Hugh Herr: Alright, welcome everyone to Cameron Taylor's PhD thesis defense.
My name is Hugh Herr. I'm Cameron's primary research advisor and I will
Hugh Herr: quickly introduce Cameron before he begins his thesis defense presentation.
I am joined by Cameron's other thesis committee members, Tom Roberts from Brown University
and Joe Paradiso, from the MIT Media Lab. Hugh Herr: Before I introduce Cameron,
a few housekeeping items. I'll have everyone know this is the very first ever PhD defense
via Zoom in the history of my research group. Hugh Herr: So Cameron, you should be very honored.
Hugh Herr: So instead of mentioning where the bathroom is located or reminding everyone to
silence their iPhones. I will only say to please mute your Zoom audio.
Hugh Herr: A little bit about the structure of the defense. First
Cameron will present his work, Hugh Herr: for 30 to 40 minutes.
Once the defense presentation has been completed, we will begin a question and answer period
that will be open to the public. Hugh Herr: For the Q&A session please type
your question into the chat window in Zoom. I will then read your question out loud for all to hear
before Cameron can answer. Hugh Herr:
Following the public Q&A session, the public aspect of the defense will conclude and Cameron's
PhD committee members will meet in a private Hugh Herr: on in private on a separate Zoom call
to discuss the thesis and defense. After what we hope to be a short deliberation
of about 15 minutes we will join the public Zoom line once again. So please remain online.
Hugh Herr: Okay, now for the short intro, Cameron received his undergraduate degree from Brigham
Young University in 2014 in electrical engineering with a minor in mathematics.
Hugh Herr: He then joined the MIT Media Lab for its graduate work.
He completed his master's degree within the Biomechatronics group on the optimization
of a novel transverse flux motor topology. Hugh Herr: For his PhD Cameron continued
conducting research within the Biomechatronics group,
Hugh Herr: and also continuing within the realm of magnetics, but now addressing a new problem,
the problem of tracking small magnetic spheres implanted within biological tissue which he
will now tell us about during his presentation. So without further ado, Cameron, please take it away.
Cameron Roy Taylor: Thank you, Hugh. Cameron Roy Taylor: Thanks for that introduction.
Cameron Roy Taylor: Thank you everyone for being here.
Cameron Roy Taylor: Briefly, let me just mention that I recommend using the side by
side view when viewing this presentation, which you can access in the Zoom settings
at the top of the screen. Cameron Roy Taylor:
I'm so excited to share with you the technology that we've been working on over the last several
years, which we called magnetomicrometry, and which we plan to use to deliver intuitive,
skillful control over bionic prostheses Cameron Roy Taylor:
We've developed this technology to meet an immense need in prosthetic control.
To control a prosthesis, on one side of the spectrum there exist fully non-invasive
technologies such as electromyography, Cameron Roy Taylor: ultrasound,
and mechanomyography, which all reside outside the body, but without exception,
have poor unstable signal quality. Cameron Roy Taylor: On the other side of
the spectrum are highly-invasive technologies such as sonomicrometry, micro-channel arrays
and intrafascicular electrodes, Cameron Roy Taylor: that provide
improved signal quality, but are expensive to implement, require delicate surgery,
and need careful post-operative care. Cameron Roy Taylor: What the field is missing is a
low-footprint, minimally-invasive tissue interface that can accurately monitor muscle actions.
Cameron Roy Taylor: Here's an example of the state of the art in electromyography control.
Cameron Roy Taylor: This is the accumulation of years of work. So I don't want to downplay
how far we've come, or how marvelous this is. But let's take a step back and evaluate
how much room there is for improvement. Cameron Roy Taylor: As you watch this video
please pay specific attention to the difference in ability between the two hands on this bionic man.
Cameron Roy Taylor:
Okay, so as you saw in this video, we're not there yet. We need to do better to emulate biology,
and to do so we're going to need new tools better than any that are currently available.
Cameron Roy Taylor: We now present magnetomicrometry, a new technology I
developed for tracking tissue lengths and speeds. Cameron Roy Taylor: It uses implanted magnetic
beads to wirelessly track the tissue lengths, making it the first ever minimally-invasive
real-time muscle tracking technology. Cameron Roy Taylor: And it uses simple
tracking hardware, making it economical, compact, and portable. With this system
we've demonstrated real-time muscle length tracking with submillimeter accuracy and with
precision to within a tenth of a millimeter. Cameron Roy Taylor: We'll start by discussing
how robotic processes are controlled, and we'll discuss how we plan to use magnets to improve
that control beyond what can be achieved with currently available technologies.
Cameron Roy Taylor: Well then dig deep into our advanced magnet tracking techniques
and we'll explain the mathematics behind our high speed tracking strategies.
Cameron Roy Taylor: Finally, will present the results of our preclinical work in turkeys,
discussing biocompatibility, accuracy, and long-term implant stability. So let's begin,
focusing in on the prosthesis itself. Cameron Roy Taylor: First let me say how
glad I am how cool it has become to have a prosthesis over the last several years.
Cameron Roy Taylor: Our science fiction writers, filmmakers, news agencies have all
been driving society to want advanced bionics, and influential moments like the one shown here
inspire us and move us forward. Cameron Roy Taylor: So
with this inspiration, let's review where we're at right now with advanced prostheses
as well as our ability to control them. Cameron Roy Taylor: We've made significant
process, progress, recently in developing robotic prostheses. State-of-the-art devices like this
commercial robotic hand shown here have many degrees of freedom and are strong, durable tools.
Cameron Roy Taylor: But these devices fall short in their ability to provide full, intuitive,
human-like control to the amputee. We're just not there yet. And here's why.
Cameron Roy Taylor: The commercially available method for controlling
advanced prostheses uses electrodes, shown here and here, which are bare metal conductors
placed on the surface of the residual limb. Cameron Roy Taylor:
These electrodes measure a signal called muscle activation, and the activation signal
is used to control the speed of the prosthesis. Cameron Roy Taylor: Any change in electrode
placement results in recalibration being required and even just moisture
changes throughout the day degrade the signal. Cameron Roy Taylor: Our lab has demonstrated
significant improvements to this method, which is called electromyography, or EMG,
but there are two fundamental limitations. Cameron Roy Taylor: To map muscle activation
to muscle force, the length of the muscle and the velocity of the muscle must be known.
Cameron Roy Taylor: The problem is that there's currently no technology for measuring these
parameters in humans in real time. So it isn't possible for us to accurately determine muscle
force, length, or speed from EMG information. Cameron Roy Taylor: If we sense muscle length
and velocity, we could accurately determine muscle force commands
Cameron Roy Taylor: from muscle activation, or even simply skip force control altogether and move
directly to position control over the prosthesis. Cameron Roy Taylor: Now, returning to the
man playing the piano. What makes us so uncomfortable with the time delay while
waiting for that last note to be played? Cameron Roy Taylor: EMG allows us to
extract some user intent, but we can't fully determine the desired forces and
movements without additional information. Cameron Roy Taylor: If a child were playing the
piano, we'd be less bothered by this, because with practice a child would quickly find the final key,
but this delay has to do with the learning curve for the prosthesis, not for the piano.
