From Kinematic to Energetic Design and Control of Wearable Robots for Agile Human Locomotion


So, a large part of what you’re going to see
here was worked on at UT Dallas, just want to give that little disclaimer but we’re very
excited to be here at Michigan with electrical engineering and the Robotics Institute, and
so we’ll be doing a lot of cool stuff here over the coming years. So the motivation behind
my talk is challenges to human mobility. And in particular, we’re very interested in
challenges faced by lower limb amputees, so there’s about a million Americans with lower
limb loss as well as general mobility limitations that can be caused by stroke, advanced age,
Osteoarthritis, lower back pain, musculoskeletal disorders, et cetera et cetera. In fact, it’s
pretty staggering numbers about one in eight adults in the United States have some sort
of mobility disability. And so, the features of gait experienced by these individuals include
more frequent falling, more metabolic energy consumption walking from point A to point
B. And slower walking and so forth. And so there’s certainly a lot of room for
improvement of conventional prosthetic and orthotic devices to enable greater mobility
in these populations. And so our approach, my group likes to think of legged robots as
a cycle of life for achieving these goals and so we like to borrow and develop concepts
in the area of legged robots, autonomous robots like Cassie and translate those into powered
prosthetic legs, learning how to cooperate and synchronize to the human with the robotic
device as well as in exoskeletons trying to augment an existing limb that may have some
sort of weakness or deficit to it. And then through the process of learning how to apply
these control methods to humans we actually learn a little bit more about how to make
more human like autonomous walking as well. So we can make these robots walk in a more
natural lifelike manner. So that’s the cycle that we embrace. And so the organization of
this talk is as follows. I’m going to start by discussing our efforts in user synchronized
kinematic control of agile powered prosthesis. And so when I say kinematic control I mean
controlling joint position and velocity. Then we’ll talk about energy aware actuator design
in order to enable greater mobility in these devices. And then we’ll close with discussions
on energetic design and control for partially assisted exoskeletons that only provide some
support for an individual, not complete support. And so you’ll see how we’re kind of going
from this kinematic control paradigm towards this energetic control paradigm in this talk. So this is the state of the art in the field
of powered prosthetic legs. At least it was when I entered the field. And so the idea
is you look at the gait cycle and you notice it has several different phases of gaits which
are indicative of certain behaviors such as heel contact, push off during late stance
and then the swing phases. And so essentially the way that control engineers were designing
the powered devices for amputees in the past is to design a different controller for each
of these behaviors. So each of these boxes may have a proportional
derivative controller in it. Sometimes in the field we call it an impedance controller.
And then there would be some switching rules to go from one mode to the next. Now the problem
is that all of these things are subject dependent. So I walk differently than Rom, Rom walks
different than Gray, and so on. And so we actually end up having an explosion of parameters
that require patient specific tuning that is not intuitive because these things may
not necessarily have physical intuition behind them, especially the switching rules and some
of the parameters depending on which control methods you’re using, and it takes a lot of
time. And the problem gets even worse when you think
about variable activity locomotion. So typically you’d have a higher level state machine where
you’d have let’s say five different modalities you can use and each one of those things would
have its own lower level cycle here with its own parameters and so what ended up happening
is you’d spend, and I’ve done this during my post doctoral training, you’d spend hours,
sometimes days with one person trying to optimize these parameters to make them walk just right. And so it’s not a clinically viable solution,
right? Medicare, insurance, whatever is not going to pay for five hours of tuning with
a team of engineers. And it’s also not necessarily very accessible to people who haven’t had
training in control theory such as clinicians. And so that’s the challenge. And so I took
inspiration from the field of robot walking to address this challenge. Now, this is a
robot that was designed by Eric Westervelt who I believe is a Michigan alum, right? And
then later adopted by Jim Schmeideler at Notre Dame. And so Ernie, has he’s called, you’ll see
in this video is controlling its hip and its knee angles as a function of its hip progression,
okay? So it’s measuring the progression of the center of mass of the hip. And so as the
