06-07-2012, 12:54 PM
Determination of the Arm Orientation for Brain-Machine Interface Prosthetics
[attachment=26965]
INTRODUCTION
A significant cause of worldwide morbidity and
mortality is disease or injury which causes loss of the
ability to actuate limbs. Conditions which fall into this
category include spinal cord injury, anterior lateral
sclerosis (ALS, Lou Gehrig’s disease), and
cerebellomedular disconnection (locked-in syndrome).
These conditions cause varying degrees of dyskinesia,
ranging from a retardation of normal movement, to
quadriplegia, to the complete inability to exercise
voluntary muscular control. In addition, conditions such
as limb injury and myopathy can cause dysfunction of
the extremities themselves. Severe diseases of both of
these types can render a fully conscious patient unable to
interact with his or her environment normally. In the
case of the more severe neuromuscular disorders,
patients often cannot live independently and rely on
others for assistance with many basic daily activities.
These diseases also lead to the death of patients,
especially for those less able to afford satisfactory health
care and assistance.
BRAIN-MACHINE INTERFACE WITH CARTESIAN (EXTRINSIC) COORDINATE FRAMES
One potential modality of more sophisticated neural
control could employ a direct interface to the motor
cortex to provide control signals for prosthetic devices.
Some of the initial work in this area was pioneered by
Georgopoulos et al, who discovered that population of
neurons encode movement directions [20]. Since then,
groups led by, for example, Schwartz, Donoghue, and
Nicolelis, have continued progress in this area by
developing more sophisticated recording techniques and
signal processing algorithms. Common signal
processing schemas for these control systems employ
discrete classifiers, linear filters, and complex neural
networks to develop reasonable estimates of the hand
motion [3], [7], [8], [9].
BRAIN-MACHINE INTERFACE WITH KINEMATIC (INTRINSIC) COORDINATE FRAMES
Because of the limitations of the Cartesian-frame
system described, it is desirable to establish a robotic
neuroprosthetic control system that operates through the
7-DOF kinematic (intrinsic) frames. This kinematic
coordinate frame approach is more likely to result in
reconstructing the precise position and motion dynamics
of each robotic link as in the original appendage than the
Cartesian coordinate approach pursued.
APPROACHES TO BRAIN-CONTROLLED PROSTHETICS
It has been shown that the activation levels of the
neurons that are correlated with arm movement change
when the transition is made from controlling a cursor
with real arm movements to no arm movements [12].
This suggests that while a neuromotor model based on
correlations between cortical activation and actual arm
movement are useful for training, the control model of a
neuroprosthetic may not need to be based on the
predictive value of a model to the movement of an actual
limb. In other words, the cortex may be able to control
joint-angle parameters of a 7-DOF device by employing
a model without necessary direct correlation to the 7
DOF control of an actual subject arm.
PRELIMINARY RESULTS AND DISCUSSION
Using the Vicon™ tracking data, primary
component analysis was conducted to isolate individual
joint axis motion from overall rotation matrices of each
joint. To produce joint angle combination graphs
analogous to Figs. 4 and 5, the inverse kinematic search
algorithm was performed on the location of one of the
experimental grips. Fig. 6 shows computed allowable
joint angle configurations for this location for arm and
elbow flexion angles. Some of the computed joint
angles in Fig. 6 are outside of the physiological range of
the joints: although the kinematic search algorithm is
seeded with physiologic boundaries on joint angle
extents, the solutions it produces are sometimes outside
of those extents.
[attachment=26965]
INTRODUCTION
A significant cause of worldwide morbidity and
mortality is disease or injury which causes loss of the
ability to actuate limbs. Conditions which fall into this
category include spinal cord injury, anterior lateral
sclerosis (ALS, Lou Gehrig’s disease), and
cerebellomedular disconnection (locked-in syndrome).
These conditions cause varying degrees of dyskinesia,
ranging from a retardation of normal movement, to
quadriplegia, to the complete inability to exercise
voluntary muscular control. In addition, conditions such
as limb injury and myopathy can cause dysfunction of
the extremities themselves. Severe diseases of both of
these types can render a fully conscious patient unable to
interact with his or her environment normally. In the
case of the more severe neuromuscular disorders,
patients often cannot live independently and rely on
others for assistance with many basic daily activities.
These diseases also lead to the death of patients,
especially for those less able to afford satisfactory health
care and assistance.
BRAIN-MACHINE INTERFACE WITH CARTESIAN (EXTRINSIC) COORDINATE FRAMES
One potential modality of more sophisticated neural
control could employ a direct interface to the motor
cortex to provide control signals for prosthetic devices.
Some of the initial work in this area was pioneered by
Georgopoulos et al, who discovered that population of
neurons encode movement directions [20]. Since then,
groups led by, for example, Schwartz, Donoghue, and
Nicolelis, have continued progress in this area by
developing more sophisticated recording techniques and
signal processing algorithms. Common signal
processing schemas for these control systems employ
discrete classifiers, linear filters, and complex neural
networks to develop reasonable estimates of the hand
motion [3], [7], [8], [9].
BRAIN-MACHINE INTERFACE WITH KINEMATIC (INTRINSIC) COORDINATE FRAMES
Because of the limitations of the Cartesian-frame
system described, it is desirable to establish a robotic
neuroprosthetic control system that operates through the
7-DOF kinematic (intrinsic) frames. This kinematic
coordinate frame approach is more likely to result in
reconstructing the precise position and motion dynamics
of each robotic link as in the original appendage than the
Cartesian coordinate approach pursued.
APPROACHES TO BRAIN-CONTROLLED PROSTHETICS
It has been shown that the activation levels of the
neurons that are correlated with arm movement change
when the transition is made from controlling a cursor
with real arm movements to no arm movements [12].
This suggests that while a neuromotor model based on
correlations between cortical activation and actual arm
movement are useful for training, the control model of a
neuroprosthetic may not need to be based on the
predictive value of a model to the movement of an actual
limb. In other words, the cortex may be able to control
joint-angle parameters of a 7-DOF device by employing
a model without necessary direct correlation to the 7
DOF control of an actual subject arm.
PRELIMINARY RESULTS AND DISCUSSION
Using the Vicon™ tracking data, primary
component analysis was conducted to isolate individual
joint axis motion from overall rotation matrices of each
joint. To produce joint angle combination graphs
analogous to Figs. 4 and 5, the inverse kinematic search
algorithm was performed on the location of one of the
experimental grips. Fig. 6 shows computed allowable
joint angle configurations for this location for arm and
elbow flexion angles. Some of the computed joint
angles in Fig. 6 are outside of the physiological range of
the joints: although the kinematic search algorithm is
seeded with physiologic boundaries on joint angle
extents, the solutions it produces are sometimes outside
of those extents.