![](bg2.png)
Figure 1: a) upright stationary posture b) elbow flexion-
extension.
The EMG signal when both forearms are in upright
posture (Figure 1.a) is shown in Figure 2. In Figure
3, however, we find the same muscles’ EMG when
left arm remains upright stationary but the right
forearm moves according to Figure 1.b.
5 10 15 20 25
0.04
0.05
0.06
0.07
0.08
0.09
Time
sec
EMG (volt)
Bicepce-Left
Tricepce-Left
Bicepce-Right
Tricepce-Right
Figure 2: EMG of right and left arm when both arms are
upright stationary.
0 5 10 15 20 25 30 35 40 45
0.05
0.1
0.15
0.2
Time (sec)
EMG (volt)
Becepce-Left
Tricepce-Left
Bicepce-Right
Tricepce-Right
Figure 3: EMG when left arm remains upright stationary
and right arm moves.
Comparing the two situations we can observe that
the EMG in the stationary limb (left arm) is
considerably affected by that of the moving limb
moreover biceps and triceps of the stationary arm
are co-activated with almost the same amount.
3 HYPOTHESIZING
It can be postulated that the source of the co-
activation of the muscles in the stationary limb is to
feel secure about the performance of the moving
limb. If the stationary limb remains more stable
against possible perturbations, in case of
perturbation less correction and hence computation
would be needed. Then the task which is intended to
be done by the opposite limb is performed with more
comfort and concentration. In a word, we spend
more energy to fire the muscles of a stationary limb
so as to avoid excessive computing.
4 APPLICATION
Stroke patients mostly suffer from hemiplegia; they
lose some of the motor neurons with their associated
information that leave them with one side affected
and one side intact. Recovery rate has been reported
significant when a stroke patient move the healthy
limb and a robot imitating the motion apply the same
pattern to the affected limb (Burgar et al, 2000),
(Luft et al, 2004), and (Hesse et al, 2003). The
reason why this accelerates the recovery is not clear
yet. However, our finding might help address this
question.
We observed that when one moves a limb, the CNS
also sends some signals to the other limb even if it is
in a static posture. The signal might not be as
powerful to move it or more probably the signal
might not meant to move it; instead it could be to
make sure that the resting limb is going to stay in the
static posture.
Now let’s imagine that every time the stroke
subject’s arm is driven by the robot there have been
some signals to fire the muscles already. That can be
the reason why a stroke patient’s recovery process is
faster when they move undergo mirror image
movement enabler system.
ACKNOWLEDGEMENTS
Hereby we would like to acknowledge the School of
Mechanical and Aerospace Engineering at Nanyang
Technological University and the M&C Lab in
especial.
REFERENCES
Burdet E, Osu R, Franklin DW, Milner TE, Kawato M.,
2001. The CNS Skillfully Stabilizes Unstable
Dynamics by Learning Optimal Impedance. Nature,
414: 446-9.
Burgar C G , Lum PS, Shor PC, Machiel Van der Loos
HF., 2000. Development of robots for rehabilitation
therapy: The Palo Alto VA/Stanford experience. J
Rehabil Res Dev. 37(6):663-73.
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