robot’s pushed forward, its leg joints will track a pattern in the forward direction and
when it’s pushed backwards it tracks back in a backward direction. And then when you give it a nice push it can
actually fall into a nice self-fulfilling walking cycle were actuation energy injection
from the leg joins cause more forward propulsion which then keeps the joints moving forward
and so on. Now this idea actually initiated from Jessy Grizzle here at Michigan. And this
is the notion of a phase variable, okay? So it’s a time invariant parametrization of the
joint trajectories. So let’s say that you want to track this kinematic pattern, so this
is joint angle over time. But you want to track it in a time invariant manner so that
you can, so that the robot can always keep up with its progression, right? So that the
leg is never too far behind or too far ahead. And so what you do is you try to find, at
least during steady walking a monotonically increasing signal that you can measure, which
we call a phase variable. So this could be the hip position going forward
in space over time. And so then if you measure that phase variable you can then parametrize
your kinematics as a function of that. And the beauty of this is that if you’re walking
and then suddenly someone grabs you and stops you from moving, then your joint angles which
are being controlled as a function of this phase variable will stop, right? Synchronizing
with your progression and then as soon as you let go of the robot then it will keep
moving again, right? And so you end up seeing this … so you see
this little phase shift here that’s happening over time but because we’ve represented the
trajectories as a function of a phase variable it’s as if nothing happened, right? Because
you never left the desired profile, you just simply slowed it down and then restarted it.
Okay? So that’s the idea of the phase variable. And it’s turned out to work really well for
walking robots. These things can walk and run and climb stairs and so forth. You can
push them and they’ll recover. And that’s still going on here at Michigan with Jesse
and Rom’s groups. So we wanted to use this for controlling a
prothetic leg. But that then motivated the question of how can we measure a phase variable
that represents the progression of human walking? And so, my former PhD student, Dr. Villarreal
came up with this concept of the thigh phase angle which is a strong predictor of the distal
joint patterns. And so the idea here is that if you’re measuring the angle of the thigh
with respect to gravity, so this is a global and inertial measurement then you look at
it over time, it has a sinusoidal trajectory. And so when you have a sinusoidal trajectory
like this you can build a phase orbit. This orbit in the phase portrait actually gives
you a clock, a sense of timing which is actually time invariant because it’s based on the position
of the thigh moving over time. And so in this study, Dr. Villarreal even
showed that it’s, the distal joint patterns are still highly correlated with the progression
of the thigh even across perturbations. So if you tripe someone, at least with a reasonably
magnitude perturbation you can still predict distal joint patterns with this measurement.
Now if you really trip someone and you’re to the point of nearly falling over, then
there might be some reflexes that kick in that might not be well explained by this.
But at least subtle perturbations are well captured by this idea of a phase variable. And so we implemented this concept in our
first leg design, this is our gen one robotic leg. It wasn’t designed for looks, okay? So
it’s not the prettiest leg but it was designed, it was our first attempt at building a wearable
robot. So the key features here is that it has an inertial measurement unit at the top
of the knee joint which gives you the orientation of the thigh, okay? So it has an accelerometer
and gyro in there. And it has a knee actuator and an ankle actuator. And I’m pointing out
that it’s highly geared, I’m not trying to brag about that. I’m just pointing out that
it’s highly geared because that’s going to become a problem later and I’m going to talk
about how we’re going to solve that as well. Okay. Definitely I’m not bragging about that. And this was work by Dr. Quintero who is now
at SFSU. And so having this implementation in the prosthetic leg, we also need to figure
out how are we going to define the kinematic patterns that the leg will follow. And so
what we do is we start with able bodied, normative kinematics which we then reparametrize again
as a function of this phase variable. But then we allow the clinician to visually augment
that trajectory. So you can imagine that the patient puts on the robotic leg, starts walking
with it. But because every gait should be user specific, should be unique the clinician
may observe okay, they need a little bit more push off during late stance or maybe a little
bit more inflection during swing. And so then they can grab these control points
and manipulate the trajectory and then it re-encodes the function of the phase variable.
