Strategies for Using Electromyographic Signals to Control Ankle Torque in
an Active Joint Brace for Alleviating Drop-Foot Gait
by
Elizabeth C. Lin
SUBMITTED TO THE DEPARTMENT OF MECHANICAL ENGINEERING IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
BACHELOR OF SCIENCE
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
MASSACHUSETTS INSTmTrrE
OF1'ECHNOLOGY
JUNE 2006
0 2 2006
©2006 Massachusetts Institute of Technology
All rights reserved.
LIE3RARIES
v
The author hereby grants to MIT permission to reproduce
and to distribute publicly paper and electronic copies of this
thesis document in whole or in part in any medium now
known or hereafter created.
Signature of Author:
Department of Mechanical Engineering
May 12, 2006
Certified by: :,
(-
-
Woodie Flowers
\f-~
Prossorof
MechanicalEngineering
Thesis Supervisor
Accepted by:
(
'*-.-X~~
John H. Lienhard V
Professor of Mechanical Engineering
Chairman, Undergraduate Thesis Committee
ARCHIVES
1
Strategies for Using Electromyographic Signals to Control Ankle Torque in
an Active Joint Brace for Alleviating Drop-Foot Gait
by
Elizabeth C. Lin
Submitted to the Department of Mechanical Engineering
on May 12, 2006 in Partial Fulfillment of the
Requirements of the Degree of Bachelor of Science in
Mechanical Engineering
ABSTRACT
This thesis describes the efforts to develop control strategies that use EMG signals from
the anterior tibialis muscle to control ankle torque in an active ankle-foot orthosis. This
would ultimately help rehabilitate persons affected by drop-foot gait, a condition which
results in the loss of ability to dorsi-flex the ankle. This causes dropping of the toe during
the swing phase of walking and "slapping down" of the foot after heel strike. Alleviation
of these gait anomalies would improve mobility efficiency, safety, and cosmesis.
Several types of orthotic devices fitted with springs and/or dampers have been used to
control torque around the ankle and consequently facilitate the natural pattern of
movement during the gait cycle. However, recent studies on powered upper-extremity
orthoses controlled by EMGsignals from the users' impaired muscles have produced an
unexpected and potentially exciting result. The use of EMG-controlled orthoses seems to
improve the user's ability to control the compromised muscles and subsequently rebuild
the connection between the brain and the output of those muscles. This could be a crucial
step in helping stroke patients make a full recovery.
Extending this control scheme to a powered ankle orthosis requires understanding the
relationship between ankle torque and EMG signals measured on the muscles likely to
serve as control signal sites. Studies have shown that the muscle whose deterioration is
most responsible for drop foot gait is the tibialis anterior. Thus, this thesis focuses on the
relationship between EMG from the tibialis anterior and ankle torque. Experiments show
that there is a clear pattern of EMG peak periods and silent periods throughout the gait
cycle. However the magnitudes of these peaks are very similar and thus require a
revision in the EMG control strategy used in the upper extremity orthoses which output a
position of the brace via the EMG profile of the muscles around it. The control strategies
devised involve using other inputs in addition to EMG, including foot switches and
position sensors, to supplement the EMG control scheme in a new version of an active
ankle foot orthosis
Thesis Supervisor: Woodie Flowers
Title: Professor of Mechanical Engineering
2
1. Introduction:
Robotic devices are becoming an integral part of the world of physical therapy. These
devices allow for frequent repetitions of motions that are often necessary for a patient to
make a full recovery, especially after suffering ailments such as stroke and spinal injuries.
While there are a plethora of robotic devices that aid the upper extremities for stroke
patients, devices for the lower extremities are usually more involved and thus not as
prevalent. Drop foot gait is a condition common amongst stroke patients which is a result
of a lack of control of the tibialis anterior muscle.'
Myomo, a company spawned from an MIT research group, has developed the Active
Joint Brace for the elbow. This brace senses a patient's EMG signals from the biceps and
triceps and amplifies them mechanically into an arm brace fitted around a patient's
elbow.2 The magnitude of the sensed EMG signals dictate the flexion and extension
torque applied by the brace. This mechanism allows a patient to administer self-therapy
while strengthening connections between his/her brain and limbs.3 In the future, the
Active Joint, Brace will hopefully also be used to improve a patient's ability to perform
daily activities.
Since the only existing prototype is used in conjunction with the elbow joint, the
developers are also looking to expand this technology to other areas of the body that are
affected by stroke. A common condition associated with stroke is drop foot gait, where
'Takebe, Kyoichi, and Basmajian, John V. "Gait Analysis in Stroke Patients to Assess Treatments of FootDrop." Arch Phys MedRehab 57: 305-3 10. (July 1976): 310.
2 Narendram, Kailas and Sahney, Mira. "Advanced Stroke Rehabilitation." Myomo. (October 2005): 7
3 lbid, 8
3
the patient's lower-extremity muscles, particularly the tibialus anterior, atrophy to the
point where they lose control of the ankle joint. This causes the foot to slap and drag
during the gait cycle, which can often cause tripping and general discomfort. Thus, a
natural choice for the next prototype is an ankle brace which will apply the same concept
and technology to a new area of the body.
Currently, the elbow version of the brace is activated solely by the EMG signals of the
biceps and triceps. The torque applied to the elbow is exactly correlated to the profile of
the EMG output. This is ideal for the relearning process of stroke patients because their
effort is directly correlated to the response. However, because the EMG signals of
relevant muscles (tibialis anterior and gastroc soleus) during the gait cycle does not have
as definite a profile as those in the arms, but rather have sporadic spikes that are very
similar in amplitude for different phases, it is more difficult to use EMG signals as the
sole input for dictating ankle torque and position. Therefore, other inputs such as
position sensors or switches are needed to facilitate the process.
