A RECONFIGURABLE SYSTEM FOR MOVEMENT
REHABILITATION AND DIAGNOSTICS WITH FES
Piotr Kaczmarek, Andrzej Kasi
´
nski, Marek Kraft and Przemysław Mazurkiewicz
Pozna´n Univeristy of Technology, Insitute of Control and Information Engeenering, Piotrowo 3a, 60-395 Pozna´n, Poland
Keywords:
Electrical stimulation, rehabilitation, movement restoration, instrumentation.
Abstract:
The architecture of a custom originally designed FES system is described. The system is built from off-the-
shelf and cusom components to obtain a target functionality. List of potential applications is provided. Some
tasks have been already tested in laboratory and clinical conditions. Hardware specification of proprietary
components is given and interfacing issues are addressed.
1 INTRODUCTION
Motion restoration through the appropriate functional
electrostimulation (FES) of a neuro-muscular system
is an interesting perspective. Still, the investigations
are continued at the level of a single joint as well as
that of a limb. The most interesting problem is to eval-
uate how the electrical stimulation program translates
into an efficient and controlled elementary motion.
Commercially available functional stimulators can be
divided in 2 groups: stimulators for clinical research
(like the S88 from Grass Teledyne), daily-use electri-
cal stimulators for rehabilitation and functional elec-
trical stimulation systems used in neurophysiological
research (like NeuroTrac, Compex Motion, ODFS,
Parastep)(Keller et al., 2002; Taylor et al., 1999). In
principle all of these stimulators provide the possibil-
ity to alter the stimulation pulse frequency, amplitude
and duration. In most cases (S88, NeuroTrac, ODFS,
Parastep) these parametersare controlled manually by
the user. Only the most advanced stimulators, like
Compex Motion, allow to control the parameters by
means of a closed loop control based on data coming
from a number of sensors (switches, force sensors,
EMG sensors). Most common approach is a sim-
ple event-triggered stimulation for a certain amount
of time with the parameters set by the therapist (ie.
in ODFS stimulators the stimulation sequence is trig-
gered by a heel-switch). All of the above mentioned
stimulators lack the capability of optimizing the shape
of the single stimulation pulse, leaving only various
square or trapezoid pulse combinations at the thera-
pist disposal.
Many research institutes try to determine the in-
fluence of stimulation parameters on the muscle con-
traction, the fatigue effect, as well as the long-term ef-
fects generated in the muscles subjected to persistent
stimulation (this refers to the regeneration process as
well as to side effects). The experiments are aimed at
investigation of physiological properties of the mus-
cles both of healthy and of disabled subjects (Chizeck
et al., 1999). An interesting issue is to reveal the opti-
mal stimulation pattern for the application in the FES
systems (Breen et al., 2006). So far the optimization
process was restricted to the selection of the stimuli
train frequency (Chou et al., 2005), or the pulse width
and amplitude. The influence of the stimulating pulse
shape on the contraction force has not been studied
yet. Moreover, research results of the experiments
concerning the pulse shape influence on the muscle
contraction are not clear (Bennie et al., 2002).
Both movement restoration and rehabilitation pro-
cesses are evaluated upon apparent effects. Such ef-
fects can be used as a feedback for the FES control
algorithm, however it is desirable to quantify the ef-
fects, which are of different character. First apparent
effect is the response of neuro-muscularsystem under
stimulation on its own. The neuro-muscular system
excitation level can be judged on the basis of the elec-
tromyographic signals (EMG) being the response to
17
Kaczmarek P., Kasi
´
nski A., Kraft M. and Mazurkiewicz P. (2008).
A RECONFIGURABLE SYSTEM FOR MOVEMENT REHABILITATION AND DIAGNOSTICS WITH FES.
In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 17-22
DOI: 10.5220/0001049500170022
Copyright
c
SciTePress
the electrostimulation. In movement restoration ex-
periments however, it is necessary to measure and to
analyze the EMG of the particular muscles contribut-
ing to the movement (Kutch and Buchanan, 2001).