So essentially we have these functions of phi which is the phase variable and that then
leads you to an error vector where theta is the actual truly, the measured angles of the
leg. This is the desired angle of the leg, given the phase. And so you have an error
vector. And then the simplest way to control the leg is to control torque based on a proportional
derivative control law, right? That’s the first thing we all learn. And so we do that, it works. But there’s also
more rigorous formal methods such as hybrid zero dynamics and Dr. Martin has a really
nice paper on that theoretical approach but we’re not going to use the theoretical approach
in the experiments just because it’s difficult to model these things and it could result
in other challenges. So, and the experiments to follow were using partial derivative control. All right, so this was our very first amputee
subject. The very first time anyone had put on our, any patient had put on our leg and
this was just him acclimating him to it. And we were just recording the video while we
were shooting the breeze with this subject. And I highly recommend doing that, record
anything just in case something interesting happens, even if it’s an outtake, it could
be useful. And so here, we’re just chatting with him
and then we notice that he became kind of comfortable with the leg and started kind
of moving his weight, shifting it forwards and backwards. And then we asked him so what
are you doing here? And he was like well I understand that the foot’s going to be where
I expect it to be based on my hip motion so I can actually trust that it’s going to be
behind me when I need it to be. So he’s not looking at it, he can’t feel it,
right because he has no proprioception. But he has learned this mapping from his hip motion
to the prosthetic foot position. And so essentially he was able to then volitionally to a degree,
it’s not true volitional control but he’s at least able to control the foot position
where he wants it to be. And so that was pretty nifty. And this was working in collaboration
with Susan Kapp. And so here’s a sequence of experiments demonstrating
how walking with a conventional prosthesis which is what we’re going to see first. This
is the leg that this subject uses every day and then comparing that with walking with
our powered leg. He’s actually a pretty good walker with it. So it’s a little loud and
we’ll talk about that later. That has to do with the high gearing and the use of a ball
screw. Now walking backwards is hard because with
this conventional leg he has no control over foot position. But against with the powered
leg and this controller he can trust that the foot will be where it should be based
on his hip motion. So Bobby it looks like he’s sort of snapping
the leg into place as he’s swinging backwards? Yeah, that was because we have … I’ll talk
about it in a second, yeah. Yeah, but you can go forward and backwards. And we can also
do things like crossing obstacles which the controller was not explicitly designed for
that. But we were able to, based again on this mapping from hip motion to foot position.
Okay, so Rom, the question about the snapping. So we, I’m kind of trivializing things a bit
here. We have some safety mechanisms in place so that the leg doesn’t for example switch
from stance to swing erratically or when you don’t want it to do it. So in order for the
leg to start flexing, before you actually take a step forward we require it to have
a certain amount of motion to actually engage we call it backwards stated. But it’s all
based on the phase variable, though. It’s just that there’s some supervisory logic that
prevents it from switching too quickly. This one was a little more entertaining, so
we got a pretty good soccer kick out of this. Whoa, nice. So … better than I can do at least. All
right. So, and so here’s steady walking. This participant does keep his hands on the hand
rails but this might have to do with the actual design of the device. So trust me on that
one. So you get more normative energy injection
into the gait cycle. And so on particular, for example while we’re walking at multiple
speeds, normatively it would be the knee work gets more negative as you go faster because
your knee is doing more braking as you walk faster. And ankle work would go up because
your ankle is doing more propulsion as you go faster. And so we get that, whereas with
the passive conventional leg, this would be more or less level and it wouldn’t be nearly
as high, either because it’s a net zero mechanical work paradigm, right? I mean these prostheses
conventionally do have springiness in them so they can store and release energy but they
can’t inject energy. And so this results in more normative biomechanics
and that’s still a somewhat theoretical concept but this is where things really matter. So
we look at compensations of the amputee participant. And there are three that are very common in
amputees. So there’s hip circumduction where they kind of rotate their hops so that their
foot doesn’t drag the ground. There’s hip hiking which is exactly what it sounds like,
again to prevent the foot from dragging on the ground. And then on the sound side they
have ankle vaulting where they kind of push off too early to again provide ground clearance.