This thesis explores the different possibilities for control strategies for such an interactive
ankle brace. Through proposing different processes, it is then possible to find one that is
most reasonable to implement and also requires the fewest additional inputs on top of
EMG signals. The more EMG signals from stroke patients is directly translated to ankle
position and torque, the more the patient has direct control of his/her actions.
4
The steps taken to develop these strategies involved researching existing ankle-foot
orthoses and determining the best method of control, finding EMG data on healthy and
stroke patients, and observing the idiosyncracies of gait cycle for drop foot patients
compared to healthy subjects, and finally simulating potential control loops. These are
the preliminary steps needed to develop a full prototype of an active ankle-foot orthosis
that is activated by EMG signals in the muscles that affect ankle position during walking.
Because using EMG signals to close the loop between brain signals and final function of
the limb allows affected patients to relearn basic motor skills such as walking, an anklefoot orthosis such as this would be a valuable tool for patients to rehabilitate and also
assist them in everyday living. Furthermore, cuing off diminished muscle signals
encourages natural motor patterns as opposed to teaching compensatory strategies. This
will in turn provide a more complete recovery for patients in the long run. Ultimately,
this process will increase the base of patients which the Active Joint Brace can reach,
thus increasing the versatility of the technology developed.
2. Background
To understand the requirements for developing these control strategies, it is first
necessary to explore:
1. The specifications of the existing elbow prototype of the Active Joint Brace
2. The specifics of the human gait cycle and the differences between normal
subjects and stroke patients
3. Existing research on other types of ankle-foot orthoses
5
2.1
The Active Joint Brace
The Active Joint Brace is a device that integrates a patient's own will with the natural
movements being assisted by a robotic device. It is embodied in a battery powered,
wearable brace with a portable power pack. The power pack contains a motor drive-train
system as well as the battery power. This power pack also has a modular design so that it
can be used with several different braces.4 At a weight of less than one pound, the Active
Joint Brace seeks to maximize patient comfort during therapy.
The Active Joint Brace senses EMG signals from antagonistic muscles around a joint and
uses their profiles to determine the torque around that joint. The only current prototype
for this model is an elbow brace and it works via EMG signals from the biceps and
triceps. First, it recognizes attempted muscle contractions when the sensors detect a
surface EMG signal of the muscles connected to the joint. 5 The device then filters and
processes these signals to identify what a patient is trying to do. The system then
actuates a motor to provide the force which is proportional to a magnitude of the control
signal and is necessary to produce a desired degree of muscle contraction. 6 The patient's
brain closes the loop using thoughts to adjust the motion. As seen in figure 2.1, the
process starts with the brain attempting to move the muscles, and the sensors picking up
4
Ibid, 7
5 Narendram, Kailas and McBean, John. 2003. "Powered Orthotic Device: Active Joint Brace." U.S.
Provisional Application No.60/428197, filed November 21, 2003: 3.
6 Ibid, 4.
6
the EMG signals from the muscles, and outputting it physically onto the elbow brace.
.
There are also hard-stops on this device to limit its range of motion for safety reasons.
. I
UJ
-
M* 6
Figure 2.1 (Complete loop for actuating the Active Joint Brace: The Brain to Sensors to
EMG to Physical output on the elbow brace)
This powered orthotic device provides increased strength for patients of degenerative
neuromuscular conditions such as stroke; thus, it is an effective form of rehabilitation that
strengthens the path between brain signals to muscle output. Furthermore, the device, as
seen in figure 2.2 is portable and wearable, which means that it can be used as a means of
assisted living.7
Figure 2.2 (Active Joint Brace)8
7 Narendram, Kailas and Sahney, Mira. "Advanced Stroke Rehabilitation." Myomo. (October 2005): 7
Active Joint Brace: 7.
8 "MIT Active Joint Brace Research." Retrieved on December 10, 2005 from
http://web.mit.edu/activej ointbrace/demos.shtml.
7
2.1
Gait Cycle
To flly understand the specifications needed for an effective ankle-foot orthoses that can
aid drop foot patients, it is necessary to compare the differences in the gait cycle of
normal subjects and stroke patients. Through this, we can determine the muscle
deficiencies that result in drop foot and hypothesize on feasible ways to supplement these
deficiencies.
2.1.1
Normal Gait Cycle
The gait cycle is defined as the period from the time that the foot strikes the ground until
the same foot makes contact with the ground again.9 This cycle consists of two main
phases: the stance phase, and the swing phase. The stance phase describes the portion of
the cycle that the foot is in contact with the ground. It consists of 60% of the gait cycle
and is subdivided to heel-strike, full-foot, mid-stance, heel-off, and toe-off. More
specifically,, heel-strike to full-foot takes up 25% of the stance phase ( 15% of the entire
cycle), full fobotto heel-off consists of 42% of the stance phase (25% of the entire cycle),
heel-off to toe-off is 33% of the stance phase (20% of the entire cycle). The swing phase
describes that portion of the cycle where the foot is not in contact with the ground and
makes up 40% of the gait cycle. It consists of acceleration, mid-swing, and deceleration.
The physical position of the foot in each stage of the stance phase can be seen in figure
2.3. An average person walking at a normal pace has a cadence of 115 strides per minute.
Thus it takes:
115 steps/minute x cycle/2 steps x minute/60seconds=
9 Gray, Edwin G., and Basmajian, John V. "Electromyography and Cinematography of Leg and Foot
("Normal" and Flat) during Walking," Anatomical Record. 161: 3.
