.

As participants balance on this robot, ankle torques were measured by force plates, scaled by torque gains, summed, and inputted to the model. The model includes body parameters based on each participant, mass, center of mass height from the ankles, and body inertia. The model outputted body angles, which were traced by the Stewart platform. Since the participant was strapped to the back-board fastened to the Stewart platform, the participant sensed the motions generated by the model, closing the feedback loop.

9

Study I: methods

Torque gain conditions

8/24/2016


10
NameML directionAP directionKtKntKtKntNormal1111ML-0.80.81.211ML-0.60.61.411AP-1.2111.20.8AP-1.4111.40.6

60; 8:20

virtually strengthen/weaken: non-unity torque gains

4 torque gains in total: each combination of direction and limb

To induce weight shifting,

Two sets of gains tested

Only ML gains altered

Targeted limb was weakened, the other strengthened

The two sets of gains differed in the amount of asymmetry

For examining optimal control, similar gains were used

Except AP gains altered

and the targeted limb was strengthened instead of weakened

10

Study I: methods

8/24/2016


11

Trial procedure

Balance measures
targeted limbs relative weighttargeted limbs relative balance contributionPre-adaptationLate AdaptationLate De-adaptation

60; 9:20

For each trial, participants stood using the robot as torque gains changed without their knowledge

Gains were initially normal during the baseline phase, changed to manipulated values for the adaptation phase, then back to normal for the de-adaptation phase

Data from the baseline phase and ends of the other two phases were analyzed and compared to examine shifts of weight and balance contribution

Relative weight was calculated as the proportion of the targeted limbs weight over the sum, averaged over time

Relative balance contribution was calculated as the proportion of the targeted limbs AP torque variance of the sum of the variances

11

Study I: ML gains, results

8/24/2016


12
ML-0.6, signalsML-0.8ML-0.6WeightPre-adaptationLate AdaptationLate De-adaptationtargeted limbs relative weightTorquePre-adaptationLate AdaptationLate De-adaptationtargeted limbs relative balance contribution
error bars := 1 SD

* := sig. diff.
targeted limbnon-targeted limb

60; 10:20

Results from manipulating ML gains show that these gains caused shifts of weight, as expected

The large gap between the weight signals during Late Adaptation compared to pre-adaptation suggests this

While the significant differences from the one-way repeated-measures ANOVAs and post-hoc paired t-tests for each condition verify this

As the gains became more asymmetric, the weight shifting increased.

From the torque data, a significant shift in balance contributions did not follow the shift in weight. This was contrary to the hypothesis.

12

Study I: ML gains, discussion

ML: weight shifts, torque does not

[weight:torque = 1:1] not observed

8/24/2016

13

reduced weight- bearing asymmetry

improved functional balance

reduced balance contribution asymmetry

1:1

W:T

?


60; 11:20

one-to-one relationship between distributions of weight and balance contribution did not hold

Difference in results: difference in voluntary and automatic balance control

when this relationship was originally observed,

participants were consciously controlling their weight distribution

But here, weight shifting was more automatic

The ineffectiveness of reducing weight bearing asymmetry at improving functional balance ability using CoP biofeedback devices may be explained by

the absence of the one-to-one relationship during quiet stance

If reducing asymmetries in the legs contributions to balance truly is important for improving functional balance

13

Study I: AP gains, results

8/24/2016


14
AP-1.4, signalsAP-1.2AP-1.4WeightPre-adaptationLate AdaptationLate De-adaptationtargeted limbs relative weightTorquePre-adaptationLate AdaptationLate De-adaptationtargeted limbs relative balance contributiontargetednon-targeted
error bars := 1 SD

* := sig. diff.

25; 11:45

When anterior-posterior gains were manipulated, there was no shifting in weight, but this was expected.

However, there was again no significant shift in relative balance contribution, contrary to the main hypothesis. Balance did not exhibit optimal adaptive behaviour

14

Study I: AP gains, discussion

AP: no torque shift

inter-limb coordination: appears habitual (similar to 1,2)

potential cause: choice of torque gains(average gain = 1)

8/24/2016

15

Kistemaker, D. A. et al. (2010). The central nervous system does not minimize energy cost in arm movements. Journal of Neurophysiology, 104, 29852994.