And what we see compared to the passive, so the dotted line is the passive and the powered
is the blue. We see actually a reduction in these compensations. They’re not completely eliminated and we wouldn’t
expect that after one experiment but we do see an almost immediate reduction in them.
So this is relevant because these compensations are metabolically costly. So also they tend
to wear the joints, the intact joints fast. So amputees tend to have arthritic hips and
arthritic knees in the sound side and so forth because they are overusing their sound limbs. Okay, so at this point, I described a continuous
sense of phase, but we still have a discrete sense of task in the sense that we would have
to know what the task is, the activity in order to change the kinematic pattern of the
leg. Right? Because so far we’ve just made the kinematic pattern time and variant but
there’s still a profile there that we’re tracking. And so in order to do different things like
walking on inclines or stairs, you’d have to change those kinematic profiles somehow.
And so you could do that with a classifier. But my goal and the topic of our current R1
project is to have a continuous sense of activity, of task in order to allow navigation over
continuously varying speeds, inclines and other types of transitions to stairs and so
forth. And so the goal here is that we can define
a multidimensional activity space where for example, slope, the incline can be any real
number within some range, speed, can be any positive real number within some range. And
then for example stairs. Are you on a staircase? One. Are you not? Zero. Or are you transitioning
between them and that would be some number in between zero and one. And then our goal
is to have a kinematic model that given a point in this activity space will then give
us a prediction of what the kinematics should look like. Okay? And so you see here that for these three different
points in the activity space we have three different trajectories that we’d want to track
in the prosthesis. So that’s the goal. And so we have some preliminary work in this direction
where we’re trying to use samples of these … of this activity space. And so in this
study, our activity space is just variable inclines and speeds. And so we do a motion
capture study where you can, where we sample a certain number of these speeds and a certain
number of these inclines and combinations of the two. But we can’t possibly sample the
continuous range, right? That’s not possible. And so we need a model that can predict in
between those samples. And so in order to avoid over fitting, we
wish to use regularization to find a model that explains the underlying features in the
data in a way that can allow us to predict activities that you don’t actually have samples
of from the motion capture study. Okay? And so for example, this function B here would
be a function of the phase variable, okay? And that would be, for example that would
come from a set of trigonometric polynomials, and C would be a function of a vector of describing
the inclination, and speed I guess. I projected away speed. But speed would also be in there
and so essentially we use group L1 regularization, which is a machine learning method for trying
to induce sparsity in the model. Okay? And so we’re able to show that this is actually
a stronger predictor of unknown, of untrained activities compared to for example linear
interpolation which is exactly what we would all try first, right? So essentially our error,
our prediction error is statistically significantly better using our sparse model than just using
linear interpolation. And so the argument here is that then we can
reliably predict the desired kinematics for any incline or speed that we can detect from
the environment. Okay. Now detecting the incline and speed is a challenge in its own right,
which we’re working on. But this is the idea. And so, we’re currently working on modeling
and control of again, these continuously varying activities. But we also want to consider stairs
and maybe one day running. The ultimate goal of course having a control system that can
perform all of the activities of daily living in a seamless manner. And so we have a way of doing that theoretically
at least in terms of transitions between a certain set of activity modes, like stairs,
flat ground, sitting. And then each of those modes might be parametrized by an incline
and a speed that would then allow us to have this continuously varying set of activities.
So this is an early project that’s still ongoing. However, in order to actually achieve this,
we’re going to have to address the limitations in the hardware that some of you have already
pointed out. The hardware is loud, the hardware is heavy, it’s got umbilical cords attached
to it. And it’s very stiff. When we use these highly geared transmissions, it makes the
joints very stiff in the sense that they’re not back drivable. You can’t … they don’t
naturally swing with dynamics and so the motor has to literally do everything for the robot
to move. And that’s not how human joints work, right? And so this is where now we’re looking at
using better hardware such as prosthetic legs that actually think about how they use energy.