Cameron Roy Taylor: Without the muscle length and velocity information required
by the control diagrams we just discussed, it will never be possible to deliver complete
biomimetic control over the prosthesis. Cameron Roy Taylor: Our goal should be to
give full, intuitive, human-like control, by improving the fidelity of force and
position control and eliminating these delays. Cameron Roy Taylor: Towards achieving this goal,
we developed a new technology called magnetomicrometry, which enables us
to wirelessly sense tissue lengths. Cameron Roy Taylor: It uses pairs of
magnetic beads implanted in tissue, which passively transmit position information to
magnetic field sensors external to the body. We can use this technology to wirelessly
sense muscle lengths and speeds in a manner that is low-cost, compact,
portable, passive, and safe. Cameron Roy Taylor:
There are other methods for sensing muscle length, but each of them has limits that prevents
us from using them for prosthetic control. Cameron Roy Taylor: Let's look at the three major
methods that are used. First, fluoromicrometry, which uses tantalum beads and two intersecting
X-ray video streams, has 90 micrometer resolution, Cameron Roy Taylor: but it requires an entire room
and has significant processing time. It also uses ionizing radiation. So it can
only be used in a short burst. Cameron Roy Taylor: Second,
ultrasound is bulky, which would make it not so great for portable applications.
Cameron Roy Taylor: It also requires the sensors to have a fixed position, so you'd have to find a
way to maintain contact and consistently reapply ultrasound gel, and it would have a high time
delay due to the need for additional processing. Cameron Roy Taylor: Third, sonomicrometry uses two
implanted ultrasound crystals, a transmitter and a receiver crystal.
Cameron Roy Taylor: It has high accuracy, but it's highly invasive, not easily miniaturized,
and requires wires to be permanently maintained through the surface of the skin.
Cameron Roy Taylor: Magnetomicrometry doesn't have any of these limitations. This is our
new strategy for controlling robotic prostheses. Cameron Roy Taylor: In this new tracking strategy,
pairs of magnetic beads are tracked wirelessly with lightweight magnetic field sensor arrays
mounted to a prosthetic socket. Cameron Roy Taylor: This makes this technology
not only portable, but also minimally invasive. Cameron Roy Taylor: Because magnetic fields
are not affected by materials such as silicon, carbon fiber,
or the human body, the magnetic field passes undisturbed from the muscles to the sensors,
as if the other materials weren't even present. Let's now dig deep and talk about how this works.
Cameron Roy Taylor: We'll now discuss the mathematics behind our
new high speed magnet tracking capabilities. Cameron Roy Taylor: First, magnet tracking is
not a new thing. It's been used for many years to track vehicles, particles, geological formations,
fingers and jewelry, tools, and even objects moving through the digestive system.
Cameron Roy Taylor: Magnet tracking has also been used to sense joint angles, and researchers
have even proposed putting single magnets into muscles in an attempt to monitor movement. This
last proposal is worth taking a moment to address. Cameron Roy Taylor: Muscle length can be passively
cycled by the motion of a joint, such as your elbow, or actively cycled when flexed,
such as when holding a glass of water. Cameron Roy Taylor: These two movements
would confound one another if trying to measure either of them via the
axial motion of just one single magnet, Cameron Roy Taylor: while lateral magnet
position would be difficult to use with a prosthesis due to changing pressures from
the socket and surrounding tissues. Finally, any motion of the sensors or residual limb relative to
one another translates directly into noise. Cameron Roy Taylor: All of these issues are
immediately solved by the use of magnet pairs in each muscle. This distinction
will guide our approach to magnet tracking. Cameron Roy Taylor: Now, traditional magnet
tracking works fine for many of these previous applications, but for something like muscle
length tracking, the time delay is much too large. Cameron Roy Taylor: On this plot, time delay in
milliseconds is on the vertical axis, and the horizontal axis roughly represents the number
of magnets being tracked simultaneously. Cameron Roy Taylor: To enable reflexive
prosthesis control, we need a time delay
of no larger than around 10 milliseconds. Cameron Roy Taylor: These are logarithmic
axes here, so that's the ten-to-the-one line. We'll put a dotted line on there
for reference. So now we can see an issue. Cameron Roy Taylor: Nobody has ever tracked
multiple magnets with this small of a delay before. And for those cases in which more than
one magnet has been tracked at once, these numbers are typical delays in a controlled environment,
not worst case scenarios. Cameron Roy Taylor: Imagine that you are
driving a car where the pedals and steering wheel occasionally had a noticeable delay.
Cameron Roy Taylor: This would simply not do. Driving would be exhausting, not to mention
dangerous. Control of robotic hands and feet shouldn't be exhausting or dangerous at any
point during the day, especially when on the road. Cameron Roy Taylor: Our goal then is to track one
or more pairs of magnets with significantly reduced tracking time delay. On this plot,
this equates to discovering solutions that take us down and to the right. So let's take
Cameron Roy Taylor: a moment to talk about how we can reduce the magnet tracking delay
to provide seamless prosthetic control. Cameron Roy Taylor: You're probably
familiar with the repeatable magnetic field pattern that exists around a permanent magnet.
Cameron Roy Taylor: Mathematically, when we're tracking magnets, we represent the strength and
orientation of the magnet with the vector m, Cameron Roy Taylor: noting the orientation
of the magnet as the angles from vertical and around vertical. We then use three parameters
to represent the magnet's location, Cameron Roy Taylor: and three parameters
to represent each sensor's location, and a vector r to represent the position of
the sensor relative to the magnet. Cameron Roy Taylor: The direction of
r from the magnet towards the sensor puts negative signs in front of the parameters we wish to track,
so we'll have to deal with these later. Cameron Roy Taylor: With estimates of m and r, we
then use the magnetic dipole field equation, which describes this repeatable magnetic field pattern,
and allows us to calculate a predicted magnetic field at the sensor's location.
Cameron Roy Taylor: When tracking multiple magnets with multiple sensors, we add indices to these
equations, corresponding to each magnet and each sensor, and we use a summation to calculate the
total predicted magnetic field at each sensor. Cameron Roy Taylor: Now, the magnet tracking
problem amounts to using the magnetic field at each of the sensors to determine the three
position parameters of each magnet, Cameron Roy Taylor: but we can't do
this without simultaneously determining the orientation and the strength of the magnets.
Cameron Roy Taylor: After the strengths have been tracked and recorded once, the magnets
can be tracked using just five parameters. Cameron Roy Taylor: What makes this problem
challenging, however, is that the magnetic dipole field equation is not guaranteed to have
Cameron Roy Taylor: a single valued inverse. So we can't write a single, simple equation that
will take the magnetic field values and give us these position parameters. Instead, we have to
use an optimization, where we iteratively tests new position parameters, repeatedly calculating
and minimizing the distance, Cameron Roy Taylor: or rather,
the difference between the magnetic field that we measure at each of the sensors
and the magnetic field predicted by our current guess of the magnet position parameters.
Cameron Roy Taylor: As an example of how this is performed, when taking
two magnets and tracking them with five sensors, Cameron Roy Taylor: we measure the magnetic
field at each of the five sensors, and we initially
have no knowledge of the magnet locations. Cameron Roy Taylor: Our algorithm then begins
with an initial guess of the magnet parameters and iterates over several different potential
magnet locations and orientations until finding a solution that predicts a magnetic field in
agreement with the measured magnetic field. Cameron Roy Taylor: This is very similar to
the game of warmer/colder, where you have a friend guide you to the location of,
for instance, the pretzels on the top shelf. Cameron Roy Taylor: When you get farther away,
your friend says "colder". But when you get closer to the target, they guide you by saying "warmer",
Cameron Roy Taylor: "warmer", "hot", "very, very hot!" Now, this is great,
and it's a very amusing game to play, but it's much more efficient if your sister just tells
you that your target is up and to the left. Cameron Roy Taylor: For some mathematical
optimizations. You can save yourself some unnecessary work by directly computing
this direction, which we call the gradient. Cameron Roy Taylor: Traditional magnet tracking
takes the slower, less-efficient route, computing additional information, like when Jess here
is guided by the warmer/colder method. Cameron Roy Taylor: In our pursuit of
a reduced time delay, we realized that we could take the shortcut represented by the green arrow
and analytically compute all of the gradient information before sampling in any new direction.