Moreover, it might be desirable to use a multi-channel
transcutaneus electrode to support the decomposition
of the EMG signal for studying the propagation of
a single action potentials and motor point localiza-
tion, or to analyze the signals in more detailed way.
In order to get a better image of the stimulation ef-
fects there might be also required to partially esti-
mate the state of a biomechanical system from ad-
ditional physiological signals such as electroneuro-
grams (ENGs)(Sinkjaer et al., 2003) or mechanomyo-
grams (MMGs) (Kaczmarek et al., 2005; Orizio et al.,
2003) which better reflect the senso-motor system ac-
tivity as well as changes of the muscle geometry, re-
spectively.
However, these signals reflect the intermediate ef-
fects of electrostimulation, but the target result (the
final effect) is the motion of a limb or at a lower level,
the motion of a joint under study. There are two in-
teresting aspects of the motion evaluation - the static
aspect, related to the force generated by a joint at a
certain perceived level of the EMG activity while cor-
related with joint configuration (angle), and the sec-
ond, which is related to the resulting motion dynam-
ics. The investigation of the first aspect is performed
by the simultaneous recording of EMGs of the appro-
priate muscle and the resulting force being directly
recorded with the tensometric device, while the joint
is locked at a particular angle. For the second aspect,
it is necessary to acquire simultaneously the signals
of the generated force, EMGs, the joint-angle rotation
signal and the task-space acceleration signal due to
the resulting dynamic motion of the limb.
This would be possible under the condition that
one disposes of a fully programable electrostimulator
with a necessary number of outputs, of a measurement
system running during stimulation (a multichannel
EMG signal acquisition and a processing system) and
of a measurement system to evaluate the biomechan-
ical effects of the FES (an instrumented exoskeletal
device - the orthosis). In this paper we describe such
a custom system, which has been built from modules
and list examples of applications of that system to the
rehabilitation and diagnostics procedures.
2 THE SYSTEM ARCHITECTURE
The schematic outline of the complete system is
shown in fig. 1. The main control and sensors readout
tasks are performed by the embedded 3,5” PC. The
Figure 1: The block diagram of the FES system.
PC is equipped with a Intel Celeron 400MHz pro-
cessor, 256MB RAM and 4GB CompactFlash card.
The configuration of the system involved in a partic-
ular task is open in a sense, that the use of a sim-
ple serial peripheral bus (SPB) and of a specially
designed communication protocol enables the user
to connect a number of intelligent sensors (like ac-
celerometers, goniometers, tensometers, point EMG
sensors) to form a distributed measurement system,
enabling tailoring of the system configuration to the
particular task at hand. These distributed sensors
perform all the measurement, preprocessing and data
forming operations on-site. This allows to reduce the
influence of various environmental interferences on
measurements. The availability of sensor data enables
the system to perform real-time closed loop control
of joints by proper muscles stimulation depending on
this data. The embedded PC is equipped with a multi-
channel A/D converter PCM-3718HG. This converter
is used to measure the data from a multi-section EMG
electrode. The PC is also fitted with the Bluetooth
wireless interface. This enables remote control (for
example by exchanging stimulation programs during
rehabilitation), the visualization of a system state and
sensor readouts (usable in biofeedback applications).
For simple therapeutic programs at the patient bed it
is possible to control the system by a palmtop.
2.1 Electrostimulator
The stimulator unit has the ability to perform stimula-
tion through four independent channels. This enables
independent stimulation of different muscle groups.
The control circuit consists of a microcontroller and
a 4-channel, 8-bit digital to analog converter (DAC).
BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices
18
Such the system setup enables the user to define the
pulse shape, frequency, amplitude and other parame-
ters (i.e. number of stimuli, initial delay, pulse train
profile and modulation frequency) independently for
each stimulation channel. The output stage consists of
a voltage controlled current source, which can source
a current of up to 60 mA and can work with volt-
ages up to 400 Vpp, which is important in the case
of the transcutaneous stimulation. The high voltage
stage is galvanically separated from the control cir-
cuit. Control and programming of the device can be
performed on-line via RS232, SPI or I2C interface.