In particular, the open source leg was designed by Elliot Rouse here at Michigan and we’re
one of the lucky early recipients of it. And it has a series elastic actuator. So essentially
you put a spring in series between the gear box and the load which provides compliance
to impacts. It also allows you to store energy and release it. And so it has the potential
to reduce energy consumption in that manner. It also provides some back drivability and
lots of different potential benefits. But if we’re going to be using this for controlling
variable activities, that begs the question of how should we select stiffness for the
series elastic actuator? And so in Elliot’s design he has six different
selectable stiffness options you can choose, but you can’t pull them out mid gait cycle,
right? You can’t change the stiffness as the leg is being used. And so, at least not yet.
And so the question is how do we select an optimal stiffness that will allow energy efficient
electrical energy consumption as well as satisfying actuator constraints? For example you don’t
want to bottom out the spring, because then it becomes a rigid actuator as soon as the
spring bottoms out. And also limitations like the peak torque
and velocity of the motor and so forth. And so that motivated a different NSF project
which is in its early stages where we’re trying to have a method for robust design of series
elastic actuators. And so it turns out Edgar, Dr. Boulevard is here in the audience somewhere.
There were go. So was a PhD student with me and now a post-doc with me. So Edgar realized
that you can express the energy consumed by the motor as a quadratic function of the spring
compliance. So that’s the inverse of stiffness, right? And so, what is energy consumption? Well it’s
not the energy to move the load because that can’t be reduced. That’s just first principals.
But it includes energy losses due to a viscous friction. Okay, so friction results in loss.
And joule heating. So that’s the heating that comes from Joule’s law, the windings in the
motor. You put current through it and it heats up with, scales with current squared. And so we can potentially reduce those things
through the design of the spring. And so, in Edgar’s analysis he was able to show that
you can actually express this as a quadratic function, energy consumption. And in the case
of a linear spring, meaning a constant stiffness, that means that you have a convex function
of energy over compliance. So you can very easily find the optimum that
minimizes energy consumption, right? Now if you’re thinking about a non-linear spring,
well then X would be a trajectory of compliance that has certain constraints on it. For example,
you want your spring to have a monotonic relationship between displacement and torque, right? So
in order for it to be conservative. So we have inequalities that correspond to actuator
constraints and feasibility constraints. And we can also introduce uncertainty. Because
this is a convex problem, a convex optimization problem there are tools available for robust
optimization that can handle uncertainty. For example, our uncertainty in the case of
a legged robot would be for example the mass of the human user. We don’t really know how
much each person weighs or if they’re wearing a backpack. You put a bunch of books in there
to go to class or whatever. Or iPads these days, right? And the position might be a little bit uncertain
right? Because the environment might result in differences in kinematics and so forth.
The efficiency of the actuator itself might be uncertain and there could be all sorts
of unmodeled dynamics as well. So now this allows us to again minimize energy consumption
and also guarantee that we still satisfy actuator constraints as the activity might change. Now there’s another approach that we’ve been
investigating which is not using series elasticity but instead using quasi direct drive actuators.
This is a different way of achieving compliance, it’s just that it’s not compliance through
a spring, it’s compliance through a lack of inertia in the actuator. So the actuator does
not have significant dynamics of its own. So you can just almost freely rotate the motor
from the load. It’s called back driving, all right? So the way we do this is we have to
have a very low gear ratio because the inertia reflected, the inertia of the rotor, of the
motor reflected through the transmission scales with the square of the gear ratio. And so the whole game comes down to getting
this gear ratio down as small as possible in order to reduce the inertia of the actuator.
