Ask your doctor how this can be used in your stroke protocol. You do have a stroke protocol? Don't you?
https://digital.lib.washington.edu/researchworks/handle/1773/22046
In this dissertation control algorithms are developed and tested for the
EXO-UL7 towards a control strategy for stroke rehabilitation. EXO-UL7
is a seven degree of freedom (Dof) powered upper limb exoskeleton that
was initially designed at the University of Washington and further
refined at the University of California Santa Cruz. Admittance control,
swivel angle prediction and neural control of the device have been
implemented and subjects tested performance of the device and control
strategy. After an initial summery of the state of the art and details
of the existing system. Each control strategy and performance from
testing is explored. Admittance control uses force sensors on EXO-UL7 to
control the movement of the device by moving in the direction that that
user pressed on the device. Because EXO-UL7 is a redundant manipulator
and supports the entire configuration of the arm, a single force sensor
on the device end effector is not enough to fully define the movement.
Additional force sensors on the device that are located at each
attachment point of the machine interface allows for the full
configuration of the device to be specified. This turns the under
defined problem into an over defined problem (to many force signals for
the number of Dof). Two strategies are developed to project the signals
onto a seven degree subspace. The first adds the force vectors in task
space then uses the Inverse kinematic to develop joint trajectories.
The second uses the Jacoban transpose to map the forces first to
instantaneous joint torques then the torques are added in joint space
and finally joint trajectories are developed from joint torques. Six
subjects performed a peg in hole experiment and it was found that task
based admittance control had about 11\% lower interaction energy
required to do the task. It was also shown that with both admittance
controlers the subjects did the tasks slower then with no Control at
all. Kinematic and dynamic constraints requced the bandwith of the
system. To improve the bandwith, Predictive algorithms are employed.
Swivel angle prediction is the first predictive algorithm implements to
improve the performance of the device. With this control strategy the
configuration of the redundant space is related to the end effector
position. by observing human behavior it was noted that one of the
fundamental task preformed by the human arm was to bring food to the
mouth. By maximizing the manipulability of the device in the direction
of the mouth, a simple stable closed form prediction of the
configuration of the device was achived. Comparing movement from motion
capture to predicted motion showed a good correlation and testing of
the algorithm on the exoskeleton device was conducted. 4 Subjects
conducted a peg in hole task. An 11.22\% reduction of interaction
energy was achieved when compared to Admittance control alone. This
algorthm can be used for motion folowwing as in teh current set up, or
to predict where what the arm configuration should be whne workingwith
disabled populations. Although this method predicted motion very well,
it only provided prodiction of the one Dof redundant space of the arm.
To further extend the prediction capability and motion following of the
device, neural control was implemented in which electro myography (EMG)
is used to read the nerve impulsed to the muscle. Although this signal
only relates muscle force to isometric muscle contraction, using other
system parameters read from Exo-UL7 such as the joint, position and
velocity, A Hill based muscle model predicts the muscle force and
ultimately the muscle torque. Due to an inherent delay between when the
nerve impulse can first be detected and when the muscle contracts (some
where o the order of 50-100 ms) the motion can be predicted before the
arm begins to move. The model has many variable that need to be
predicted for each individual so before each subject test an parameter
fitting is conducted. Four subjects preformed a peg in hole test. It
was shown that the interaction power increased compared to admittance
control, but the completion time decreased. With further examination in
was noted that the interaction force and energy when using the neural
control was the same as with the admittance control. This implies that
with the same force we achieved a higher velocity, which means that the
system had a higher overall gain. The performance gains were not
uniform through out all the subjects. The parameter fit conducted for
each subject did not guarantee convergence to even a local minimum and
there is still opportunities to improve the system performance.
Admittance control did a good job of motion following and neural and
swivel prediction improved upon this control scheme. There is still
further work to be done on the system. Currently using the systems that
were build in this dissertation, a clinical trial for stroke
rehabilitation is under way at the university of California San
Fransisco.
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