De Rugy, A. et al. (2012). Muscle Coordination Is Habitual Rather than Optimal. Journal of Neuroscience, 32(21), 73847391.


70; 12:55

result support an alternate hypothesis:

that inter-limb balance coordination is habitual rather than optimally adaptive.

This idea come from previous motor adaptation studies,

where participants practiced a novel task and preferred habitual coordination patterns over optimal ones

Null result may have been caused by the choice of torque gains

Because the average gain was always 1, the sum of the non-scaled torques did not differ much from the sum of the scaled torques

Employing a normal balance strategy was sufficient. There was no need to adapt.

Using asymmetrical torque gains with an average less than 1, such as 2 and -1, would substantially change the summed torques and force participants to learn a new balance strategy.

15

Outline

Motivation, Research Question and Background

Study I

Study II


8/24/2016


16

0

The second study addresses this problem and uses this approach

16

Study II: hypothesis

8/24/2016


17

Based on predictions by optimal control theory, virtually strengthening a targeted limb and virtually reversing the other limb in the anterior-posterior direction will increase the targeted limbs relative contribution to anterior posterior standing balance.

30; 13:25

The study hypothesizes that

17

Study II: methods

8/24/2016


18

Participants

10 different healthy people

(6 female, 4 male; 20.9 2.1 years)

Apparatus

Same robot, but AP motion only

Protocol

AP torque gains: {2, -1}

Combination of two distinct strategies may arise

stiffening strategy: habitual inter-limb coordination

shifting strategy: adaptive coordination

80; 14:45

10 participants volunteered for the study

balanced using the same robotic as the first study, but the robot moved only in the anterior-posterior direction

Only one set of manipulated gains was tested: 2 and -1

Because the average gain was 0.5, the overall torque contributions is reduced to half

A normal strategy would not produce enough torque to remain upright.

Participants may respond with a combination of two strategies

Either a stiffening strategy that relies on increasing contribution of both limbs and suggests that balance coordination is habitual

or a shifting strategy as hypothesized and suggests that balance coordination is adaptive

18

Study II: methods, protocol

8/24/2016


19

70: 15:55

Protocol schedule here has several similarities to the previous one

There are three phases that alternate between normal and manipulated torque gains, and data is analyzed during the baseline phase and at the end of the other two phases

Main difference: a single set of altered torque gains were examined over two days, 24 hours between sessions

Each day had baseline, adaptation and de-adaptation phases

Adaptation time: from 5 minutes in one day to 48 minutes over two days

Each Day was split into multiple trials to accommodate the increased length and to reduce fatigue

Day 2 had a shorter adaptation phase because a faster rate of adaptation was expected.

for each analysis period, Two measures of relative balance contributions were calculated

Quiet Balance Contribution during unperturbed stance, and Dynamic Balance Contribution during unperturbed stance

19

Study II: methods, balance measures

8/24/2016


20
Dynamic Balance Contribution(van Asseldonk et al. 2006)Quiet Balance ContributionMay involve compensatory control mechanisms(Normal) quiet balance controlStabilizing mechanismsStabilizing and de-stabilizing mechanisms
: = targeted limbs balance controller

: = non-targeted limbs balance controller

:= scalar projection of on to

:= number of perturbation frequencies

:= number of 10-second periods

105; 17:40

Dynamic Balance Contribution

Based on frequency response estimates of both limbs balance controllers

And averaging the targeted limbs relative contribution across frequencies

Because these controller estimates are based on participants responses to a body angle perturbation (light shaking)

Dynamic Balance Contribution has the advantage that it

mostly measures stabilizing torques produced by the neural controller in response to the perturbation

destabilizing torques due to sensory or motor noise have reduced effect on this measure

Disadvantage

The perturbation responses may be affected by compensatory control mechanisms

It may not represent quiet balance control

Quiet Balance Contribution

Has the opposite advantages and disadvantages

Balance is unperturbed when this measures is calculated: reduced involvement of compensatory control mechanism

While increasing the effect of sensory and motor noise

proportion of the targeted limb mean-removed rms torque over the sum

20

Study II: results

8/24/2016


21
angle ()torque (Nm)angle ()torque (Nm)Pre-adaptationEarly AdaptationLate AdaptationLate De-adaptation
Day 1