8
.95 seconds per cycle
Figure 2.4 shows a detailed description of the duration of each step in the gait cycle.
heel-stike
fill-foot
mid-stance
heel-off
toe-off
Figure 2.3 (Stages of the stance phase)'0
Because the foot is not in contact with the ground during the swing phase, it is not shown
in the diagram; however, at the moment of acceleration, the leg is behind the trunk, at
mid-swing it is directly under the trunk, and at deceleration, right before the heel-strike of
the second cycle, it is well in front of the trunk."'
Event
% Total Cycle
Approx. Time
Heel strike to Foot
Flat
Mid stance
0-13%
.124 seconds
13-33%
.19 seconds
Heel off
33-53%
.19 seconds
Toe-off
53-60%
.067 seconds
Acceleration
60-73%
.124seconds
Mid-swing
73-87%
.133 seconds
Deceleration
87-100%
.124 Seconds
.-
0' Modified
from: Halfner, Brian. "Transtibial Energy Storage-and-Return Prosthetic Devices: A Review of
Energy Concepts and a Proposed Nomenclature."Journal of Rehabilitation Research and Development
39:1 (January 2002), http://www.vard.org/jour/02/39/1/hafner.htm:
'' Gray and Basmajian, 4
1.
9
Total: .95 Seconds
Figure 2.4 (Table of duration of phases of the gait-cycle)'
2
There are several muscles that are involved in the gait-cycle, including the tibialis
anterior, tibialis posterior, flexor hallucis longus, and the gastroc-soleus (calf). Each has a
different function in dorsi-flexion and plantar-flexion of the foot; however, for the
purposes of this thesis, the main focus will be on is the tibialis anterior and the gastrocsoleus. The locations of these muscles can be seen in figure 2.2.2
-
gastronemius
muscle --
soleus
muscle
tibilais anterior
Figure 2.2.2 (Tibialis Anterior and Gastronemius/Soleus muscle)' 3
Electromyographic signals (EMG) show the activity in a given muscle via electrical
signals. When a muscle is exerting more effort, the activity level will be higher, and thus
the EMG signals will have a higher amplitude. Because the tibialis anterior is the muscle
that is mainly responsible for the dorsiflexion of the foot while walking, it is important to
observe the EMG activity of a muscle during the gait cycle.
12 Image from: Mathiyakom, Witaya. "Motion Analysis." Retrieved on May 6, 2006 from
http://www.usc.edu/dept/LAS/kinesiology/exsc30
1/LabManual/Labl_MotionAnalysis.pdf
13 Modified from image in: Human Locomotion Research Center: University of California, Los Angeles.
Retrieved May 6, 2006 from http://www.harkema.ucla.edu/TAjpg
10
At heel strike of the stance phase, there is a peak in EMG activity of the tibialis anterior
(TA). This is because the muscle is exerting energy to decelerate the foot so that it has a
controlled approach to the ground. This prevents the foot from slapping the ground.14
This signal tapers to close to zero during full-foot, mid-stance, and heel-off. There is a
second peak in EMG activity in the TA at toe-off of the stance phase. This peak is
related to the dorsiflexion of the ankle, which permits the toes to clear the floor rather
than drag during the swing phase. 15 The peak of activity at toe-offtapers to a slight to
moderate mean level of activity during acceleration of the swing phase. At mid-swing
there is a period of electrical silence, and during deceleration, EMG signals again build
up to the peak that is experienced at heel strike. 16 The gastroc-soleus (calf) muscle, on
the other hand, exhibits peaks in activity at the points when the TA is silent. This is
because they are antagonistic flexor/extensor muscles. This means that during heel strike,
the gastroc-soleus is silent and activity begins to peak during full-foot and tapers during
toe-off. It is inactive during the swing.' 7 The graph in figure 2.4 shows the relative
activity of each muscle during the different stages of the gait cycle.
14 Gray and Basmajian, 9
'5 Ibid, Gray and Basmajian 10
'5 Ibid, Gray and Basmajian 10
17 Perry, Jacquelin. "The Contribution of Dynamic Electromyography to Gait Analysis." Retrieved May 6,
2006 from http://www.laboratorium.dist.unige.it/-piero/Teaching/Gait/PERRY%20
Contribution
%20of1/o20Dynamic%20Electromyography.htmPerry
11
Tib. Ant
Soleus
Phase
heel
strike
full midfoot stance
heeloff
toe-
off
acc.
mid deceleration
swing
Figure 2.4 (EMG activity in tibialis and anterior and soleus muscles during each phase of
the gait cycle)'8
The pattern of ankle torque and position during the gait-cycle is also necessary for the
design of a potential ankle-foot orthosis. The torque around the ankle is highly
dependent on speed, and thus it is difficult to describe a normalized set of torque values
for stages of the gait cycle. As seen in figure 2.5, however, the ankle torques at 3
different speeds can be seen. In these diagrams, a positive torque means that the ankle is
exerting a force to move the toes in an upward direction. A negative torque means the
ankle is exerting a force to move the toes down. An angle of zero radians denotes that the
leg and foot are perpendicular.
18 Modified from: Perry, Jacquelin. "The Contribution of Dynamic Electromyography to Gait Analysis."
Retrieved May 6, 2006 from http://www.laboratorium.dist.unige.it/-piero/Teaching/Gait/PERRY%20
Contribution %20of/o20Dynamic%20Electromyography.htm.
12
Slow Speed (.9mn s)
Nonnal Speed (1.251ms)
-2
'a
2W =0.104 +/- 0.07
zE
33
-1.5
tV
c
0
:3
I-c
0-.5
rC
a)
-1
4
0
0.5
Ankle Angle (rad)
Fast Speed( 1.791n s)
-2 1 W= 0.268 +- 0.06
-1.5
1
Z
-1
b
-0.5
0
t 0.5
C
-0.4
-0.2
0
.