In that way the described device let the therapist to
define flexibly the movement-restoration or rehabili-
tation requirements to each individual patient and to
the particular task.
2.2 Orthosis and its Instrumentation
The orthosis is a skeleton enabling the force and po-
sition sensors attachment. Fig. 2 presents the ex-
oskeleton of the ankle joint, which is one of the most
important joints for balancing and locomotion tasks.
The orthosis enables to perform static measurements
(during an isometric contractions), as well as dynam-
ical acquisitions during walking or balancing. It is
equipped with the angle sensor i.e. an incremental
encoder of 0.05 deg resolution and with the force sen-
sor based on a planar-beam force sensor with a full-
bridge strain gage. The system is capable of measur-
ing a torque generated by a dorsi as well as a plantar
flexion during isometric contractions for variable an-
kle joint angles in the range between -10 and 10Nm.
The mechanical solution of the force sensor allows to
obtain the force, that is always perpendicular to the
sensor beam and independent on the ankle joint an-
gle. The orthosis controller has a 10-bit AD converter
with a variable input gain. This let the user to se-
lect appropriate gain depending on the performed test
and the torque value. Moreover, the controller per-
forms signal processing tasks such as a signal filter-
ing and sampling with rate up to 4kHz, evaluation of
the absolute angle of the joint and data buffering. The
communication and data transfer to the Embedded PC
is performed via SPB. Moreover, during locomotion
or balancing tests, 3D accelerometric sensors can be
fixed to the exoskeleton. The accelerometric sensor
is equipped with a microcontroller and a 10bit AD
converter, moreover it has 3 digital inputs enabling to
connect the additional switches (foot switches or limit
switches) or other discrete triggering elements. The
smart sensor can operate in three modes, selected by
the user via SPB. The first one is the data acquisition
mode, where raw data is transmitted to the embedded
PC. The second is the data processing mode, enabling
the estimation of the velocity and of the position and
the signal filtering. The third is the events detection
or a fuzzification mode, which is used for fuzzy logic
control algorithm. The sensor in such a mode evalu-
ates the system state from the accelerometric signals
and digital input state according to the given set of
fuzzy rules.
The use of registered signals is the following. 3D
acceleration signal together with goniometric signal
let to reconstruct the position and the motion tra-
jectory independently. This enables to analyze the
kinematic effects of the electrostimulation and to dis-
cover pathological deficits. The tensometric subsys-
tem gives the opportunity to evaluate the contraction
force at a single joint. Thus not only the kinestatic
evaluation is possible but also dynamics of the move-
ment can be studied.
Figure 2: Instrumented Orthosis of the ankle joint.
2.3 EMG Acquisition Subsystem
Multichannel EMG. Important part of the pre-
sented system is an EMG electrode (Fig 3). The elec-
trode consists of 9 rectangular AgCl contacts surfaces
placed of a distance of 5 mm and with the contact
surface size of 10x1[mm]. Multichannel EMG signal
acquisition system is modular, where every module is
connected to a single acquisition channel. It consists
of an instrumental amplifier, analog high-pass Butter-
worth filter (f3dB=10Hz), low-pass Butterworth filter
(f3dB=1000Hz) and a final amplifier stage giving the
total gain within the range 1000÷2000. Acquired sig-
nals are collected by a PCM-3718HG card connected
to the embedded PC.
Point-like EMG Smart Sensor. Smart EMG sen-
sor is a device with analogue and digital parts. The
analog part is similar to that described in a previous
section. However, the contact surfaces (electrodes)
are integrated with the PCB of the analog subsystem.
A RECONFIGURABLE SYSTEM FOR MOVEMENT REHABILITATION AND DIAGNOSTICS WITH FES
19
Figure 3: Linear electrode array for multichannel EMG.
