FES Control FES Control Strategies & Challenges Presented by: Rahele Shafaei Professor:...

FES ControlFES ControlStrategies & Challenges

Presented by: Rahele Shafaei

Professor: Dr.Towhidkhah

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Table of contents

♠ FES♠ 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)

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Introduction

از جمله روش هاي •متداول در جبران

نسبي عوارِض� نقص حرکتي ناشي از آسيب

سيستم اعصاب در است و نقش مركزي

محرک مصنوعي عصب را بازي مي کند.

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• 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.

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Electrodes

• 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

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Applications• FES is the most

commonly used technique for improving motor function in SCI individuals.GraspingReaching Standing Stationary rowingCycling

Neuroprosthesis

Push Button

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Other…بهبود شنوايي و بيناييجلوگيري از ناخودداري مثانه و رودهكنترل دفعتنظيم ريتم قلبيبازگرداندن عملكرد اندام جنسي مردبهبود عملكرد تنفسيايجاد حركت در اندام هاي تحتانيايجاد حركت در اندام هاي فوقاني رفع افتادگي پا(drop foot)

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Differences between the production of tension inneurologically intact and SCI individuals

Synchronous motor-unit stimulation Nonphysiological recruitment

FATIGUEFATIGUE

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FES Control

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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.

FrequencyAmplitudePulse width

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Considerations

• 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.

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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.

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But…• Many potential applications require more

sophisticated real-time control of the stimulation as well as closed-loopclosed-loop compensation for modeling errors and disturbances.– balancing during standing

– torso control during sitting, and walking.

Also…• Closed-loop FES systems require less user

interaction, thus facilitating tasks

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However…• 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.

Also…• The sensors that are required for feedback can make

closed-loop FES systems cumbersome and time consuming to attach and remove.

Consequently…• Closed-loop control strategies have not gained ground in

clinical applications of FES technology.

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So… In joint level control, the challenges are:Nonlinear and time varying responseSpinal reflexes and perturbations due to

spastic muscle contractionsControlling a highly coupled systemMany muscles are biarticularTime delay of 10–50 ms between stimulation

and the onset of a muscle contraction

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FES control system

muscle spasms &spinal reflexes

muscle being bumped

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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

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Existing Strategies for Closed-loop Control

an overview of some of the

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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

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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

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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

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Examples of Closed-Loop FES Controllers for Regulating Knee Angle

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Open-loop controller

Uses the inverse knee model as a compensator

Closed-loop PID controller

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Feedforward-feedback controller

Combines the inverse knee model with a PID controller

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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.

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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 components.

EMG Closed-Loop FES Control

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Abstract

• A novel assistive system with the minimum effect on the voluntary movement.

• EMG is adopted as the sensing feedback information to regulate FES

• 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

FES. 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 reciprocally.

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Advantages

• 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

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problems

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

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• 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.

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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.

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Steps

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.

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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 processing

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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.

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

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Arrangement of the electrodes Experimental setup for EMG recording under FES

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Results for 1sth filter

performance with regard to removal of the artifacts

caused by FES during voluntary movement

performance with regard to removal of the artifacts

caused by FES during voluntary movement

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References

• CHERYL L. LYNCH and MILOS R. POPOVI: Functional Electrical Stimulation,CLOSED-LOOP CONTROL OF INDUCED MUSCLE CONTRACTIONS, IEEE CONTROL SYSTEMS MAGAZINE,April 2008

• 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.

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Models of the Response ofElectrically Stimulated Muscle

Physiological models

• Accurate• complex• Specific to a particular

subject• Values of some of the

anatomical and physiological parameters can be difficult to obtain

Black box models

• Reproduce the input-output behavior of real muscle

• Their structure does not necessarily reflect the physiology of muscle.

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