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									    FES Control
Strategies & Challenges

    Presented by: Rahele Shafaei
      Professor: Dr.Towhidkhah
Table of contents
♠ FES Control
♠ an overview of some of the Existing
  Strategies for Closed-loop Control
♠ Examples of Closed-Loop FES Controllers for
  Regulating Knee Angle
♠ EMG Closed-Loop FES Control

Functional Electrical Stimulation

• FES uses short electrical pulses to generate
  contractions in paralyzed muscles that exert
  torques about the joint.

biphasic waveform: F=20–40 Hz, A= 0–120 mA,PD=0–300 μs.

• The neuron receives the series of pulses that are
  delivered using electrodes:

    transcutaneous (placed on the skin surface)
    percutaneous (placed within a muscle)
    epimysial (placed on the surface of the muscle)
    cuff (wrapped around the nerve that innervates
     the muscle of interest)
    Bion

• FES is the most
  commonly used                        Push Button

  technique for improving
  motor function in SCI
    Grasping
    Reaching               Neuroprosthesis

    Standing
    Stationary rowing
    Cycling









         (drop foot)   

Differences between the production of tension in
neurologically intact and SCI individuals

Synchronous motor-unit stimulation
 Nonphysiological recruitment


FES Control
Control parameters
• Joint angle/torque can be controlled by
  modulating the intensity of stimulation
  delivered to the flexor and extensor muscles,
  which actuate the joint in opposite directions.

    Frequency
    Amplitude
    Pulse width


• Spasticity
• Hyperactive feedback loops in the CNS

 CPGs and other spinal reflexes affect closed-loop FES control
  because these phenomena effectively act as exogenous control
  signals sent to the paralyzed muscles in parallel with the FES
  control signal. Since these exogenous control signals can
  disrupt the desired joint movement, the FES control system
  must compensate for these unintended control signals.

Control in clinical FES systems

• Open-loop
    Require continuous or repeated user input, which means
     that the user must devote his or her full attention to operating
     the FES device.

• Finite state (closed-loop)
    Execute a preset stimulation sequence in an open-loop
     fashion when a specific condition is met.
    Example:gait of stroke patients who struggle with drop foot
    Typically do not correct for model errors or disturbances.

• Many potential applications require more
  sophisticated real-time control of the stimulation as
  well as closed-loop compensation for modeling
  errors and disturbances.
   – balancing during standing
   – torso control during sitting, and walking.

• Closed-loop FES systems require less user
  interaction, thus facilitating tasks

• The response of muscles to electrical stimulation,
  which is nonlinear, time varying, and coupled, is often
  accompanied by unpredictable perturbations in
  people who have SCI.
• The sensors that are required for feedback can make
  closed-loop FES systems cumbersome and time
  consuming to attach and remove.
• Closed-loop control strategies have not gained
  ground in clinical applications of FES technology.

In joint level control, the challenges are:
 Nonlinear and time varying response
 Spinal reflexes and perturbations due to
  spastic muscle contractions
 Controlling a highly coupled system
 Many muscles are biarticular
 Time delay of 10–50 ms between stimulation
  and the onset of a muscle contraction

FES control system
                              muscle spasms &
                              spinal reflexes

               muscle being

Considerations when testing closed-loop
FES control systems
 FES controller must be initially tested in isolation from
  voluntary muscle contractions.

 Subjects with SCI have to be trained before controller
  testing     approximate the muscles of a chronic FES user, so
  provide a more realistic testbed for a clinical FES controller.

 Individuals must be neurologically stable before they
  are recruited (after at least 12 months)

 Standard performance measures must be reported
             an overview of some of the

Existing Strategies for Closed-loop Control

A controller for FES-based unsupported
standing in a paraplegic subject

• The objective of the controller is to maintain a hip angle of 0◦.
• The control algorithm consists of a proportional-integral
  differential (PID) controller in series with a nonlinear function
  that relates PID output to the duration of the stimulation pulses.
• The performance of the controller is evaluated by applying a
  disturbance that causes the subject to bend at the hip and
  recording how quickly the controller rejects the disturbance.
• The results show that the controller provides a 41% reduction in
  RMS error and a 52% reduction in steady-state error compared
  to open-loop control.

•   J.J. Abbas and H.J. Chizeck,1991

An FES system for unsupported standing that maintains
balance by stimulating the ankle flexor and extensor
muscles to regulate ankle moment

• Uses an H∞ controller to regulate the moment about the ankles.
  H∞ control guarantees stability when the nominal models of the
  plant and the uncertainties in the system are accurate and is
  able to compensate for perturbations that are included in the
  plant model.
• The H∞ controller maintains stability during a series of ankle-
  moment     tracking     and     disturbance-rejection tests  in
  neurologically intact subjects.