Cameron Roy Taylor: To compute the gradient of our magnet tracking optimization, we calculate
how our magnetic field prediction error changes as we change each of our magnet position estimates.
Cameron Roy Taylor: Each change is called the derivative of the error with respect
to the position estimates. Now, since the change in where we estimate the magnet to be
Cameron Roy Taylor: only changes the magnetic field we predict, and not the magnetic field
that we measure, this is equivalent to taking the derivative of our predicted magnetic field with
respect to each of these tracking parameters. Cameron Roy Taylor: Now our error E sub i, is a
vector with three components. So when we take the derivative, we need to take the derivative with
respect to these three components, corresponding to x, y, and z, the three rows shown here,
Cameron Roy Taylor: taking the derivative with respect to all six of our tracking parameters,
x, y, z theta, phi, and m, corresponding to the columns here,
Cameron Roy Taylor: we can assemble our derivatives into a matrix. Since the
derivative of the error is the same as the derivative of our prediction, we can update
this matrix to reflect this information. Cameron Roy Taylor: And to account for
those negative signs we saw earlier in front of the position parameters,
we use the chain rule to extract the negative signs for the first three columns.
Cameron Roy Taylor: Now, the rows of this matrix are the three gradients for a single magnet
with respect to a single sensor, but if we wish to calculate the gradients for all
magnets with respect to all sensors, Cameron Roy Taylor: we repeat this
calculation for all magnets and sensors, and we assemble the information into a
matrix that we call the Jacobian. Cameron Roy Taylor: Now, here's
the important part. If you're really passionate about the speed and accuracy of magnet tracking,
each of these derivatives can be calculated analytically by hand to speed up and increase
the accuracy of the gradient calculation. Cameron Roy Taylor: This is the strategy
that distinguishes our method from traditional magnet tracking.
This means that you don't, you don't have to put in the work to calculate these gradients.
Cameron Roy Taylor: Because I've already done it for you. You're welcome. Now this is a lot
of information to digest, so don't try to ingest it all. There's just two takeaways
I want you to get from these equations. Cameron Roy Taylor: First, notice how many
times x squared, y squared, and z squared appear in these equations in total x, y,
and z are multiplied by themselves 141 times. Cameron Roy Taylor: If we're trying to maximize
the speed with which we calculate the gradient, we should compute each of
these once and reuse the results throughout, Cameron Roy Taylor: instead of performing these
multiplications over and over again. This cuts out 138 unnecessary multiplications
per magnet, per sensor. Cameron Roy Taylor: Okay,
so second, we can repeatedly apply this same technique
over multiple subexpression lengths. Cameron Roy Taylor: Focus in on the
common subexpression, x squared plus y squared plus z squared on the left, which is raised to
a power and sometimes multiplied by m. Cameron Roy Taylor: If we compute these
subexpressions as early as possible in our calculation,
we can eliminate the need to repeatedly calculate these subexpressions. This is a well-established
technique called common subexpression elimination. Cameron Roy Taylor: To perform common
subexpression elimination as efficiently as possible,
as of the year 2020 it's still best to optimize the order of the subexpression calculation
Cameron Roy Taylor: by hand, which amounts to solving a great big puzzle,
like a diabolical Sudoku grid. Here, you can see our hand optimized solution.
Cameron Roy Taylor: Note how x, y, and z are each squared early in the diagram, and that for the
particular shared subexpression we highlighted in the last slide, they are summed together on
the left, raised to a power and multiplied by m. Cameron Roy Taylor: Throughout the development
and testing of our algorithm, we built seven different versions of the magnet
tracking array. Shown here are the first six. Cameron Roy Taylor: For each of these arrays,
we used sensors similar to the ones used for the compass in your smartphone.
Cameron Roy Taylor: We used version four to perform our primary time delay testing,
while validating the accuracy using state-of-the-art motion capture.
Cameron Roy Taylor: Our algorithm stably initializes the new magnet locations,
and we demonstrate here high-speed real-time magnet tracking, even when tracking multiple
pairs of magnets. Cameron Roy Taylor:
First translating traditional magnet tracking methods into C++
gives us a time delay benchmark, which we'll show here in orange,
for comparison with our tracking algorithm. Cameron Roy Taylor: Applying the analytic
gradient techniques that we discussed a moment ago gives us a significant speed
improvement, shown here in purple. Cameron Roy Taylor: Recall that the
axis is logarithmic, so this is a remarkable change in speed.
Cameron Roy Taylor: Finally, applying the common subexpression elimination techniques that we just
described results in maximum bandwidth improvements of 336% 525% 635% and 773%,
Cameron Roy Taylor: for the simultaneous tracking of 1, 2, 3 and 4 magnets, respectively. We did
this using hardware with modest specifications. To run the tracking algorithm we used a 1.7 GHz
MacBook Air with 8 GB of RAM from early 2014. Cameron Roy Taylor: Now,
the portion of the techniques that we just described work great in the lab,
but how do we address doing this in the wild? Cameron Roy Taylor: Simply turning in place
changes the relative orientation of the earth's magnetic field,
and to further complicate this problem, cars, Cameron Roy Taylor: bicycles, manholes,
and buildings all warp and redirect the magnetic field of the earth. We needed
a way to compensate for these disturbances. Cameron Roy Taylor: Various approaches have been
taken to solve this problem: one using a sensor that is located far away from the sensing array,
Cameron Roy Taylor: another using a differential setup with just two sensors,
and another using magnetic shielding. Cameron Roy Taylor: The differential setup limits
accuracy and doesn't allow tracking of multiple magnets, so this was not an option for us.
Cameron Roy Taylor: The extra sensor makes the tracking array bulky and
could introduce additional errors, and the shielding creates a trade off between
tracking volume and the mass of the system. Cameron Roy Taylor: To solve these problems,
we developed a new solution which needs no additional components
and compensates for magnetic field disturbances Cameron Roy Taylor: using software alone. To
understand how this works, let's talk about taking a hiking trip with friends.
Cameron Roy Taylor: At any given location on the Earth's surface,
there's an ambient magnetic field. Cameron Roy Taylor: This magnetic field isn't
the same from one geographical region to another, but within a small area it's roughly consistent.