Day 2
bodytargeted limbnon-targeted limb

55; 18:35

These body angle and torque signals, show how participants generally responded to the protocol

Both signals, and both days top and bottom,

Variabilities were low during pre-adaptation

Increased substantially during early adaptation

Decreased by late adaptation

And lastly, they returned to baseline levels by late-deadaptation

Zooming into the torque signals to examine the variabilities of each limb

Grey targeted limb variabilities during Late Adaptation were noticeably greater than the non-targeted limb, suggesting that relative balance contributions did shift

21

Study II: results

8/24/2016


22
**: p < 0.01***: p < 0.001
error bars := 1 SD

80; 19:55

Two-way repeated measures ANOVAs and post-hoc paired t-tests confirm this shifting pattern

Both balance contribution measures significantly increased from Pre-adaptation to Late Adaptation

inter-limb coordination can adapt to manipulated torque gains and is not always habitual

Balance contributions remained significantly different during Late De-adaptation compared to baseline

participants did not de-adapt within the allotted time

Overall, Balance contributions were significantly greater on Day 2 than Day 1

A shifted strategy was learned on day 1 and carried over to Day 2. Some motor learning had occurred.

Over two days, relative balance contributions shifted by 17-20 percent

22

Study II: discussion

important protocol changes

decreasing overall torque contribution (average gain)

increasing gain asymmetry (left-right gain difference)

helps associate shifted balance with increased performance

optimizing adaptations

primary cost to minimize: uncertain

8/24/2016


23

90; 21:25

Two protocol changes were important for producing these adaptations

decreasing the overall torque contribution to force the adoption of a new strategy, as motivated from the first study lack of shifting

Increasing the gain asymmetry

Because body angle alone cannot convey the performance efficiency of each limb

High gain asymmetry was important for helping the neural balance controller detect the association between shifted balance and increased performance

The optimally adaptive nature of balance control is suggested by the decreases in both sway and torque activity from the beginning of the adaptation phases to the end of the adaptation phases

However, these decreases do not lead to a conclusion on whether the balance controller prefers to minimize sway or energy. The optimizing objective of balance remains uncertain.

23

Outline


Study I

Study II


8/24/2016


24

0

This brings us to the final part of the presentation

24

Summary

motivation:

aid post-stroke balance therapy design

dynamics-manipulating balance robot

optimal adaptive balance

Study I:

induced weight shifting =/> shifted balance

AP torque gains =/> shifted balance

Study II:

AP torque gains required a stiffening or shifting strategy

significant shifts in balance contributions

8/24/2016


25

A robotic balance simulator indeed can evoke shifts of anterior posterior balance contributions between limbs in healthy participants, as suggested by optimal control theory.

90; 22:55

To summarize the presentation so far

Two ideas were examined to aid the design of post-stroke balance therapy,

this thesis examined the use of a dynamics-manipulating robot

optimal adaptations during balance control

initial study,

Altering medial lateral torque gains induced weight shifting but did not lead to shifted balance contributions

Altering anterior posterior torque gains also did not produce shifted balance,

Likely because a normal balance strategy was sufficient.

In the follow-up study

anterior-posterior torque gains that required a stiffening or shifting strategy were used

Balance contributions shifted within the same day and its effects carried over to the next day.

In conclusion

25

Contributions

evidence that inter-limb balance coordination can adapt, in accordance to optimal control theory

novel technique of independently manipulating each legs torque contribution to simulated balance

8/24/2016


26

50; 23:45

Two major contributions

First

Existing studies show reaching may be optimal adaptive, and balance control may be energy-minimizing

Second...

Some robots manipulated the dynamics of standing balance, but no other robots independently manipulate the torque contribution of each limb to balance

26

Future work

post-stroke balance therapy

relative balance contributions vs. functional balance

use highly asymmetric torque gains, without reducing the overall torque contribution to balance

8/24/2016


27

65; 24:50

natural next step

investigate the use of anterior-posterior torque gains for post-stroke balance therapy.

However, the relationship between relative balance contributions and functional balance should be investigated

Prior to or in tandem with initially using this protocol with stroke survivors

Lastly, Whether highly asymmetric gains, alone, are sufficient for producing shifts of balance contribution should be tested.

