Abstract
The spinal cord
can be damaged by injury or disease, which can lead to medical complications
including the loss of motor function. FES is
one technique that can be used to replace lost motor function. Many potential
clinical applications of FES require
closed-loop control of electrical stimulation. This paper outlines the
challenges to control that are presented by FES.
Also, a set of design criteria for clinical FES
applications is proposed; these criteria were developed with input from medical
and technical personnel as well as spinal cord injured individuals. Lastly, a
review of the state of the art in closed-loop control of FES is provided, and a
framework for future work on clinical FES
systems is suggested.
1.
INTRODUCTION
Restoring motor function to individuals with spinal cord injuries
(SCI) is an active research topic in rehabilitation engineering, and functional
electrical stimulation (FES) can be used to
achieve this goal. FES involves artificially
inducing a current in specific motor neurons to generate a skeletal muscle
contraction. FES can be used to induce joint
movement by stimulating the flexor and/or extensor muscles of the joint. The
resulting joint angle can be controlled by modulating the amplitude of
stimulation. FES has been used for a wide
range of applications including providing a tenodesis grasp to quadriplegic
individuals [1], facilitating standing for individuals with complete SCI [2],
providing FES-based cardiovascular exercise to SCI patients [3], and
reinforcing gait patterns during walking for incomplete SCI patients [4].
To date, most FES systems that are in
use outside of research labs are open-loop systems, meaning that the controller
receives no information about the actual state of the system. These systems
require continuous user input to perform well, which limits their usefulness to
situations in which the user can devote his or her full attention to operating
the FES device.
However, there are many potential applications of FES technology for SCI
individuals that require the FES system to
work autonomously. These applications include neuroprosthesis systems for
balancing during standing, torso control during sitting, and FES-assisted
walking. A critical step in the development of clinically useful
FES systems is finding a suitable algorithm
for closed-loop control of the stimulation. This paper discusses the control
challenges presented by FES, and presents a set of design criteria for clinical
FES systems. Some existing strategies for
closed-loop control of FES are reviewed, and a
framework for future research in this area is proposed.
1.1.
FES Control Challenges
FES presents several
significant control challenges. First, muscle response characteristics are
nonlinear and time-varying. The response of stimulated muscle changes
nonlinearly as the muscle fatigues. Also, regular use of FES
causes a training effect, so the response of the stimulated muscles changes
over time as they become stronger and more fatigue resistant.
Second, certain motor reflexes at the spinal cord level may be
preserved in individuals with spinal cord injury. These reflexes are often
unpredictable and may impede joint movements. Spasticity is also common in SCI,
and is characterized by varying degrees of increased muscle tone and
hyperactive spinal reflexes. In the absence of supra-spinal signals, muscles
can develop a tendency to maximally contract in response to a wide range of
muscular or cutaneous stimuli, causing the limbs to be in an abnormally flexed
position.
Third, the neuromuscular system is a highly coupled system; for
example, the torque that can be exerted by the quadriceps muscle is a function
of the knee joint and hip joint angles, among other factors. Fourth, there is a
significant time delay between stimulation and the onset of a muscle
contraction, in addition to the processing and transmission delays involved in
the electrical stimulation system.
2. METHODS
A set of general design
criteria for clinical FES systems are reported
below. These criteria were developed with input from neurologist, physiatrists,
therapists, engineers, and patients who have participated in FES programs at
the Toronto Rehabilitation Institute and University Hospital Balgrist (Zurich, Switzerland)
since 1997. In order to be clinically useful, a FES
system must be portable, reliable for daily use, robust to changes in the
response of the muscles to electrical stimulation, and easy to use. Specifically, any closed-loop algorithm that is
to be used in a clinical FES system must:
1. compensate for
the nonlinear, time-varying, and coupled nature of the muscle being controlled,
including the effects of fatigue and training.
2. be stable in the
presence of the time delays and perturbations (reflex contractions) that are
inherent to the system.