Cameron Roy Taylor: For instance, if you compare compasses with your hiking partners,
you'll find that your compasses point in roughly the same direction, and even if your compasses
are several centimeters away from each other. Cameron Roy Taylor: If you're using digital
compasses, you'll find that the compasses are measuring not only the same magnetic
field orientation, but the same magnetic field strength as well. We thus say that
the geomagnetic field is spatially uniform. Cameron Roy Taylor: When we're tracking magnets,
each sensor sees this spatially-uniform disturbance field superimposed on top
of the field from the magnets. Cameron Roy Taylor: If the sensors
are aligned in the same direction, they will each see the same disturbance as one another,
so this disturbance can be detected and compensated for. Let's take a look at how we
can modify our optimization to make this happen. Cameron Roy Taylor: When predicting the magnetic
field at each of the sensors, we simply add a constant vector disturbance term to the
front of the calculation for each sensor. Cameron Roy Taylor: Then, when calculating
the gradient of our optimization, we want to know how our magnetic field prediction changes
in x, as well as in y, and in z, when we change our disturbance field prediction
in x, as well as in y and in z. Cameron Roy Taylor: By definition, our disturbance
field predictions along the x, y, and z axes only affect our prediction of the magnetic field
along the x, y, and z axes, respectively, so our gradient submatrix is simply the identity matrix.
Cameron Roy Taylor: To account for the three new parameters needed for disturbance tracking,
we add three new columns to the Jacobian matrix and repeat this submatrix for each sensor.
Cameron Roy Taylor: With these changes alone, we can compensate for disturbances
in a majority of magnetic environments. Cameron Roy Taylor: The time delay
experiment that we performed earlier employed this disturbance compensation technique,
so there is no additional delay from this strategy apart from what we have already accounted for.
Cameron Roy Taylor: Notice how when the array is moved relative to the earth that the magnet
measurements stay constant, and when additional disturbances are added, that the tracking
algorithm is resilient to these as well. Cameron Roy Taylor: When recording the data
for the magnet tracking time delay with various magnet diameters and various numbers of magnets,
we also simultaneously compensated for and recorded the disturbance field.
Cameron Roy Taylor: In this plot, the disturbance field estimated by our algorithm is on the
horizontal axis and the vertical axis shows how often each estimate of disturbance field was made.
Cameron Roy Taylor: Each plot corresponds to a different magnet size or number of magnets, as
labeled on the left and represented on the right. Cameron Roy Taylor: The vertical black bars
indicate the disturbance field as measured before the tracking began,
so these are the true disturbance field values. Cameron Roy Taylor: These true values were not
communicated to our algorithm before it began tracking. As you can see,
regardless of if the algorithm is tracking small or large magnets or multiple magnets at once,
Cameron Roy Taylor: the disturbance compensation is able to faithfully track the field.
Cameron Roy Taylor: This turns out to work well not just for someone who is
walking and turns a corner, but also for far field disturbances from metallic objects.
Cameron Roy Taylor: I recorded these ambient field fluctuations, when I was commuting down to Brown
and had another train pass mine. Cameron Roy Taylor: Notice that the
corresponding x, y, and z axes across all of the sensors move up and down together.
In other words, our uniform field Cameron Roy Taylor: assumption
about the disturbance can reject all far field, magnetic disturbances,
regardless of the source. Cameron Roy Taylor: Now,
in addition to disturbance from outside effects. We also have to account for
disturbance from components on our sensing array. Cameron Roy Taylor: When a piece of iron is placed
in a magnetic field, it warps the field. When the field is strong, the iron will become magnetized
and retain this magnetization, even after it is removed from the field. This is the effect you see
when you expose a screwdriver to a magnet. Cameron Roy Taylor: What this means is that
when a magnet is brought near the sensing array,
ferromagnetic materials in the sensor or on the board warp and redirect the magnetic field.
Cameron Roy Taylor: And if the magnet is brought too close to the sensing array,
some of these components will become magnetized, requiring calibration to
compensate for the additional interference. Cameron Roy Taylor: To account for this,
we extended ellipsoidal calibration by combining several relevant state-of-the-art
magnetic field sensor calibration techniques together for an improved calibration method,
Cameron Roy Taylor: and we also added a step to normalize the scaling
between the different sensors in our array. Cameron Roy Taylor: Using just the ambient
magnetic field in the room, we first rotate the sensor a few times in the air and then run
the calibration algorithm. You can see here how ellipsoidal data collected by each of the sensors
is transformed into a single equivalent sphere. Cameron Roy Taylor: So finally, let's talk about
the animal work that we have performed to validate the use of this magnet tracking algorithm.
Cameron Roy Taylor: Returning to our vision, we plan to implant pairs of magnetic beads into
muscles to measure muscle length and velocity and use these measurements to impart intuitive,
high-fidelity control over robotic prostheses. Cameron Roy Taylor: To achieve this vision,
we need to address biocompatibility, verify in-vivo tracking accuracy, and ensure
long-term resistance of the implants to migration. Cameron Roy Taylor: To address biocompatibility,
we looked into various different coating options, and after consulting and collaborating on various
coating materials and techniques, we determined that Parylene C was a viable coating option,
and we had the magnets professionally coated. Cameron Roy Taylor: We then designed a sensing
array with 96 magnetic field sensors in an anatomically-relevant geometry, using two
circuit boards attached together in a 3D-printed fixture, and we used this sensor array to conduct
an in-vivo magnetomicrometry study in turkeys. Cameron Roy Taylor: To validate magnetomicrometry,
we used an in-vivo turkey model with a pair of magnetic beads injected into the proximal
and distal ends of the gastrocnemius muscle. Cameron Roy Taylor: With the turkey anesthetized,
we used a motor to apply a mechanical frequency sweep to the turkey's ankle, and we used to
spring to provide an opposing force. Cameron Roy Taylor: Throughout this
joint movement, we used a magnetic field sensor array positioned outside the turkey's leg, with
a laptop computer to run the tracking algorithm, Cameron Roy Taylor: which we used to track the
muscle length throughout the frequency sweep in real time.
Cameron Roy Taylor: For validation. We used stereo X-ray videofluoroscopy,
which consists of two X-ray sources which cast two shadows of the radio-opaque magnetic beads from
different angles onto two image intensifiers, Cameron Roy Taylor: to capture and record the
X-ray data. Using video recordings of these two shadows, we can calculate
the three-dimensional positions of the magnetic beads in the X-ray coordinate frame
and then compute the distance between them. Cameron Roy Taylor: This technique, called
fluoromicrometry, has been used previously with tantalum beads, and was shown to have a
precision of approximately 90 micrometers. Cameron Roy Taylor: Here's a plot from
our first turkey, Turkey A. The vertical axis gives the distance between magnets,
as measured by magnetomicrometry in blue and by fluoromicrometry in orange.
Cameron Roy Taylor: The horizontal axis is time in seconds, and the green plot gives
the absolute difference between the two curves. Cameron Roy Taylor: Notice how we are able to
faithfully track, even when the movements are well below a single millimeter.
Cameron Roy Taylor: The difference between the two curves is easily below 100 micrometers,
or one-tenth of a millimeter. When the excursions are larger than a millimeter,
tracking is significantly easier, as you can see in this plot from our second turkey,
Turkey B, here where the magnet, Cameron Roy Taylor: where the magnets
Cameron Roy Taylor: demonstrate that the muscle is changing
length by two millimeters. And this is still with passive cycling. We expect to see even larger
excursions during active muscle contraction. Cameron Roy Taylor: With a couple of the birds,
Turkeys C and Turkey D, we found consistent offsets between the magnetomicrometry
and fluoromicrometry measurements, as shown in this image from Turkey C here,
Cameron Roy Taylor: as well as in this image from Turkey D here,
but the standard deviation of this difference in data is still within 100 micrometers.
Cameron Roy Taylor: We expect that this issue is due to misalignment of our circuit boards,
since we attach them together using a 3D-printed fixture and plastic screws.