If reducing the overall torque contribution is not necessary, then adapting to manipulated torque gains will be less tiring and less discomforting

27

Thank you!

8/24/2016


28

Howard, I. S. et al. (2009). A modular planar robotic manipulandum with end-point torque control. Journal of Neuroscience Methods, 181(2), 199-211.

Huang, H. J. et al. (2012). Reduction of Metabolic Cost during Motor Learning of Arm Reaching Dynamics. Journal of Neuroscience, 32(6), 2182-2190.

28

Study I: results, ML gains

8/24/2016


29

Torque variance shift

Weight shift

Pre-adaptation to Late Adaptation
0.20-0.200.10.2ML-0.8ML-0.6

Upon further examining the participants data, everyone was found to shift their weight, but not everyone shifted their torque contributions in the same direction. More people shifted their torque toward the targeted limb when gains were more asymmetric.

29

Study II: methods, joint-input output

8/24/2016


30

Study II: methods, balance measures

8/24/2016


31
Dynamic Balance Contribution(van Asseldonk et al. 2006)Quiet Balance ContributionMay involve compensatory control mechanisms(Normal) quiet balance controlStabilizing mechanismsStabilizing and de-stabilizing mechanisms

105; 17:40

Dynamic Balance Contribution

Based on frequency response estimates of both limbs balance controllers

And averaging the targeted limbs relative contribution across frequencies

Because these controller estimates are based on participants responses to a body angle perturbation (light shaking)

Dynamic Balance Contribution has the advantage that it

mostly measures stabilizing torques produced by the neural controller in response to the perturbation

destabilizing torques due to sensory or motor noise have reduced effect on this measure

Disadvantage

The perturbation responses may be affected by compensatory control mechanisms

It may not represent quiet balance control

Quiet Balance Contribution

Has the opposite advantages and disadvantages

Balance is unperturbed when this measures is calculated: reduced involvement of compensatory control mechanism

While increasing the effect of sensory and motor noise

proportion of the targeted limb mean-removed rms torque over the sum

31

Study II: results

8/24/2016


32

time (s)

rms mgh-normalized torque

These plots show how sway and torque activity decreased over the course of the adaptations phases of Day 1 and Day 2

The fitted exponential curves are derived from data averaged across participants

Averaged data are based on calculations of mean-removed rms body angle and torques over 10-second periods

The similar activity levels at the end of Day 1 and start of Day 2 also suggest that adapted behaviour carried over to the next day

32

Study II: results

8/24/2016


33

time (s)

slowly learned shifted balance strategy

two-rate adaptation model1: slow adaptation processes with good retention

Joiner, W. M., & Smith, M. A. (2008). Long-term retention explained by a model of short-term learning in the adaptive control of reaching. Journal of Neurophysiology, 100(5), 29482955.

25; 20:20

Here, I want to quickly show how the targeted limbs relative balance contribution slowly increased over the adaptation phases of both days

To create these curves, quiet Balance Contribution was essentially calculated over 10 second periods, then averaged across participants and fitted to exponential curves

Slow changes and good retention of shifted balance contributions

Agree with Joiner and Smiths two-rate adaptation model

They propose that motor adaptation involves a fast adaptation process with poor retention and a slow adaptation process with good retention

In this study, the adaptations agree with the slow process

In split-belt treadmill studes, people adapt within 15-20 stride cycles, which agree with the fast process

33

Study II: results, co-contraction

8/24/2016


34
150 % rms EMG.100 msSoleus onlyTibialis anterior onlyCo-contraction
{0.83, 0.82}

{0.53, 0.48}

{0.52, 0.53}

{0.52, 0.53}

{CIt, CInt}:

CI := co-contraction index

A similar procedure was applied to the targeted limbs relative mean-removed rms torque proportion, similar to how Quiet Balance Contribution was calculated but over a smaller period. The fitted curves show the gradual shifting of relative balance contributions.

In order to examine varying levels of co-contraction, surface electromyography was used to measure muscle activity of the soleus and tibialis anterior muscles of the first four participants on Day 1. After rectifying the signals and normalizing them to the mean-removed rms during Pre-adaptation, co-contraction indices were calculated. Visually, the co-contraction index could be considered the overlap of both signals. Co-contraction levels were similar in both limbs. Only during the beginning of the adaptation phase was there large increase in co-contraction compared to baseline, suggesting that participants used a stiffening strategy at first.