The digital part consists of microcontroller with a 10-
bits AD converter. Its main role is the acquisition of
the analog EMG signal, buffering and basic prepro-
cessing such as evaluation of the RMS value. The
sensor can operate in two modes. In the first, the raw
data are transmitted via SPB to the master controller,
in the second mode only the results of processing are
transmitted. The sensor is designed to be placed di-
rectly over a tested muscle in order to reduce noise
and artefacts. Additionally, all IC chips can work in
the shutdown mode for protecting the sensor ampli-
fiers from damage due to the high-voltagestimulation.
3 APPLICATIONS OF THE
SYSTEM
3.1 Research Tasks
As far the system has been used in 4 research tasks
focused on the FES issues.
Stimulus Waveform Optimization. The system re-
stricted to the stimulator, the force sensor and the
EMG sensors has been used to evaluate the influence
of the stimuli shape on the muscle contraction dynam-
ics, and comfort level. The experiments were per-
formed on a group of healthy volunteers and subjects
with movement deficits. Within this study the bipha-
sic stimuli with inter-phase interval (IPI) have been
tested. 24 stimulus waveforms have been taken into
account. They are combination of a pulse widths (50,
100, 175, 250us) and the inter phase intervals (0, 50,
250, 500, 1000, 2000us). The preliminary results sug-
gest that the contraction force recorded during stimu-
lation depends not only on the pulse amplitude, width
and frequency, but also on the IPI. The contraction
force has increased significantly with increase of the
IPI. Additionally decrease of the pain sensation side-
effects was reported. The results suggest, that the
modification of the pulse shape seems to be an alter-
native for the the commonly used force control tech-
niques such as a pulse-width, amplitude or frequency
modulation.
Evaluation of the EMG-force Relationship. Us-
ing the instrumented orthosis and exoskeletons one
is limited to apply the FES system only in the clin-
ics. Therefore it is essential to built the portable FES
with feedback based on measured physiological sig-
nals. This is necessary in order to evaluate or estimate
the force and the joint configuration. In this study,
the maximal voluntary contraction force and the EMG
signal have been recorded while varying the ankle
joint angle. It was observed, that for a medial gas-
trocnemius muscle the maximal force increases with
increase of the dorsi flexion, however the RMS value
of the recorded EMG signal significantly decreases,
which may suggest the decrease of the muscle activ-
ity level. This phenomenon demonstrates that the for
the force estimation at a gastrocnemius muscle from
the EMG signal, the simultaneous estimation of the
ankle joint orientation is required. This effect did not
occurred for tibialis anterior muscle.
Evaluation of the Fuzzy Rules for a Fuzzy Logic
Controller for Movement and Balancing Tasks.
The efforts are made to create a fuzzy sets describ-
ing the phase of gate cycle during locomotion from
5 accelerometric sensors located at lower limbs and
the trunk as well as from EMG sensors. This could
enable to estimate the mass center position changes
during locomotion or balancing tasks. The smart sen-
sors are powerful enough to perform simple classifi-
cation tasks and enable easy testing and evaluation of
the rules.
Developing of the FES Control Algorithms. Us-
ing the Embedded PC with Linux on-board as the
system supervisor enables an easy development and
testing of various control algorithms based on fuzzy
logic, neural networks or classical control. Moreover,
the standard, well known and powerfull tools and li-
braries can be used for the application developing and
testing. The distributed intelligent sensors may be re-
configured to work in a given control mode delivering
a appropriate feedback signals.
3.2 Diagnostics Tasks
The diagnostics tasks refer to the potential application
of the system in the clinical field. In the presented
system it is possible to perform the electrophysiolog-
ical as well as kinematics test, which may support the
diagnosis. Moreover, such system could be used for
BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices
20
Figure 4: The multichannel EMG signals recorded from a tibialis anterior muscle with 8-point linear electrode array. A
selected Action Potential (AP) propagating through the muscle has been marked with the arrows. The mean EMG signal
power has also been computed. The black dots denote the mean signal power of the particular EMG channels. The maximum
of the mean signal power between the channels ch4 and ch5 denotes the motor point localization.
selecting the individually optimized stimulation treat-
ment.