We expect that this could be corrected for with calibration or high-precision fixturing.
Cameron Roy Taylor: Now compiling all of the differences between magnetomicrometry
and fluoromicrometry from all four turkeys into a histogram,
Cameron Roy Taylor: where three trials were collected per leg,
we have the difference between the measurements along the horizontal axis and the probability
of these differences along the vertical axis. Cameron Roy Taylor: Each plot corresponds to
a different turkey and each leg is plotted in a different color, as indicated on the right.
Cameron Roy Taylor: Note that the units on the horizontal axis are in micrometers, or
one one-thousandths of millimeters so the standard deviations, which are less than 100 micrometers,
are less than a tenth of a millimeter, Cameron Roy Taylor: Note also the consistency in
the offset between the multiple trials, suggesting again the need to carefully position our multiple
sensor boards relative to one another. Cameron Roy Taylor: We wanted to further
investigate these translation offsets between the sensor
arrays, as well as our maximum sensing depth, Cameron Roy Taylor: so we placed two magnets into
a LEGO array at a known distance apart from one another, and we put a single magnetic field sensor
board above the magnets at various known heights. Cameron Roy Taylor: The horizontal axis of this
plot shows the distance between the magnets that was measured by magnetomicrometry,
and the vertical axis shows the probability of a given distance being measured by the algorithm,
Cameron Roy Taylor: where the sensor proximity is given on the right.
The vertical gray line shows the distance between the magnets according to the LEGO plate.
Cameron Roy Taylor: Notice that magnetomicrometry, with these magnets and this board,
maintains accuracy to a depth of about two or three centimeters, after which the accuracy
begins to fall off. Cameron Roy Taylor:
Finally, to ensure long-term stability of the magnetic beads against muscle, we collected
longitudinal data of the magnetic bead separation distances using computed tomography, or CT, scans.
Cameron Roy Taylor: In addition to implanting the magnets in the gastrocnemius, or calf,
muscles of the turkeys, we also implanted magnets in the iliotibialis cranialis,
or thigh, muscles of the turkeys. Cameron Roy Taylor: During the implant procedure
we vary the implant separation distances in order to determine a
minimum required separation distance. Cameron Roy Taylor: In this plot,
the vertical axis is the separation distance between magnets for each pair of magnetic beads,
and the horizontal distance axis, Cameron Roy Taylor: the horizontal axis is time.
Our theory was that if you implant the magnets far enough apart,
Cameron Roy Taylor: then over time the magnets will stay approximately the same distance away
from each other. However, we expected that if you implant the magnets too close to each other,
Cameron Roy Taylor: the magnets will migrate towards one another and eventually stick together.
And this is precisely what we saw. Cameron Roy Taylor: We found that for three
millimeter diameter magnets, there's a cutoff point around 20 millimeters, where you should
not implant the magnets any closer to each other. Cameron Roy Taylor: Note that the bottom line
drops to three millimeters from magnet center the magnet center, indicating that the magnet pair has
migrated towards one another and stuck together. Cameron Roy Taylor: The blue lines in this plot
indicate magnet pairs implanted in the gastocnemius muscle,
and the brown lines indicate magnet pairs implanted in the iliotibialis cranialis
Cameron Roy Taylor: muscles. Cameron Roy Taylor: Finally,
before we summarize these contributions. Let's discuss what work still remains to
transition magnetomicrometry into human use, as well as what new applications this new
technology enables. Cameron Roy Taylor:
Before clinical translation, we need to further develop the surgical implantation technique.
Cameron Roy Taylor: We have a well-developed method for implantation in turkeys,
but the technique needs to be further refined for human use, for instance, enabling ultrasound
guidance in the implantation process. Cameron Roy Taylor: Further,
we're currently in the process of running a preliminary long-term biocompatibility analysis,
so we're looking forward to viewing and further adding to these results.
Cameron Roy Taylor: As discussed previously, muscle lengths and velocities are an important
part of the prosthesis control loop and these control loops are already set up
in a way that is ready to be upgraded, Cameron Roy Taylor: but work will need to
be performed in developing and carrying out the specific details of the implementation.
Finally, additional magnet tracking hardware and software developments are in the pipeline,
and we're already excited about the next-generation magnet tracking system.
Cameron Roy Taylor: With this new technology now being accessible to the field of biomechatonics,
the field now has a wide breadth of new capabilities.
Cameron Roy Taylor: In addition to now having a path forward to improving prosthetic control,
both in controlling robotic hands, as well as in controlling robotic legs and feet, such as these
advanced robotics developed here in our lab, Cameron Roy Taylor: we now have a path forward
towards improved control over orthotic and exoskeletal devices for support,
rehabilitation, and augmentation, Cameron Roy Taylor: untethered animal and human
biomechanics studies in natural environments, to elucidate new principles of motion,
improved virtual reality hand and tool tracking without need for line-of-sight, and a new tool
for stimulation control and feedback for paralysis mitigation and recovery.
Cameron Roy Taylor: In summary, to address the high magnet-tracking time delays in state of
the art multiple-magnet tracking, we developed a high-speed multiple-magnet tracking algorithm.
Cameron Roy Taylor: Then, finding limitations to the use of magnet
tracking and portable applications, we developed a disturbance rejection algorithm,
which can be implemented entirely in software. Cameron Roy Taylor: To address the need for a
framework to calibrate multiple magnetic field sensors at once, we extended and improved
previous ellipsoid fitting techniques to adapt them specifically to multi-sensor calibration.
Finally, seeing the need Cameron Roy Taylor:
seeing the great need, for a minimally-invasive technology to accurately
track muscle lengths and speeds in real time, Cameron Roy Taylor: we developed a new technology,
magnetomicrometry, to meet this need, and we validated this technology by tracked,
by tracking muscle legs in an animal model. Cameron Roy Taylor: We published our work on
magnet tracking in the IEEE Sensors Journal, so additional information on our high-speed magnet
tracking technique can be found there, and we're currently working towards publishing our work
on magnetomicrometry. Cameron Roy Taylor:
I'd like to start by specifically thinking my advisor, Hugh. Hugh, thank you for the freedom
that you've given me to explore. It was a really big deal when I started with you,
Cameron Roy Taylor: and shared with you the initial ideas from my PhD work,
and I could tell that you thought my early ideas were crazy. But you still let me explore
and try crazy things. Thank you for that. Cameron Roy Taylor: And while you've mentored
me through this most recent technology, I've appreciated your mentorship, your approach to
scientific research, and your drive to create technologies that will help people live full,
productive, meaningful lives. Cameron Roy Taylor:
I'd like to thank my committee for their guidance through this process and for allowing
our research to grow in an organic, messy, Cameron Roy Taylor: nonlinear, beautiful way,
Tom, thank you for the countless hours you spent
working with me on experimental design, surgical techniques,
and experimental setup and follow through. Cameron Roy Taylor: Without your commitment,
this work would still be a spark instead of a bonfire. And Joe, thank you for your
encouragement to think outside of the box. Cameron Roy Taylor: about what the future
ought to look like, even in the cases where it doesn't immediately seem physically possible for
technology to enable that future Cameron Roy Taylor:
Okay, a long list here. Cameron Roy Taylor: I'd like to start
by thanking the Biomechatronics group for being such a wonderful set of colleagues and friends.
Seong Ho Yeon, thank you for working with me on crazy circuit designs.