34

Study I: discussion

ML: weight shifts, torque inconsistently shifts

[weight:torque = 1:1] not observed

AP: no torque shift

Inter-limb coordination: appears habitual

Potential cause: choice of torque gains (average gain = 1)

8/24/2016


35

reduced weight- bearing asymmetry

improved functional balance

reduced balance contribution asymmetry

1:1

W:T

?

Results did not agree with hypotheses

ML trials: significant weight shifting, but not a significant shift of relative balance contribution

one-to-one relationship between distributions of weight and balance contribution did not hold

when this relationship was originally observed,

participants were consciously controlling their weight distribution

But here, weight shifting was more automatic

The ineffectiveness of reducing weight bearing asymmetry at improving functional balance ability using CoP biofeedback devices may be explained by

the absence of the one-to-one relationship during quiet stance

If reducing asymmetries in the legs contributions to balance truly is important for improving functional balance

AP trials: there was also no significant shift in relative torque contributions

Balance did not exhibit optimal adaptive behaviour

results support an alternate hypothesis:

that inter-limb balance coordination is habitual rather than optimally adaptive.

This idea come from previous motor adaptation studies

In these studies, optimal coordination patterns were available to participants, but participants did not adopt them.

Null result may have been caused by the choice of torque gains

Because the average gain was always 1, the sum of the non-scaled torques did not differ much from the sum of the scaled torques, especially in symmetrically balancing participants

Employing a normal balance strategy was sufficient. There was no need to adapt.

Using asymmetrical torque gains with an average less than 1, such as 2 and -1, would cause the summed torques to change substantially and force participants to learn a new balance strategy.

35

Lavf54.63.104

TleftTright

ParticipantForce platesKtReal-time inverted pendulum simulationStewart and ankle-pitch platforms Knt TntTntTtTtmhDigitalActual+t := targeted limbnt := non-targeted limbbold := vector w/ AP & ML

803008030080BaselineAdaptationDe-adaptationControl-1Post-adaptationControl-2time (s)NormalManipulatedTrial phasesData analysis periods

Limb torques[1 1]3 Nm2 sSummed torques

Limb torques[1.4 0.6][1 1]3 Nm2 sSummed torques

[2 -1]Limb torquesSummed torques[1.4 0.6][1 1]3 Nm2 s

480 s240 s5 to 7 min rest480 s480 s240 sbaselineadaptationde-adaptationPre-adaptationLate AdaptationLate De-adaptationnormalmanipulatedtorque gainstrial phasesdata analysis periodsA. Day 15 to 7 min rest5 to 7 min rest480 s240 s5 to 7 min rest480 s240 sbaselineadaptationde-adaptationPre-adaptationLate AdaptationLate De-adaptationnormalmanipulatedtorque gainstrial phasesdata analysis periodsB. Day 2, after 24 h 5 to 7 min restUnperturbed, QBCPerturbed, DBCUnperturbed200 s100 s100 s100 s100 s100 s100 s

480 s240 s5 to 7 min rest480 s480 s240 sbaselineadaptationde-adaptationPre-adaptationLate AdaptationLate De-adaptationnormalmanipulatedtorque gainstrial phasesdata analysis periodsA. Day 15 to 7 min rest5 to 7 min rest480 s240 s5 to 7 min rest480 s240 sbaselineadaptationde-adaptationPre-adaptationLate AdaptationLate De-adaptationnormalmanipulatedtorque gainstrial phasesdata analysis periodsB. Day 2, after 24 h5 to 7 min restUnperturbed, QBCPerturbed, DBCUnperturbed200 s20 s20 s20 s20 s100 s100 s100 s100 s100 s100 s

Trms,tTrms,ntTntTtNms

A

Day 1 Day 2

B

C

time (s)

150 % rms EMG.

100 ms

Soleus only Tibialis anterior only Co-contraction

norma

lized

EMG

Pre-adaptation Early Adaptation Late Adaptation Late De-adaptation

Documents

PW_thesis_defense