0.2
0.4
Figure 2.5 (Ankle torque vs. ankle angle at different speeds)' 9
These graphs show ankle torque during the stance phase. It starts with heel-strike (1) and
moves to full-foot (2) to mid-stance (3), to toe-off (4). This data was taken from 68
"normal" subjects and normalized. Most of the threshold values of torque and angle are
the same at each specific stage, however, it can be seen that the trajectory followed is
quite variant. As speed increases, there becomes a wider range of angles experienced. In
general, we can see that the torque of the ankle goes through the following trajectory: at
heel-strike the ankle is applying a zero torque. This torque increases to a slightly positive
value at full-foot (the ankle applies a force to keep the foot from slapping down) and
decreases to a negative value at mid-stance as the foot is pushing down to support the
weight of the body. It increases to a slightly positive value at toe-offbecause there is no
'9 Courtesy of Sam Au from the Biomechatronics Lab at MIT
13
force being applied to the foot as it loses contact with the ground but the ankle is working
to allow the foot to clear the ground without dragging.
The ankle angle, which indicates the position of the foot, goes through the following path.
At hell-strike, the foot starts at zero radians to the ground (perpendicular at the ankle),
and moves to a 0.1 to 0.2 radian angle, depending on speed, at mid-stance (the angle
between the foot and leg is now acute), and back to an angle of around -0.3 radians at toeoff (the angle between the foot and leg is now obtuse). The foot returns to the zero radian
position throughout the swing phase so it is ready to restart the cycle at heel-strike.
2.1.2
Drop-Foot Gait
The most common ankle condition to result from stroke is drop-foot gait, which is a
motor deficiency that results in the instability in dorsiflexion of the foot while walking.2 0
The loss of dorsiflexion control around the ankle joint has a detrimental effect on several
phases of the gait cycle including heel-strike to full-foot, and toe-off to swing phase.
Namely, the foot will slap down (foot slap) after heel-strike and will drag on the ground
after toe-off (toe drag) and during the swing phase. This causes significant discomfort
while walking and may also result in the patient tripping
L:.
unexpectedly while walking.
Figure 2.6 (Drop Foot)2 ]
lr\LJUIJ
Cr-;q
UUL
13
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AU.c
huy., l aVLIIlrr\+
n"nl
-+- ..paIlIlyIS3
u1
iallill
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- -l-Q
L1 1ll1UilVb
UI
20 Takebe and Basmajian, 306.
"Anatomy of the Foot and Ankle with Foot Drop Deformity." Retrieved May 6, 2006 from
http://lpig.doereport.com/generateexhibit.php?ID=842&ExhibitKeywordsRaw-&TL=32737&A=65077
21
14
innervatedby the common peroneal nerve, or the anterior tibial muscle and the peroneal
group.
Because the deterioration of the tibialis anterior muscle is mainly responsible for the
symptoms associated with drop foot, studying the EMG signals from this muscle is our
primary concern. Although there has been some work done on observing the EMG
signals from that muscle for stroke patients during walking, there is not much that is
readily available due to the infancy of most projects regarding ankle-foot orthoses.
However, there is one available paper published in Sao Paolo Brazil by Fernanda Ishida
Correa titled, "Muscle Activity During Gait Following Stroke" that compares the muscle
activities and joint moments in the lower extremities during walking. From his study of
fifteen healthy subjects and fifteen stroke patients of the same age and gender, we can see
the level of deterioration of EMG signals in the tibialis anterior of stroke patients.
,,.
*iF /,
w
,
.....................
...
......
.
.
.
"
,
B,
I
;
1. .....-
*
,
. ...................
-
-
.
.
Figure 2.6 (EMG of the tibialis anterior of normal subjects and stroke patients) 2 3
22 Blaya and Herr 1
23 CORREA, Fernanda shida, SOARES, Flivia, ANDRADE, Daniel Ventura et al. "Muscle activity during gait
following stroke."
stroke." Arq.
Arq. Neuro-Psiquiatr.,
following
Neuro-Psiquiatr., Sept.
Sept. 2005,
2005, vol.63,
vol.63, no.3b,
no.3b, p.847-851.
p.847-851.
15
From this data, we can see that stroke patients also experience a peak at heel-strike and
toe-off as normal subjects do; however, to a much smaller magnitude. The entire profile
of the EMG activity in stroke patients is flattened which means that sensors needed for
detecting these peaks need to be more sensitive. In fact in some subjects, the peak at toeoff is so subdued that it is almost undetectable. For these subjects, it would be difficult to
use EMG signals as a means of mimicking ankle torque to prevent drop foot.
2.2
Existing Devices
While there exist a few devices that aim to treat this condition, none of them offer
treatment using EMG signals that specifically target the ankle joint. For example, the
Active Ankle Foot Orthosis, developed by MIT researcher, Hugh Herr, has sought to aid
drop-foot gait by likening the trajectory of the ankle joint while walking to that of a
torsional spring. It builds upon the concept that "the major complications of drop foot
gait, namely foot slap and toe drag, can be reduced b actively controlling orthotic joint
.1- .......
...........
I- to
L--~-1rl---^impeuance
in response
waclng pnase anu sLep-Lo-sLep
\'llrlaIWa
M,,e,
rthmk
phase variations." 24 Because ankle moment is
FO)
proportional to ankle position, it uses position sensors to
determine what the ankle moment should be. It then
%nkle
rnrle
p.,i,,
replicates the spring constant needed to achieve that
snm
moment.
Figure 2.7 (Active Ankle Foot Orthosis)2 5
24Blaya, Joaquin A. and and Herr, Hugh. "Adaptive Control of a Variable-Impedance Ankle-Foot Orthosis
to Assist Drop-Foot Gait." IEEE Transactionson Neural Systems and RehabilitationEngineering 12:1
(March 2004): 2.