Haley Abramson, thank you for driving the software aspect of the magnet tracking project forward.
Cameron Roy Taylor: Your excitement motivated our timeline and your commitment drove this project
to move along as fast as it did. Cameron Roy Taylor: Tyler Clites,
thank you for the inspiration and for taking the time to train me in surgical techniques and
electrical stimulation. Bruce Deffenbaugh, Cameron Roy Taylor: thanks for our regular
conversations about electromagnetics. Shriya Srinivasan, thank you for mentoring me on
experimental design of animal studies. Cameron Roy Taylor: Lindsey Reynolds,
thank you for all of your amazing work that you do to keep our group up and running. Thank
you. Sebastian Roubert for your continued excitement in prosthetic control interfaces
and you're consistent checking in. Cameron Roy Taylor: And thanks to
my current and past officemates, Emily Rogers,
Cameron Roy Taylor: Robin Hsieh, Cameron Roy Taylor:
and Stephanie Ku for lending an ear when I needed to get advice on research problems.
Lisa Freed, thank you for always being willing to give advice on animal work and protocols.
Cameron Roy Taylor: Roman Stolyarov, your passion for magnet tracking work
helped keep me moving on ideas that I would have otherwise set on the shelf. Thanks for
that encouragement. Aaron Jaeger. Thanks for being such a great sounding board for ideas.
Matt Carney, thanks for keeping us all together. Cameron Roy Taylor: Anthony Zorzos and Ron Riso,
thank you for taking the time to mentor me through neural interfacing techniques.
Michael Eilenberg, Michael Nawrot, Cameron Roy Taylor: thanks for showing me
the ropes on motion capture and biomechanics, and David Hill, thank you for introducing me,
literally, to biplanar fluoroscopy. Cameron Roy Taylor: And thank you
to the rest of the group for our crazy coffee
chats and conversations in the hall. Cameron Roy Taylor: These have been
critical to keeping a balanced mind and moving research forward in ways that make the most sense.
I'd like to thank the Roberts lab Cameron Roy Taylor: for your support,
particularly Mary Kate for your huge commitment on this project as well as
Erika for your invaluable help with logistics, Cameron Roy Taylor: Rich, for your advice on
experimental setup, Beth and John for your guidance on X-ray processing, and Jarrod and Amy
for help with getting experiments off the ground. Cameron Roy Taylor: Thanks to the Responsive
Environments group for letting us use critical tools at times of urgent need,
and specifically to Brian Mayton, who came through to help with hardware advice at critical times,
Cameron Roy Taylor: several times throughout the project. Thanks to the MAS Office,
Keira, Monica, Linda, Sarra, Amanda, Lily, Cameron Roy Taylor: you made the whole experience
of working on a PhD much more human. Thank you sincerely for that. To my PhD cohort, thank you
so much for your interest and support on random things and for going through all of this with us.
Cameron Roy Taylor: To the Lifelong Kindergarten group at the Media Lab,
and especially to Mitch Resnick, thanks for your enthusiastic support and letting me come,
sometimes even very, very early in the morning to permanently borrow LEGOs for experiments.
Cameron Roy Taylor: To the Langer lab, especially to Bob Langer, thank you for your
advice on migration studies that came at a key time in our project. This advice was extremely
helpful in moving the project forward. Cameron Roy Taylor: A special thank you
to Mike Gray for your guidance on biocompatibility, as well as
your guidance in a multitude of other ways. Cameron Roy Taylor: And to thank you to Ian
Hunter's Bioinstrumentation Lab, for allowing us to use their Parylene coating equipment.
Cameron Roy Taylor: A thank you also to the MIT and Brown veterinary care staff, particularly to
Jennifer Haupt and Morgan Jamiel at MIT. Your help with out-of-the-ordinary requests made a large
difference in the development of this technology. Cameron Roy Taylor: I have Michael Brumett
to thank for the beautiful artwork showing the magnetomicrometry concept
and the turkey muscle frequency sweep experiments. Cameron Roy Taylor: Thank you for taking the
time to convey these ideas so cleanly and inspirationally. And to the Salah Foundation,
Cameron Roy Taylor: thank you for your support of this project. Without your support we would
be spending our time trying to determine how to fund this research, instead of moving it forward
towards clinical translation. Cameron Roy Taylor: And a special thank you to
my family. Your support, confidence, and love Cameron Roy Taylor:
have been more important than anything else. Cameron Roy Taylor:
Specifically to my lifelong partner, Sara. Thank you for being a rock I can lean on in hard times,
for being my comfort in the even harder times, and for putting perspective into my life about
what really matters while we're here. Cameron Roy Taylor: Thank you.
Hugh Herr: Thank you, Cameron. Excellent presentation. Let's put our hands together.
Hugh Herr: So I will. I'll begin with a question and then we'll open it up to everyone to ask a
question. Again, you can write your question into the chat window and I will read the question and
Hugh Herr: Cameron will do his best job at answering so Cameron, you mentioned your
presentation that error increases with the depth from the sensor array to the magnetic spheres.
Hugh Herr: and as we've chatted many, many times the
Hugh Herr: prosthetic interface is about a centimeter thickness
and then you have the skin thickness and the fascia before you even get to superficial
Hugh Herr: muscles. Hugh Herr: So, you know, maybe you can
do that at three centimeters, but it may be very, very challenging in many anatomical regions, um,
could you share with everyone strategies moving forward to mitigate the increase in error with
increasing depth, what we can do to Cameron Roy Taylor: Yeah yeah
Cameron Roy Taylor: Yeah, so this is a this is a really important problem,
um, that we need to expand on in order to to have high accuracy at high depth to access muscles that
are especially deep in that arms or legs. Cameron Roy Taylor: So by adding additional
sensors to the sensing arrays because of our high speed magnet tracking algorithm,
Cameron Roy Taylor: which we expect that we're going to be making some modifications to
to make even faster will allow us to have additional sensors in the tracking and with these
additional additional sensors, Cameron Roy Taylor: it effectively drops
the noise of the system and allows us to track at high accuracy at lower depths and in addition
Cameron Roy Taylor: all of this work was done unfiltered, we didn't use
Kalman filtering or Savitzky-Golay filtering or or anything to smooth out the data,
which means that the the accuracy can further be increased at depth beyond what we've already done.
Hugh Herr: Great. Thank you. Additional questions or comments from the audience?
Hugh Herr: Um, I see one here something, does magnet implants prevent user from MRI scan?
Cameron Roy Taylor: That's a, that's a really good question.
Cameron Roy Taylor: That is something that we're still looking into
Cameron Roy Taylor: That will be a problem that we're going to have to investigate with
respect to the surface area of the implants, as well as the strength of the implants.
Cameron Roy Taylor: Our hope is that the surface area of the implants is large enough to resist the
movement due to MRI. Cameron Roy Taylor: Noting
that we're already putting them, that we're already able to put them far enough apart
to not have them move towards each other. Cameron Roy Taylor:
We think that it will be possible to implant these in muscles and to be able to do
MRIs without the magnets moving in the muscles and we'll do further pre-clinical validation to ensure
that before we move into the clinical realm. Hugh Herr: Great, Connor Henley asked, what sort
of common life scenarios might break the uniform ambient background assumption in your model?
Cameron Roy Taylor: Yeah, great question. Um, so the uniform field assumption is at far field.