Multisection EMG. Signals recorded with the mul-
tichannel EMG signal acquisition system have been
used to estimate the conduction velocity of motor
unit’s action potentials (AP). This can be achieved by
a special technique based on analysis of the APs hav-
ing similar shape and being recorded in neighbouring
channels. Selected AP generated by a single MU has
been marked in Fig. 4. The AP propagation veloc-
ity can be computed on the basis of measured delay
of the AP observed in particular channels and known
distance between electrode points. Additionally, the
system allows to localize neuromuscular junction in a
particular muscle. It has been shown that signal pa-
rameters such as AVR or mean frequency (Fmnf) of
the signals recorded at given points located along the
tested muscle, can indicate the localization of the in-
nervation zone. The motor point can be localized in
two ways: the first is localization based upon the max-
imum signal power and the second is the AP phase
inversion. The phase inversion can be seen in Fig 4
where the AP marked with the arrows is inverted in
the channels ch5, ch6 and ch7. The motor point lo-
calization is important for an appropriate stimulation
electrodes placement in FES application as well as a
linear array-electrode placement in the signal decom-
position task. The main purpose of the decomposition
task is to estimate a discharge time of a particular mo-
tor unit during a voluntary contraction. In contrary to
the decomposition systems of the intracellular EMG
signals, decomposition of the multichannel surface
EMG signals does not require to use needle electrodes
which is an advantage. Multichannel recordings may
compensate for the lower selectivity and give a deeper
insight into the motor units activity. These informa-
tion can be used in more sophisticated algorithms to
improve the overall performance. The smart EMG
sensors can be used during walking tasks in order to
A RECONFIGURABLE SYSTEM FOR MOVEMENT REHABILITATION AND DIAGNOSTICS WITH FES
21
analyze the muscles synchronization issues. Two sen-
sors located over 2 antagonistic muscles can discover
their synchronization during contractions. Moreover,
the information from these sensors can be used in con-
trol algorithms.
System Identification. The estimation of the sys-
tem state only from physiological sensors or the de-
termination of fuzzy sets must be adjusted to the in-
dividual subject. Moreover, the optimal control algo-
rithm for the FES controller must be selected. The
system is capable to perform tests enabling to work
out the force-EMG relationship for each subject. It is
possible to prepare a set of tests which enable semi-
automatic calibration of the sensors system.
The Stimuli Optimization. The stimulation exper-
iments revealed the variability of the excitability level
for the same stimulation procedure. The variability is
dependent on the pathology but also on the individ-
ual features of the subject. Therefore, the stimulation
waveforms and stimulus shapes should be selected in-
dividually. It was observed, that the stimulation let to
divide the subjects into few groups. The system let to
perform tests enabling classification of the subject to
the particular group.
3.3 Rehabilitation Tasks
Repetitive Exercises with or without Stimulation
Support. The aim of such a treatment is to increase
the maximum contraction force, or to increase a range
of motion. A subject can observe the actual force
level, the EMG amplitude, or the joint angle on the
screen, and to try to follow the reference trajectory as
set by the therapist. Moreover it is possible to use the
stimulation in order to compensate partially for the
error between the reference and the actual trajectory.
Restoration of Movement Functions for the Phys-
ically Disabled Subject. The main aim of the pre-
sented system is to develop a daily-use FES system
for the restoration of movement functions with a min-
imal number of sensors. The system is potentially ca-
pable to operate in closed feedback loop mode with
sensors and stimulation sequences configured indi-
vidually by therapist on the basis of the identifica-
tion tests results. However, to obtain satisfying results
with this application further investigations and devel-
opment of control algorithm is necessary.
ACKNOWLEDGEMENTS
The work was partially supported by the Polish
Ministry of Education and Science, project no.
1445/T11/2004/27.
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