25
Ibid, 2.
16
As seen in Figure 2.7, this prototype is quite bulky due to the various switches and
actuators.
Furthermore, there is very little mental interaction between the patient and the
brace which makes it tough for a patient to actually relearn the walking process. This inturn keeps the patient dependant on the brace. Still, much can be taken from Herr and
Blaya's research on ankle torques and moments and applied to the Active Joint Brace.
At the other end of the spectrum there are also devices that are mainly focused with
rehabilitation rather than assisted living. One example of this is the Ankle-Bot, developed
by MIT mechanical engineers, Neville Hogan and Hermano Krebs. The Ankle-Bot is a
purely therapeutic device that has a patient use the device to play a video game that
requires him/her to use his/her muscles to relearn specific motor skills.
Figure 2.8 (Stages of Ankle-Bot Treatment) 26
While the Ankle-bot is effective in helping patients remap neurological pathways, it has
physical limitations on being used outside a rehabilitation clinic because of its large size
and extensive external wiring.
Thomson, Elizabeth A. "MIT develops Anklebot for Stroke Patients." (June 30, 2005). Retrieved
December 10, 2005 from http://web.mit. edu/newsoffice/2005/stroke-robot.html.
26
17
The last example of an existing device for aiding drop foot gait, and perhaps the closest
in technological intuition to the Active Joint Brace, is a full-body robotic suit developed
at the Tsukuba University in Japan.
Figure 2.9 (Hal-5 Suit Full Body Suit)2 7
This suit uses EMG signals to strengthen all major joints in the body including the ankle
joint. However at a cost of $20,000, it is not targeted to stroke patients but rather to the
elderly who need assistance performing daily activities.
Thus, the ankle prototype of the Active Joint Brace can refine the EMG activation
process more specifically to treat drop-foot gait and thus help eliminate this wide-spread
problem among stroke patients.
27 "Robot Suit HAL." Retrieved on December 10, 2005 from
http://sanlab.kz.tsukuba.ac.jp/HAL/indexE.html
18
3. Methods
While the bulk of this thesis project involved doing preliminary research on possible
ways of making a feasible ankle-foot-orthoses actuated by EMG signals, the project
culminated in devising control strategies using the data gathered and presented above.
3.1
Data Collection
Most of the research that has been presented above has been collected through
researching journals and speaking to experts in the field and doing some experimental
work on healthy subjects.
After researching the anatomy of the ankle and the dynamics of drop foot gait, I secured
interviews with researchers at the Massachusetts Institute of Technology who are
working on different types of ankle-foot orthoses and stroke-rehabilitation techniques.
Namely, I spoke with Dr. Hermano Krebs from the Laboratory for Biomechanics and
Human Rehabilitation who was working on the Ankle-bot and with Samuel Au of the
Bio-mechatronics Laboratory in MIT's Media lab who is researching the Active AnkleFoot-Orthoses (AAFO). Au was able to provide me with parameters for an EMG
activated AFO, which will be presented below.
In order to gain a better understanding of the deficiencies in stroke patients, I consulted
Dr. Paulo Bonato of the Motion Analysis Laboratory at Spaulding Rehabilitation Center
in Boston, Massachusetts. Dr. Bonato has been involved in extensive research on EMG
signals of stroke patients during the gait cycle. However, due to confidentiality issues, he
19
could not give me access to any of his findings. He did, however, suggest a few journals
to look at for a better understanding of the anatomy of walking.
3.2
Experimental Work
Since researchers of the Active Joint Brace have already developed the equipment needed
to read EMG signals from the biceps and triceps, they were interested in seeing if the
same testing equipment could be used for the ankle brace as well. Since we are targeting
the tibialis anterior and the gastroc/soleus, they first wanted to see the best location for
sensing these signals using their ready-made equipment. Thus, using strap-on sensors
and a control box, I tested 3 subjects including myself to see the area of the muscles that
would achieve the best results. The method for testing the gastroc/soleus was reading the
signals from one calf muscle while the subject stood on the toes of both feet and
comparing it to the signals when she stood on the toes of just one foot. Readings were
taken from six areas of the calf muscle, the upper and lower portions of the inner, center,
and outer calf. The area that showed the best 2 to 1
relationship of signal magnitude for 1 foot to 2 foot support
signaled the best area for the sensors. Through this process,
it was found that the lower center of the calf, as shown in
figure 3.1, was the best location for the sensors to be placed.
Figure 3.1 (Optimal location of sensors on calf of subject A)
20
For the tibialis anterior, three locations were tested, the upper, middle and lower portions
of the muscle. Subjects were asked to pull their toe towards their body against the
resistance of a thera-band secured onto a post while seated on the ground. The theraband was then folded in half as to create double the resistance and the subjects were
asked to repeat the motion. Again the best 2 to 1 relationship in EMG signals for the
trials with twice the resistance as compared to just a single
resistance indicated the optimal location for sensors on the
tibialis anterior.
This location was found to be at the upper
portion of the muscle right above the widest part of the lower
leg.
Figure 3.2 (Optimal location for sensors on the tibialis anterior)
The next step was to test the more dynamic sensors that could record EMG signals over a
given period of time. Because the gait cycle has more phases in a shorter amount of time
that could cause EMG to fluctuate at a higher frequency than that of the arm flexing and
extending, researchers wanted to know if the changes in EMG signals could even be
effectively picked up by their sensors. Thus, sensors were strapped onto the calf and
tibialis anterior at the aforementioned optimal locations and the EMG signals of each was
monitored for 3 seconds at a frequency of 10,000 hertz across three sensors. More
detailed descriptions of this experiment can be found in Appendix A.