Cameron Roy Taylor: And far field is different depending on the size of the disturbance
Cameron Roy Taylor: So for instance, like your refrigerator. Like if you, if you brought your
Cameron Roy Taylor: your arm, like, immediately within contact of your refrigerator,
then that would cause a disturbance, but you'd have to be really close. So, so far field.
Cameron Roy Taylor: isn't as far away there. But if you have a another permanent magnet nearby
that can mess with the field on the. The algorithm is able to deal with a
permanent magnet being brought nearby. But if you bring it to close, it's an issue.
Cameron Roy Taylor: Another way of dealing with,
if you have like a super large magnetic interference in the room. Another way to make
the far field kind of change in position is to add a another order to your like another
Cameron Roy Taylor: order of terms to your disturbance calculations. So you can,
in addition to doing uniform disturbance tracking you could take the gradient and track the gradient
of the disturbance as well. And that gives you one more step in canceling that out.
Cameron Roy Taylor: So there's there's lots of different scenarios. We've spent lots of time
thinking through all of them. I, I hope that Cameron Roy Taylor: we've covered it enough,
but we're going to be continuing to think about it. And we'll be doing
also lots of testing on it to make sure that it doesn't fail in any unexpected scenarios.
Hugh Herr: Great, Connor Henley also asked in his second question, have you considered
second order optimization methods? Cameron Roy Taylor:
Uh, second order optimization methods, Cameron Roy Taylor:
I'm not completely sure what that means. And Hugh Herr: Methods that use the
Hessian, for example. Cameron Roy Taylor: Oh methods that
use the Hessian, yes. Um, so yes. Yes, we have considered using the Hessian in magnet tracking we
Cameron Roy Taylor: One problem with Cameron Roy Taylor: jumping straight into using
the Newton's method is you need some help from nonlinear algorithms like the Levenberg-Marquardt
algorithm, the algorithm that we're using here. Cameron Roy Taylor: But after you initialize it
I think using the Hessian is actually a really good idea. And so that's something that we're
we're looking into we just, we haven't had the time to dig into it yet and analyze it.
Hugh Herr: Great. Haley Abramson asks. Hugh Herr: You show the three millimeter
magnets being attracted to each other over time in the turkey legs. How will you choose which
size magnets to implant in humans, assuming depth, working with MRI scans, etc. matter?
Cameron Roy Taylor: Yeah. Yeah, great question. So there's a lot of
trade-offs involved in determining these so Cameron Roy Taylor: You need to determine
what the diameter of the magnet, what the N-rating, or strength, of the magnet is,
as well as the distances between the magnets and the depth of the magnet.
Cameron Roy Taylor: So all of those are going to be a very large trade off
as we're working through this problem. But what our plan is, is to model the anatomy.
Cameron Roy Taylor: of the limb and first in simulation test out the tracking and make sure
from just back of the envelope that Cameron Roy Taylor: that about where
we're expecting to put the magnets is going to work with high accuracy,
and then we'll create a model of the limb, where we actually have magnets physically in the model.
And we'll test it out to ensure that we're using all of the right parameters.
Hugh Herr: Great. Lindsey Reynolds asks, do you do you envision magnet tracking to ever be used
for physical therapy? Hugh Herr:
Creating resistance, strengthening tissue, etc.? Cameron Roy Taylor: Ah, good question. So,
so there's there's two aspects to that question, the first one
Cameron Roy Taylor: has to do with imparting a force on the magnets.
Cameron Roy Taylor: To impart the amount of force that you would need to
to create a resistance in the muscle, Cameron Roy Taylor: in for like a large
scale like your bicep muscle, you'd have to have so much force that it would actually
cause the magnets to migrate. Cameron Roy Taylor: So we wouldn't want to do that
using the magnets. However, it would be possible to use the magnets to monitor a physical therapy
regimen and to track how someone's doing, what the stiffness of their muscles look like
and what their performance is as they're working. Hugh Herr: Okay. Um, S., I'm not sure I'm
pronouncing this name correctly but S. Roubert, with respect to migration and muscle,
what is the interplay between magnetic strength and orientation? I believe, is the question.
Cameron Roy Taylor: Mm hmm. Yeah. Cameron Roy Taylor:
That that is a, it's a good question and something I've spent a lot of time thinking about it,
something that's that's interesting here Cameron Roy Taylor: is that because of their
spherical magnets, when we implant them we we implant them and they take
whatever orientation they want to take. Our, our assumption is that they'll.
Cameron Roy Taylor: after we put them in, that the
fibrosis from the muscle, that the scar tissue will hold them into place and that they won't
shift. And even if they do shift, they'll still stay in that muscle fibrosis capsule.
Cameron Roy Taylor: So the, the orientation doesn't actually
Cameron Roy Taylor: doesn't actually matter because they'll stay in place
and I guess there's there's kind of a nuance second part to that question.
Cameron Roy Taylor: The orientation is something that we need to compute in order to find
the just do the mathematics of the problem we need to compute it in order to find the
Cartesian positions of the magnets, but we don't actually need the orientation so we toss it out.
Hugh Herr: Right. He or she further asked do you expect a difference in humans?
Cameron Roy Taylor: Hmm, I Cameron Roy Taylor:
So in terms of muscle properties the properties are extremely similar between
turkeys and humans. So in terms of migration. Cameron Roy Taylor: we're not expecting any
differences. In terms of scale. Cameron Roy Taylor: The entire
problem has to be scaled and it's not it's not a, like a linear scaling from turkeys to humans,
Cameron Roy Taylor: but there are some linear aspects of the problem that we
can use to scale it so we'll be able to learn lots from what we've done here
Cameron Roy Taylor: as we're transitioning to the new scale.
Hugh Herr: Great. Joost Bonson asked, or are there lessons from the Bod-Mod
community relevant for you? Cameron Roy Taylor:
Great question, Joost. Hugh Herr: Those people who elected to implant
magnets in their fingers under skin, etc. For the yeah
Cameron Roy Taylor: Okay, so first a technical regulatory question. And since
this is being recorded and will be part of the presentation. I should also take a moment to note
Cameron Roy Taylor: that we do not recommend implanting magnets in yourself.
Cameron Roy Taylor: Or putting them in your nose or mouth or swallowing them or
any other of those things you should be careful with magnets. They're dangerous and
you can read about the danger of magnets online. Cameron Roy Taylor: Now the body mod techniques
that have been done. We've, we've actually learned a lot of interesting things.
Cameron Roy Taylor: The first one was actually about MRI. Um, so someone had
implanted a magnet into their finger and did a Reddit AMA and talked about how they got an MRI.
Cameron Roy Taylor: My microphone just went a little crazy there, but I'm
Cameron Roy Taylor: gonna unplug my Cameron Roy Taylor: microphone here.
Cameron Roy Taylor: Can you can you still hear me, yes.
Cameron Roy Taylor: Okay. Um, so someone, someone had implanted a magnet and their
finger and then gotten an MRI and we don't know whether or not that the finger was
Cameron Roy Taylor: In the strongest part of the field or not, but they related how the
magnet rotated inside of their finger but stayed in place and was with still useful
for their purposes after the MRI. So that was that was encouraging that it might be possible.
Cameron Roy Taylor: to to have these be MRI compatible.