3. be implemented in
portable, battery powered electronics, and should be designed for at least 16
hours of operation each day (this operation may be intermittent, depending on
the application).
4.
be compatible with efficient setup and calibration
procedures that are simple enough to be performed by a therapist or a patient.
It should also be possible to easily incorporate the calibration procedure into
the user's daily routine.
The system should be tested
with individuals who are similar to the
intended end user of the system. SCI subjects exhibit a significantly different
response to electrical stimulation than healthy subjects, so the two types of
subjects cannot be used interchangeably for testing purposes. Also, subjects
should undergo a standard training process before testing begins in order to
condition the muscles and increase their fatigue resistance.
3. RESULTS
Several
strategies for closed-loop control of FES
muscle contractions are reviewed below. Jaime et al [5] and Matjačić
et al [6] implemented PID controllers for unsupported standing in paraplegic
subjects. Matjačić et al pointed out that the derivative action of
such controllers tends to amplify high frequency noise, which can lead to
system instability if the data is noisy. Hunt et al used H-infinity control [7,
8] and linear quadratic Gaussian control [9] for unsupported standing in
paraplegic subjects. This group reported stable standing and were able to
reject a 1 degree perturbation about the ankle joint.
Hatwell et al
used a model reference controller for FES
control of knee joint movement in paraplegics [10]. The controller tracked
angles at the extremes of the range of joint movement quite well, but exhibited
poor control of mid-range angles. The authors also noted that their algorithm
assumed a linearized plant, and so may not compensate for the nonlinear
recruitment characteristics of muscle and disturbances arising from spastic
reflexes.
Chang et al
reported a combined neural network/PID control system for FES-based knee joint
control [11]. The neural network was trained to obtain the inverse dynamics of
the knee, and was then used for feedforward control. The PID controller was
used as a feedback controller in parallel with the feedforward controller to
compensate for tracking errors caused by disturbances and modelling errors. The
system was tested on one able-bodied subject and one paraplegic subject. The
authors found that the combined neuro-PID controller performed better than
classic fixed-parameter PID control.
Previdi and
Carpanzano used a gain scheduling control strategy that interpolated between
locally valid linear quadratic regulators for controlling FES-induced knee
joint movement [12]. Each local regulator was designed based on a linearized
local model of the knee joint behaviour, which was estimated from a set of
input/output data. Ferrarin et al developed an adaptive control algorithm for
FES-induced knee joint movement [13]. The controller used an inverse dynamic
model of the quadriceps muscle to deliver stimulation to both the muscle and a
direct dynamic model of the muscle.
The error between the measured and predicted knee angles drove the adaptation
mechanism. Error was minimized by iteratively updating two time-varying
parameters of the direct model. The algorithms were tested with two paraplegic
subjects. The authors found that the adaptive algorithm was better able to cope
with fatigue than a PID feedforward/feedback algorithm. Jezernik et al used
sliding mode FES control to regulate knee
joint angle [14]. The controller was tested on six neurologically intact
subjects and two untrained paraplegic subjects. Good tracking of a desired knee
joint trajectory was achieved for up to 8 seconds.
4. DISCUSSION AND CONCLUSIONS
The PID controllers
reported in [5, 6, 11] could work for clinical FES
applications, but would require the use of accurate sensors to avoid
instability. The H-infinity controller described by Hunt et al showed promising
results when rejecting a minor perturbation. However, its ability to perform in
a more realistic situation where it is required to reject larger perturbations
has yet to be determined. The model reference controller reported by Hatwell et
al could be clinically useful if it was altered to work with a more realistic
nonlinear plant. The gain scheduling controller [12] also shows promise for
clinical FES applications provided that
fatigued muscle can be accurately represented by the controller’s model
structure. Ferrarin et al reported
promising results with their adaptive controller for healthy subjects; these
results should be verified with SCI subjects. Jezernik's sliding mode
controller may be a useful controller for clinical applications if its tracking
time could be increased. In general, none of the reported controllers are
capable of meeting the design criteria for clinical FES
systems outlined above. Several of
the controllers showed promise, but must be modified to be more robust to
fatigue and perturbations, and must be tested with trained, SCI subjects to
verify their effectiveness.