21
Figure 3.3 (EMG of Calf of Subject A during 3 second of the Gait Cycle)
t
Heel-Strike
Toe-off
Figure 3.4 (EMG of Tibialis Anterior of Subject A during 3 seconds of the Gait Cycle)
Although the data collected was noisier than anticipated, it still showed clear peaks when
expected which indicates that the sensors used would not have to be greatly altered for
the new prototype.
22
3.3
Control Strategies
This project culminated in designing viable control strategies for implementing an EMG
controlled ankle-foot orthosis. Because, as seen through the prior analysis, the peaks in
EMG at heel-strike and toe-off are actually quite similar in magnitude, it was proposed
that other inputs be included to help differentiate between the two phases. The amount of
user control depends on the number of inputs in addition to EMG signals on the tibialis
anterior. For example, if the EMG signals are the only inputs in the device, then the
user's will is the sole contributor the movement of his foot; the brace will provide a little
bit of aid to that movement. However, when switches and position feedback sensors are
introduced to the system, the system may be able to run more effectively because the
brace will know exactly which portion of the gait cycle the user is engaged in. Otherwise,
there could be false positive signals during sporadic spikes in EMG signals which could
cause the brace to apply a torque at the wrong time. However, the introduction of
switches decreases the user interaction significantly. A system that varies torque solely
using position sensors and switches will not require to user to input any effort to get a
result, and thus will not help the re-learning process.
This section will explore three possible control strategies. These strategies will involve
1. Two switches and EMG from the tibialis anterior (Control Strategy 1)
2. A position sensor and EMG from the tibialis anterior and (Control Strategy 2)
3. EMG from the tibialis anterior and EMG from the gastroc/soleus (Control
Strategy 3)
23
3.3.1
Control Strategy 1
The goal of control Strategy 1 was to apply ankle torque right after heel-strike to prevent
the foot from slapping down and to again apply a torque after right before toe off to keep
the foot from dragging on the ground. Since the EMG signals during those two phases
are at a peak in the tibialis anterior, a switch at the heel and one at the toe can indicate
when the EMG signals from the tibialis anterior is to be used to change in ankle torque.
Thus, at heel-strike, a switch located at the heel is compressed and begins the control
loop. If the EMG signals of the tibialis anterior is above a given threshold level (XI),
then a positive torque will be applied to decelerate the foot and prevent it from slapping.
During full foot, mid-stance, and heel off, the EMG signals taper to a level well below
the set threshold and thus no torque is applied. Right before toe off, another switch is
compressed, and at the point of toe off, it is release. The release of the second switch
indicates that the second peak in EMG signals is about to occur. If this peak is above
second threshold (X2 ), which will probably be slightly lower than the first), then another
positive torque will be applied at the ankle to prevent the foot from dragging on the
ground. This torque will be applied throughout the swing phase. At heel strike when the
first switch is activated again, the system will not look for a peak above the first threshold
and to apply the first torque that prevents foot slap. The advantage of this strategy is that
the phases of the gait cycle that require an application of torque are explicitly identified
through the switches. However, the brain is not as involved in the actual application of
this torque. A more detailed description is provided in the schematic can be seen in
Appendix B.
24
3.3.2
Control Strategy 2
This control strategy will replace the toe-switch with a position sensor that measures the
angle around the ankle. The advantage to this is that it gives a very exact orientation of
the foot which is greatly correlated with ankle torque according to Herr and Blaya's
research. When the system detects an angle near zero radian angle (900 angle at the ankle
according to the conventions of figure 2.5) and a heel switch is activated, it will look for
an EMG value of above the same threshold as the first one mentioned in control strategy
1 (X 1). Once the EMG decreases to below the threshold, it will wait for the ankle to
reach and angle of Oe (around -.3 radians, denoting a slightly obtuse ankle angle), it will
look for the EMG signals of above the second threshold (X2 ) and hold it until the ankle
reaches 0 radians again. A disadvantage of this system, however, is that the patient may
not be able to achieve 0 radians in the foot to active the loop. This is why the heel switch
is necessary; the addition of the switch, however, again reduces the brains interaction
with the system. A more detailed description can be seen in Appendix B
3.3.3
Control Strategy 3
This strategy will take the EMG signals from both the tibialis anterior and the
gastroc/soleus to control the torque of the ankle joint. In this strategy, a positive torque
will be applied when the first spike of EMG in the tibialis anterior occurs and surpasses a
given threshold. At this point the gastroc/soleus is relatively silent. As the EMG signals
25
from the T.A. cuts out and the EMG signals from the gastroc/soleus kicks in, the applied
torque will be removed. Once the second spike in EMG signals of the T.A occurs, the
system will look for it to surpass a second threshold and apply a torque to prevent toe
drag. The torque will be maintained until it disappears and the EMG signals of the T.A.
reoccurs at heel strike. This is the strategy that involves the most user interaction which
is optimal for the re-learning process. However, this strategy is very sensitive to
unwanted noise in the system. This means that a lag needs to be introduced where the
brace is not activated unless a signal is sensed for a period of time. Additional studies
need to be done in order to implement this. Furthermore, the system needs to be sensitive
in detecting EMG signals from the tibialis anterior to differentiate between the two
phases when it is active. A more detailed description can be seen in Appendix B.