But then when you reduce the gear ratio then you have to deal with making sure you have
sufficient torque coming out of the actuator as well. And so that’s where the high torque
motors come in. We have to use high torque motors that typically are pancake motors because
they have higher torque density. And these two things combined allow us to do some pretty
nifty things with our Gen-2 leg. So, you can see it’s very back drivable. This
is powered off. It requires one to three newton meters of torque to back drive the knee or
the ankle, so it’s very back drivable. And the goal there is to allow more dynamic motion
and also energy harvesting. Because when this leg is doing negative work, when its braking,
the motor is doing that. And that results in a charge going to the battery to prolong
battery life and so forth. Also, there’s fewer moving, meshing parts and those parts are
moving slower than a highly geared transmission which results in less noise. So we can finally
address that problem with the lawn mower sounds coming from the prosthetic leg. All right, so here’s the video I promised
you where the subject will wake without the hand rails at some point. So here it has very
compliant impacts with the ground because there’s almost no inertia at the joint. Of
course the limb itself has inertia. And it’s also able to have a very fast push off to
swing transition because it has a very high bandwidth. It’s one of the benefits of a quasi
direct drive actuator is very high bandwidth. So you can go from high force at push off
to high velocity at early swing in almost no time. The battery is right here. Yeah.
And it’s enough for about I believe, it depends on walking speed but it’s enough for around
5000 steps or more. Oh, 50 decibels adjusted is about the noise level of a household refrigerator,
so very quiet. Something that at least we find acceptable at home, right? And again
we’re not stuck to treadmill walking. And one of the other benefits of the quasi
direct drive actuator is again I mentioned earlier energy regeneration and energy sharing
between the joints. So when the knee is doing negative work and the ankle is doing positive
work they’ll share that energy rather than the battery having to provide all of it. And
that resulted in a specific power of about 50% less specific power than the state of
the art Vanderbilt prosthetic leg. So we’ve cut energy consumption in about a half. So in the last part of this talk I want to
switch topics to exoskeletons. However, still very related to the latest leg I showed you
in the sense of its actuation paradigm. And so the state of the art for exoskeletons is
they’re primarily designed for spinal cord injury applications where the human can do
very little to move their own limbs. And so the exoskeleton has to do everything. And
so in this context it makes sense to design very stiff actuators so that the weight of
the human subject doesn’t drive the actuator, the joints so they don’t collapse, right? However, that stiffness means that these are
not very useful for working with stroke or anyone who has voluntary control of their
limbs. And so, where my lab is going with this is going towards a more partial assistive
paradigm where instead of having stiff actuators, rigid actuators we have back drivable actuators
and we use quasi direct drive designs to achieve that. And also, we don’t want to use kinematic
control in the context of partial assistance because again even with a back drivable actuator,
you need the controller to not be creating forces that oppose the human’s intention,
right? So if you’re controlling kinematics then the
human must still follow the kinematic trajectory that the robot tells them to follow. And so
that’s why we’re heading towards energetic control objectives. And so our patient populations
of interest are stroke, OA, advanced age and overuse injuries. So as I hinted at we have
this quasi direct drive paradigm where we have a 24 to one gear ratio in this particular
exoskeleton which has a powered knee and a powered ankle. Yet, we’re still able to produce
large torques at each actuator, so about half of what they would need in everyday life.
So this is actually still a quite powerful exoskeleton in terms of partial assistance.
But only about one newton meter of back drive torque. And it appeared on the cover of IEEE
Control Systems Magazine a couple of years ago. And so in terms of how we control it, we are
using a method called energy shaping, otherwise known as Lagrangian or Hamiltonian shaping.
And the idea here is that we model the human body as a Lagrangian. Okay, the Lagrangian
is a scalar function that is kinetic energy minus potential energy. And the reason that matters is because if
you put it into the Euler-Lagrange equations it spits out the equations of motion, which
we see here. So the m for example is the matrix of masses and inertias and how they depend
on joint angles. C is the matrix of Coriolis centripetal terms and g is the vector or gravitational
terms. And we have orthosis torques which of course
influence the dynamics. And so in energy shaping, at least the way we’re using it, we are designing
the control input, u, such that when you close the control loop, the close loop system dynamics
behave like a different mechanical system, okay? So now this mechanical system corresponds
to a different Lagrangian where mass and inertia have been reduced. That’s the idea. We use the control torques at the orthosis
joints to reduce the perceived mass gravity inertia of the human body so that they can
use less muscle effort to move their limbs and to fight gravity and so forth. So this
is the idea. And in the case of under actuation which we do deal with here, this is challenging.