FES has been in
existence since the 1960’s, and yet few SCI patients have been provided with FES assistive devices. This is due in part to the
challenges that controlling FES presents, including the nonlinear, coupled, and
time-varying response of electrically stimulated muscle, the problems posed by
fatigue, reflexes, and spasticity, and the time delays inherent in any FES
system. Designing clinical FES is a very
challenging task. However, the human body itself provides clear evidence that
it is possible to control skeletal muscle contractions to perform useful work.
We hope that this paper will provide the FES research community with a review
of the state of the art in closed-loop control for FES, and will help to re-focus
research efforts on getting FES out of the lab
and making the technology available to as wide a range of people as possible.
References
[1] Adamczyk MM, Crago PE.
Simulated feedforward neural network coordination of hand grasp and wrist angle
in a neuroprosthesis. IEEE Trans. Biomed. Eng., 8:297-304. 2000.
[2] Abbas JJ, Riener
R. Using mathematical models and advanced control systems techniques to enhance
neuroprosthesis function. Neuromodulation,
4:187-195. 2001.
[3] Davoodi R,
Andrews BJ. Fuzzy logic control of FES rowing
exercise in paraplegia. IEEE Trans. Biomed. Eng., 51:541-543, 2004.
[4] Thrasher TA,
Popovic MR. Rehabilitation of incomplete SCI using a neuroprosthesis for
walking. Proc. 25th IEEE EMBS Conf., 1:1566-1568. 2003.
[5] Jaime RP, Matjacic
Z, Hunt KJ. Paraplegic standing supported by FES-controlled ankle stiffness. IEEE
Trans. Neural Syst. Rehab. Eng.,
10:239-248, 2002.
[6] Matjacic Z, Hunt
KJ, Gollee H, et al. Control of posture with FES systems. Med. Eng. Phys.,
25: 51-62, 2003.
[7] Hunt KJ, Jaime
RP, Gollee H. Robust control of electrically-stimulated muscle using polynomial
H-infinity design. Control Eng.
Practice, 9:313-328, 2001.
[8] Holderbaum W,
Hunt KJ, Gollee H. H-infinity robust control design for unsupported paraplegic
standing. Control Eng.
Practice, 10:1211-1222, 2002.
[9] Gollee H, Hunt
KJ, Wood DE.
New results in feedback control of unsupported standing in paraplegia. IEEE
Trans. Neural Syst. Rehab. Eng.,
12:73-80, 2004.
[10] Hatwell MS,
Oderkerk BJ, Ari Sacher C, et al. The development of a model reference adaptive
controller to control the knee joint of paraplegics. IEEE Trans. Automat.
Contr., 36:683-691, 1991.
[11] Chang GC, Luh
JJ, Diao GD, et al. A neuro-control system for the knee joint position control
with quadriceps stimulation. IEEE Trans. Rehab. Eng., 5:2-11, 1997.
[12] Previdi F,
Carpanzano E. Design of a gain scheduling controller for knee-joint angle
control by using functional electrical stimulation. IEEE Trans. Contr. Syst.
Technol., 11:310-324, 2003.
[13] Ferrarin M,
Palazzo F, Riener R, et al. Model-based control of FES-induced single joint
movements. IEEE Trans. Neural Syst. Rehab. Eng., 9:245-257, 2001.
[14] Jezernik S,
Wassink RGV, Keller T. Sliding mode closed-loop control of FES:
controlling the shank movement. IEEE Trans. Biomed. Eng.,
51:263-272, 2004.
Acknowledgements
The
authors would like to thank the Natural Sciences and Engineering Research
Council of Canada for financial support.