Although in theory, control strategy 3 would be optimal to patients trying to relearn
muscle control during walking, it is much more difficult to implement because the peaks
in both the tibialis anterior and the gastroc/soleus are very similar in amplitude, thus it
would be hard to distinguish which torque to apply because the different phases as so
similar to each other. However, since the magnitude of torque does not need to be exact,
it is still a very feasible possibility
4. Future Work
Since this thesis project only covered the preliminary research needed to develop the
ankle prototype of the active joint brace, much further work needs to be done. First,
more specific studies on stroke patients and their EMG patterns need to be conducted
26
under different conditions such as varying speeds and inclines. This can be done at the
Spaulding Rehabilitation Hospital with proper consent. It is important to note that the
control strategies devised in this thesis did not take the time lag between signal detection
and physical output into account. Ideally the system would filter out the EMG noise that
does not correspond with a significant step of the gait cycle. The system would achieve
this by introducing a lag between initial signal detection and torque output. The system
would only apply this torque if an EMG signal is experienced for a given length of time.
The use of switches eliminates the need for this mechanism; however if a future
prototype is designed that uses solely EMG, a time lag is a necessary feature in the loop
to prevent the system from applying a torque to the ankle at unwanted times.
A prototype using one of the above strategies, or perhaps a more involved one,
needs to be developed and tested on the stroke patients to see if it can work. Ideally, the
ultimate control strategy would involve using the profile of EMG signals from the tibialis
anterior to exactly correspond to the ankle torque and angle of the orthoses, however,
since the profile is so uniform, this may be difficult to do. Perhaps with more data
collected from stroke patients, a more defined profile can be established. This would
provide the optimal means of helping stroke patients regain control of their muscles
during walking because it would involve the highest level of feed-back from the brain.
5.
Conclusion
Through doing research for this thesis project, I was able to gain hands on experience in
testing subjects as well as expand my knowledge on drop foot gait, a serious problem
27
facing stroke patients in the world today. The experiments performed and the
background researched yielded optimistic results of a possible ankle-foot orthoses
controlled by EMG signals from the tibialis anterior. All in all, I have found that an active
ankle foot orthosis using electromyography signals is indeed a feasible project, but will
require more inputs than just EMG signals as the elbow prototype of the active joint
brace solely uses. Although this is only the first step in implementing this project, the
work done throughout this thesis will hopefully allow stroke patients to not only
experience greater comfort while walking, but also rehabilitate themselves in the
meantime.
28
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30
Appendix B:
Control Algorithm # 1
Desired Output:
1.) Apply positive decreasing torque at heel strike
2.) Apply 0 torque at full-foot and mid-stance
3.) Apply positive torque at toe-off until heel-strike again
Torque applied at each phase
Phase
1.5
u, 1
elSike
0.5
/
T Of6wng
Mi-Stance/
0.
\~ Full Foot
A1eel Off
* a value of 1 means that a torque is being applied
Inputs:
* heel switch
· toe switch
* EMG from Tibialis Anterior
a.) Heel Strike
Outputs:
* Ankle Torque
EMG: At heel-strike, there is a spike in EMG
signals. This marks the beginning of the gait cycle
Control: at heel-strike, a switch will be activated
that begins the loop. If an EMG of the tibialis
anterior muscle of above a threshold value X is
reached, a positive torque of T1 will be applied to
prevent the foot from slapping down. As the EMG
Heel switch
signal is maintained above the threshold value (XI),
the torque will continue to be applied
Note: the signal will last for approximately 8 ms
XI will be approximately 1.5 IgV
T1 will be approximately 0.2Nm/kg mass of user
b.) Full Foot, Mid-Stance, Heel-Off
EMG: From full-foot to mid-stance, EMG from
the tibialis anterior tapers to close to 0.
Control: At this point, the EMG level will be
below the threshold level Xland thus the system
will apply no torque
31
c.) Pre- Toe-off and Toe-Off
EMG: Another peak of EMG activity occurs in the
tibialis anterior at toe-off
Control: A toe-switch, when decompressed, will
activate the second portion of this loop. When an
EMG of at least X2 is detected, the system will apply
a slight torque of T 2 to help lift the foot until the heel
switch is activated again
Toe switch
Note: X 2 will be similar in amplitude to XI
c.) Swing Phase (Deceleration, mid-swing, and acceleration)
ii
////////
EMG: EMG tapers duringIacceleration and reaches
a period of silence during mid-stance, and
accelerates until it reaches its peak again at heel
strike.
Control: A positive torque will continue to be
applied throughout the swing phase although EMG
will not be above the threshold in order to keep the
foot from dragging on the ground.
Note: The 1stEMG system will kick in again at
heel-strike when the switch is activated
Pros/Cons
Pros
1.) The Brace will know exactly what
position the foot is in
2.) Eliminates possibility that false
spikes in EMG signals will confuse
system
Cons
1.) Less user interaction
2.) Possibility of switch failing to
engage
3.) Easy to implement
32
Control Diagram:
Inputs
Control Algorithm
Output
If Heel Switch (A) is Activated
and T.A. (B) EMG is > Xi,
(A) Heel Switch
If Toe Switch (C) is Activated
and Released, and Heel Switch
(B) T.A .EMG
(A) has not been activated, and
T.A. EMG (B) reaches a
threshold of X2
Er
(C) Toe Switch
If Heel Switch (A) is activated
and T.A. EMG is < X1 or Toe
Switch is activated and EMG
never reaches X2
*k No
Torque
33
Control Strategy 2
Desired Output:
1.) Apply positive torque at heel strike and ankle is near 0 radians
2.) Apply 0 torque through full-foot and mid-stance
3.) Apply positive torque at toe-off when ankle angle is at approximately -.3 radians
Torque applied at each phase
Phase
1.5
1 -l
Strike
-
0.5
M-Stance
0
Full Foot
eel Off
Outputs:
* Ankle Torque
Inputs:
Position sensor (measures 0)
EMG from Tibialis Anterior
Heel Switch
a.) Heel Strike
Too Ofving.,
EMG: At heel-strike, there is a spike in EMG, this
also marks the beginning of the gait cycle
Control: at heel-strike, the position sensor will
recognize an angle near 0 radians and a heel switch
will be activated. At this point, if an EMG of the
tibialis anterior muscle of above a threshold value
X1 is reached, a positive torque of T, will be applied
Positi(
Heel switch
b.) Full Foot, Mid-Stance, Heel-Off
to prevent the foot from slapping down. As the
EMG signal is maintained above the threshold value
(X1), the torque will continue to be applied
EMG: From full-foot to mid-stance, EMG from the
tibialis anterior tapers to close to 0.