Cameron Roy Taylor: And I'm trying to think if there's if there's, that, that's probably the
biggest one from the the body mod community, um, Cameron Roy Taylor: I think another interesting
thing, I'll just share one more, is that people who have implanted these
in locations where there are peripheral nerve sensors which are especially sensitive have
Cameron Roy Taylor: experienced the feeling of feeling AC magnetic fields when they're
getting like close to an outlet on the wall Cameron Roy Taylor: and so it's just it's good
for us to be aware that there might be feeling Cameron Roy Taylor: in people who have these
implants that they'll, they'll have an additional sense imparted to them,
Cameron Roy Taylor: perhaps. We're not sure, but it's an, it's an interesting conjecture.
Hugh Herr: Like using the Hassian and the Jacobian, you already
answered that regarding the Cameron Roy Taylor: volume one second.
Cameron Roy Taylor: I can't hear you hear, Hugh Cameron Roy Taylor:
Let me try putting my headphones back in. See if we can solve this problem.
Hugh Herr: Can you hear me? Cameron Roy Taylor: I can
hear you now. Yeah, great. Hugh Herr: Guillermo asks
Hugh Herr: What's the smallest hardware footprint you think of being the magnetometer board
can can be achieved? Cameron Roy Taylor:
As a good question. It's that's extremely application specific
Cameron Roy Taylor: So if, if you are wanting to track.
Cameron Roy Taylor: For instance, like a Cameron Roy Taylor: An ant moving through with a
very tiny anthill or something like that, then, like, like you could you could try to get them
the sensor is extremely close together. Cameron Roy Taylor: And in our application.
We've want to use as much footprint, as we can, while keeping it lightweight,
because the additional sensors. Cameron Roy Taylor:
Reduce effectively reduce the noise. Cameron Roy Taylor: You might be asking,
though. Instead, the question, not how small can you make the total sensor board, but how close
you can get the sensors to each other. Cameron Roy Taylor: And we basically
reached the limit like Cameron Roy Taylor:
Working with Seong Ho, we really push the limit on physically how close you can get the sensors to
each other. So the board that you saw is the best that you can do with like currently available off
the shelf fabrication components. Cameron Roy Taylor:
But if you if you manufactured the sensors in the factory.
Cameron Roy Taylor: If one of the Cameron Roy Taylor: Compass
manufacturers decided to manufacture them directly onto the silicon,
you could get dramatically closer Cameron Roy Taylor: maybe on the order of like
less than a millimeter between them, but you still have to figure out where to put your capacitors
and your communication equipment. Hugh Herr: So that I don't know
if you mentioned this Cameron, but the current board size is roughly the size of a credit card.
Cameron Roy Taylor: Right. Yes. Yeah, I think that's a
Cameron Roy Taylor: very important distinction. Hugh Herr: Um Noella again I'm probably
mispronouncing the last name, Bore, asks can you use this technique for Sciatic Nerve disorders.
Cameron Roy Taylor: I'm not an expert on Sciatic Nerve disorders.
Cameron Roy Taylor: I'm going to punt on that question, since I don't I don't
know much about Sciatic Nerve disorders and say I
Cameron Roy Taylor: I'm not sure. Hugh Herr: Okay, Cedric asks. Did
you also consider electromagnetic actuation on these magnets for muscle stimulations?
Cameron Roy Taylor: Yes. Yeah, we did. Um, so I think it would be very interesting to, we,
I mentioned in response to Lindsey's question Cameron Roy Taylor: that we wouldn't want to
use very large magnetic fields Cameron Roy Taylor: to
Cameron Roy Taylor: to actually physically activate the muscle.
Cameron Roy Taylor: However, it would be interesting to use coils outside the body
to set up a, Cameron Roy Taylor:
just the right frequency of magnetic field that would cause tiny vibrations in the magnets, which
could cause your sensory nerves to pick up on it, and perhaps it could give you some information
which could impart proprioception, for instance. Hugh Herr: Excellent. Joe Paradiso asks,
instead of calculating magnetic position could use machine learning to go directly
from sensor outputs to prosthetic commands? Cameron Roy Taylor: Yeah, yeah, that's
Cameron Roy Taylor: That's a good question. I, so, I early on we looked
into using machine learning to, let's just talk about the, from magnetic field sensors
to magnet positions for a moment here I'm Cameron Roy Taylor: In, in the case of
Cameron Roy Taylor: just tracking magnets with the magnetic field sensors, because the problem,
like the mathematics is already developed Cameron Roy Taylor: the machine learning would
just add an extra layer of unnecessary computation. And so we discarded that
idea. It would actually significantly increase the latency to do it that way.
Cameron Roy Taylor: And in terms of mapping magnetic fields directly to
the robotic prosthesis. Um, it sounds tempting because it's like, oh, like we
cut out the middleman cut out the Cameron Roy Taylor: having
to compute the positions in the distance, but because we understand the muscle physiology and
then that by, without even having to do lots of machine learning,
Cameron Roy Taylor: training and Cameron Roy Taylor: guidance,
Cameron Roy Taylor: we can just implant these magnets into the muscles and then
use those principles, the, those principles of physiology, to create a model that that works in
any environment, no matter what someone's doing. Hugh Herr: Excellent. D-I-V Div asks,
I'm imagining the implications of such technology in emergency medical settings.
And I'm wondering if you were able to test this with smooth muscle function.
Cameron Roy Taylor: We, we only used this with skeletal muscle
on so we only put it in the gastrocnemius and iliotibialis cranialis muscles of the turkey.
Cameron Roy Taylor: I Cameron Roy Taylor:
Might be useful information that Cameron Roy Taylor:
The one of those is pennate and one of those is straight in, in muscle striation.
Cameron Roy Taylor: In terms of smooth muscle I, I'm thinking that you're hitting, hinting at
Cameron Roy Taylor: putting it, help me out here Hugh, is the heart smooth muscle?
Hugh Herr: This, the abdomens. Cameron Roy Taylor: The abdomen, so on.
Cameron Roy Taylor: So I that there's been Hugh Herr: Sure. Here she's saying to
smooth out muscle function or smooth muscle. I'm not sure with the, question
Cameron Roy Taylor: Yeah, so, um, so, so let me, let me just address the question of putting it in
Hugh Herr: Anything involuntary really Cameron Roy Taylor: Ah, okay. Um, yeah, so
Hugh Herr: He or she says, to the heart counts. Cameron Roy Taylor: The heart counts. Okay.
Um, yeah. So, so first I would I would caution strongly against using
this in the loop with pace, making Cameron Roy Taylor: because magnets are
conductive and so they would, they would create a hotspot, and you would get burns. And so you would
want to avoid, avoid putting it near the heart. Cameron Roy Taylor:
That there might be some other application you come up for it. But
in terms of a, like a pacemaking application that it would be a dangerous place to put them.
In terms of involuntary contractions, Cameron Roy Taylor: putting them into the
muscles that help you breathe, um, could actually be an interesting way to monitor breath
and impart breath to patients who are, for instance, paralyzed, um,
Cameron Roy Taylor: Yeah, so I think there's there's additional applications
there that could be explored. Cameron Roy Taylor: Great.
Hugh Herr: Wonderful, um, that concludes the Q&A session.
Cameron Roy Taylor: Let's put our Hugh Herr: hands together again for Cameron.
Hugh Herr: So now, Cameron's committee will drop off this Zoom line and go on to a separate Zoom
and discuss the defense and the thesis. After about 15 minutes we will return to the Zoom call
so please stay online. Cameron Roy Taylor: Thank you everyone.