These studies focus on the relationship between the mechanical behavior of the limb, or the jaw in the case of speech, and patterns of limb movement and learning.
Laboissiere
R, Lametti DR, Ostry DJ (2009) Impedance control
and its
relation to precision in
orofacial movement. J Neurophysiol
102:523-531.
Abstract PDF
Speech production involves some of the most precise
and finely timed patterns of human movement. Here, in the context of
jaw movement in speech, we show that spatial precision in speech
production is systematically associated with the regulation of
impedance and in particular, with jaw stiffness—a measure of resistance
to displacement. We estimated stiffness and also variability during
movement using a robotic device to apply brief force pulses to the jaw.
Estimates of stiffness were obtained using the perturbed position and
force trajectory and an estimate of what the trajectory would be in the
absence of load. We estimated this “reference trajectory” using a new
technique based on Fourier analysis. A moving-average (MA) procedure
was used to estimate stiffness by modeling restoring force as the
moving average of previous jaw displacements. The stiffness matrix was
obtained from the steady state of the MA model. We applied this
technique to data from 31 subjects whose jaw movements were perturbed
during speech utterances and kinematically matched nonspeech movements.
We observed systematic differences in stiffness over the course of
jaw-lowering and jaw-raising movements that were correlated with
measures of kinematic variability. Jaw stiffness was high and
variability was low early and late in the movement when the jaw was
elevated. Stiffness was low and variability was high in the middle of
movement when the jaw was lowered. Similar patterns were observed for
speech and nonspeech conditions. The systematic relationship between
stiffness and variability points to the idea that stiffness regulation
is integral to the control of orofacial movement variability.
Darainy
M, Mattar AAG, Ostry DJ
DJ (2009) Effects of human arm impedance on dynamics
learning and generalization. J Neurophysiol 101:3158–3168.
Abstract PDF
Previous studies have demonstrated anisotropic
patterns of hand impedance under static conditions and during movement.
Here we show that the pattern of kinematic error observed in studies of
dynamics learning is associated with this anisotropic impedance
pattern. We also show that the magnitude of kinematic error associated
with this anisotropy dictates the amount of motor learning and,
consequently, the extent to which dynamics learning generalizes.
Subjects were trained to reach to visual targets while holding a
robotic device that applied forces during movement. On infrequent
trials, the load was removed and the resulting kinematic error was
measured. We found a strong correlation between the pattern of
kinematic error and the anisotropic pattern of hand stiffness. In a
second experiment subjects were trained under force-field conditions to
move in two directions: one in which the dynamic perturbation was in
the direction of maximum arm impedance and the associated kinematic
error was low and another in which the perturbation was in the
direction of low impedance where kinematic error was high.
Generalization of learning was assessed in a reference direction that
lay intermediate to the two training directions. We found that transfer
of learning was greater when training occurred in the direction
associated with the larger kinematic error. This suggests that the
anisotropic patterns of impedance and kinematic error determine the
magnitude of dynamics learning and the extent to which it generalizes.
Lametti
DR, Houle G, Ostry DJ (2007) Control of
movement variability and the regulation of limb impedance. J
Neurophysiol 98:3516-3524.
Abstract PDF
Humans routinely make movements to targets that have different accuracy
requirements in different directions. Examples extend from everyday
occurrences such as grasping the handle of a coffee cup to the more
refined instance of a surgeon positioning a scalpel. The attainment
of accuracy in situations such as these might be related to the
nervous system's capacity to regulate the limb's resistance to
displacement, or impedance. To test this idea, subjects made
movements from random starting locations to targets that had
shape-dependent accuracy requirements. We used a robotic device to
assess both limb impedance and patterns of movement variability just
as the subject reached the target. We show that impedance increases
in directions where required accuracy is high. Independent of target
shape, patterns of limb stiffness are seen
to predict spatial patterns of movement variability. The nervous
system is thus seen to modulate limb impedance in entirely
predictable environments to aid in the attainment of reaching
accuracy.
Darainy M, Malfait N,
Gribble PL, Towhidkhah F, Ostry
DJ (2004) Learning to control arm stiffness under static
conditions. J Neurophysiol 92:3344-3350.
Abstract PDF
We used a robotic device to test the idea that impedance control involves
a process of learning or adaptation that is acquired over time and permits
the voluntary control of the pattern of stiffness at the hand. The tests
were conducted in statics. Subjects were trained over the course of three
successive days to resist the effects of one of three different kinds
of mechanical loads, single axis loads acting in the lateral direction,
single axis loads acting in the forward/backward direction and isotropic
loads that perturbed the limb in eight directions about a circle. We found
that subjects in contact with single axis loads voluntarily modified their
hand stiffness orientation such that changes to the direction of maximum
stiffness mirrored the direction of applied load. In the case of isotropic
loads, a uniform increase in endpoint stiffness was observed. Using a
physiologically realistic model of two-joint arm movement, the experimentally
determined pattern of impedance change could be replicated by assuming
that coactivation of elbow and double joint muscles was independent of
coactivation of muscles at the shoulder. Moreover, using this pattern
of coactivation control we were able to replicate an asymmetric pattern
of rotation of the stiffness ellipse that was observed empirically. The
present findings are consistent with the idea that arm stiffness is controlled
through the use of at least two independent cocontraction commands.