•   K.J. Hunt, R.P. Jaime, and H. Gollee,2001
•   W. Holderbaum, K.J. Hunt, and H. Gollee,2002

A neuro-PID controller for regulating knee angle

• Uses an artificial neural network to map the nonlinear
  relationship between the desired knee angle and the required
  stimulation parameters and also uses a PID controller in a
  negative feedback loop to compensate for tracking errors
  caused by disturbances and modeling errors. The neural
  network is trained using a conjugate gradient algorithm, and the
  PID controller is tuned using the Ziegler-Nichols method.
• The neuro-PID controller achieves an RMS error of 5◦.

•   G.C. Chang, J.J. Luh, G.D. Liao, J.S. Lai, C.K. Cheng, B.L. Kuo, and T.S. Kuo,1997

Examples of Closed-Loop FES
Controllers for Regulating Knee Angle

Open-loop controller

Uses the inverse knee model as a compensator

Closed-loop PID controller

Feedforward-feedback controller

Combines the inverse knee model with a PID controller

Adaptive controller

Uses the inverse knee model to deliver a stimulation signal to both the
plant and the direct model, so that the direct knee model functions as an
observer of the plan.

Comparison                             (M. Ferrarin, F. Palazzo, R. Riener, and J. Quintern ,2001)

•   The RMS errors for each controller when tracking a sinusoid:
     1)   11.7◦
     2)   6.0◦
     3)   4.6◦,
     4)   less than 10◦ after 2 min of adaptation.
•   The average lag for the same tracking task:
     1)   0.18 s
     2)   0.29 s
     3)   0.18 s
     4)   not reported because the lag changes during the adaptation process.

•   The feedforward-feedback controller performs best. But the inverse
    model is imperfect because it neglects noninvertible model

EMG Closed-Loop FES
• A novel assistive system with the minimum effect on the voluntary
• EMG is adopted as the sensing feedback information to regulate
• A two-stage filter is proposed to process the raw EMG signal.
      The first stage removes the artifacts in the raw EMG signal contaminated by
      The second stage filter separates the high frequency tremulous EMG from the
       low frequency voluntary components.
•    The extracted tremor EMG of biceps and triceps will then be used
    as control input in the FES controller to stimulate the two muscles

• Physical and drug therapy cannot provide a
  successful treatment
• Sensors widely used in the system for tremor
  suppression are accelerometers, gyroscopes,
  goniometers and force transducers

• In using EMG for FES application, the stimulation
  pulses will contaminate the natural EMG     eliminate
  two artifacts:
    Stimilation artifact(SA)
    Muscle response (M-wave)

• Reducing the tremulous motion while preserving the
  voluntary motion
• So the key problem involved with the tremor
  suppression is how to distinguish the tremulous
  component from the voluntary motion.
• The tremor EMG will be used to control the FES.

 System diagram of EMG
controlled FES for pathological
tremor suppression.

Surface EMG from biceps and
triceps recorded and filtered, the
tremor EMG are used to control
the stimulation for biceps and
triceps reciprocally, in order to
attenuate the tremulous motion
and minimize the effect on the
volitional motion.

1) The raw EMG data is collected on healthy subjects.
2) Without the use of electrical stimulation, the patients
   are tasked to perform similar movements.

3)  Raw EMG data is measured from patients during
   electrical stimulation. Filter algorithms developed in
   steps 1 2 are applied to process the data.
4) Controller design, the amplitude of electrical pulse
   can be controlled directly by the tremor EMG.

     The key work of the whole system is about the EMG

Two-stage filter for EMG signal processing for
pathological tremor suppression via FES

In the 1st stage, artifacts caused by stimulation are filtered and
natural EMG is chieved; in the 2nd stage, tremor EMG is
distinguished from volitional EMG.

Arrangement of the electrodes   Experimental setup for EMG
                                recording under FES

Results for 1sth filter
performance with regard to removal of the artifacts   performance with regard to removal of the artifacts
caused by FES during voluntary movement               caused by FES during voluntary movement

•   CHERYL L. LYNCH and MILOS R. POPOVI: Functional Electrical

•   Dingguo Zhang and Wei Tech Ang, Reciprocal EMG Controlled FES for
    Pathological Tremor Suppression of Forearm, Proceeding of the 29th annual
    International Conference of the IEEE EMBS, Cité Internationale, Lyon, France
    August 23-26, 2007.

Models of the Response of
Electrically Stimulated Muscle

Physiological models           Black box models
• Accurate                     • Reproduce the input-
• complex                        output behavior of real
• Specific to a particular       muscle
  subject                      • Their structure does not
• Values of some of the          necessarily reflect the
  anatomical and                 physiology of muscle.
  physiological parameters
  can be difficult to obtain

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