There’s something called the matching conditions that need to be satisfied to show that there
exists a control law that gives you this closed loop system. And that’s not trivial, it’s
a set of partial differential equations. But we have some clever ways to deal with
that and Jinping is in the room somewhere and working on that hard. And so here’s a
demonstration of our knee and ankle exoskeleton doing energy shaping on the human user. And
you can see that he’s able to sit, stand, walk freely. Now this is an able bodied user,
so he could do this normally. But the fact that the exoskeleton isn’t stopping him says
a lot. Okay, because it’s very back drival, he’s
able to continue to be in control of his movements while being supported by the exoskeleton.
And so we’ve done some analyses of this. We look at EMG activation. VM means vastus medialis
and we are able to show that with the assistance, the active mode, so blue is the EMG activity
when the exoskeleton is on and powered on we see that it’s lower than the case of bare,
without the exoskeleton and passive is wearing the exoskeleton but without the motors on.
So we actually see a pretty substantial drop in the EMG activity of this particular muscle.
And red is the torque showing that it’s doing something, to do that, right? So this is for sitting, sit to stand and we
see also promising results for walking. Where again we see in blue, this is the EMG activity
of the soleus muscle which is reduced during, especially during push off with the assistance.
Okay, so this is my last exoskeleton I want to talk about. And so this is a powered knee
only and it’s a much smaller scale exoskeleton than the one I showed you earlier. And so
this is meant to be a conservative treatment for OA and also to prevent lower back strains.
So for example when people have to lift things repetitively in warehouses, in the military,
assembly lines, et cetera. We want an exoskeleton that can assist that so that they don’t fatigue
and then use their back in an unsafe manner. And so here because we’re primarily targeting
individuals who are more able bodied, who have very minor impairments if any at all,
we took this to the next extreme where we have only a seven to one gear ratio. And in
order to reduce the gear ratio that much we had to come up with a custom design for an
electric motor that can product higher torque for longer periods of time. And so in order
to do that we use encapsulated windings which have a more efficient heat transmission so
that the heat around the windings of the motor get distributed to the environment in a more
efficient manner. So that way we can have a higher continuous torque. So this is less than half a newton meter of
back drive torque and up to 20 … now I should update this. Now it’s up to 25, right Chris?
25 Newton meters because we improved the magnets. Okay, so this just demonstrates it’s very
easy to move around with this thing. And one of the applications is assisting stair ascent
because that’s a potential home use application, especially for advanced age. And he’s the
lifting and lowering experiment that Nikhil conducted where we have a … how heavy was
this? 20 pounds? Yes. 20 pounds. And we have a force plate to make
sure that the subject isn’t biasing one leg versus the other and then we’re recording
EMG activity of multiple muscles but we’re only going to show one of them. And so this is bare mode, this is just a baseline.
This is passive mode, so wearing the device but it’s powered off. You see very minimal
difference which is actually kind of good in itself, it’s very back drivable. And this
is active mode. And blue is the EMG, red is the torque coming from the exoskeleton. And
so you see that EMG has dropped. And this is the rectus femoris, yes. So. All right, so this a clear picture of this
plot. So you’re able to see again a reduction in the muscle activation. And the goal again
is to prevent fatigue so that proper lifting form is maintained for longer. So in closing
we have several ongoing studies that are more towards clinical outcomes so we’re looking
at functional outcomes for stroke subjects, assessing muscle tension and posture during
lifting, lowering and carrying and assessing muscle tension and pain in knee OA subjects.
We have enrolled one OA subject but the results, we’re still looking at the data. We haven’t
really processed it yet. And then a very quick overview of some other
projects and I’m sorry that I’m missing some people from this presentation but we have
several other collaborative projects back at UT Dallas as well as Virginia Tech and
UT Arlington. So in closing I just want to recognize the contributions of my lab members,
and this is just a subset of the lab. But this was the last photo we took in Dallas
before the big move and then the funding agencies NH, the NSF and the Burroughs Welcome fund.
So I’m happy to answer your questions.

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