Control: Since at this point, the EMG level will be
below the threshold level Xland thus will apply no
-- -
torque
34
c.) Pre- Toe-off and Toe-Off
EMG: Another peak of EMG activity occurs in the
tibialis anterior at toe-off
Control: The position sensor will pick up the ankle
angle as being 01 (- -.3 radians meaning it is slightly
Position Sensor
obtuse) It will then look to detect an EMG of above
the threshold X2 . At this point, the system will
apply a slight torque of T2 to help lift the foot until
//////777-
the heel switch is activated again.
Note: X 2 will be similar in amplitude to X1
d.) Swing Phase (Deceleration, mid-swring, and acceleration)
0
/7//////
EMG: EMG tapers during acceleration and reaches
a period of silence during mid-stance, and
accelerates until it reaches its peak again at heel
strike.
Control: A positive torque will continue to be
applied throughout the swing phase although EMG
will not be above the threshold in order to keep the
foot from dragging on the ground until the ankle
angle reaches 0 radians again. At this point, the
system will again try to detect an EMG of above XI
Pros/Cons:
Pros
1.) The Brace will be able to start the
sloop via switch and position
Cons
1.) User may not be able to hold ankle
at 0 radians before heel strike, thus
2.) Eliminates possibility that false
heel switch is the only input
spikes in EMG signals will confuse
system
35
Control Diagram:
Inputs
Control Algorithm
Outputs
If Position Sensor (A) angle near
(A) Position
0 radians, heel switch is activated
(C) and T.A. (B) EMG is > X 1
Sensor
If Position Sensor (A) detects
angle near 01 and T.A. EMG (B)
(B) T.A .EMG
reaches a threshold of X2
(C) Heel Switch
If position sensor is at an angle
not near 0 0l radians and EMG is
< X or X2
|No
Torque
36
l
Control Strategy 3
Desired Output:
1.) Apply positive torque at heel strike when EMG of tibialis anterior is above X1
2.) Apply 0 torque at full-foot and mid-stance when EMG of gastroc/soleus is above
Yl
3.) Apply positive torque at toe-off when tibialis anterior EMG is above X2 and
continue to apply that torque when EMG from gastroc/soleus is above Y2
Torque applied at each phase
Phase
1.5 --
1
-
0.5
tstike
Mi-tance
Full Foot
Inputs:
0
..
T O...wing
.
EMG from gastroc/soleus (calf)
EMG from Tibialis Anterior
a.) Heel Strike
:%el Off
Outputs:
* Ankle Torque
EMG: At heel-strike, there is a spike in EMG of the
T.A. and very little EMG in the calf. This indicates
the beginning of gait cycle
- - --
Control: at heel-strike, the if an EMG of the tibialis
anterior muscle of above a threshold value X is
reached and the calf muscle is relatively silent
(below Yo,) then a positive torque of T1 will be
applied to prevent the foot from slapping down. As
the EMG signal is maintained above the threshold
value (X 1), the torque will continue to be applied
-
b.) Full Foot, Mid-Stance, Heel-Off
.to
EMG: From full-foot to mid-stance, EMG level the
tibialis anterior tapers to close to 0 and the calf
muscle sees a relatively large amount of activity
Control: Since at this point, the T.A EMG level is
relatively silent and the calf is relatively active, the
system will apply 0 torque if EMG from the T.A. is
< Xoand EMG from the calf is > Y1
37
c.) Pre- Toe-off and Toe-Off
EMG: Another peak of EMG activity occurs in the
tibialis anterior at toe-off as the calf experiences
another quiet moment.
Control: The system will look to detect an EMG of
above the threshold X2 in the T.A. and below Yoin
the calf. At this poiint, the system will apply a slight
torque of T2 to help lift the foot until the heel strikes
////////7-
again.
d.) Swing Phase (Deceleration, mid-swing, and acceleration)
ii
/77/7/
EMG: EMG of T.A. ttapersduring acceleration and
reaches a period of sil,ence during mid-stance, and
accelerates until it realches its peak again at heel
strike. The EMG of the calf is relatively silent.
Control: A positive torque (T2 ) will continue to be
applied throughout the swing phase when EMG of
the T.A. hits X 2 and applied until and EMG is at X1
again. This will keep the foot from dragging on the
ground. When the EMG of the T.A reaches X1
again, it will again apply a torque of T.
Pros/Cons:
Pros
Cons
1.) EMG is the sole contributor in
controlling ankle torque. This
means that the user has the most
control of the system. controlThe
1.) False spikes in EMG can cause the
brace to output a torque at an
inappropriate time
Brace will be able to start the sloop
via switch and position
38
Control Diagram:
Inputs
Control Algorithm
Outputs
If T.A. EMG (A) is > XI and G.S.
EMG (B) is < Yo
(A) T.A. EMG
If T.A. EMG (A) is < Xo and G.S
EMG (B) > Y 1,
No
Torque
(B) G.S. (Calf)
.
EMG
If T.A. EMG (A) is > X2 and G.S.
EMG (B) is < Yo
0
T2
39