October 26, 2025 • 34 mins
Jack Chew talks all about measurement tech for the clinical space with Athan who is CEO of our partners Kinvent! He is an interesting charecter with a background in sport and engineering which led him to develop MSK specific measurement equipment!
Get all your equipment for measuring here: http://landing.kinvent.com/partner-physiomatters
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Episode Transcript
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(00:05):
Nathan, welcome to chewing it over.
Why should we measure stuff in MSK anyway?
Actually, it's because if you can measure it, then you can
improve it. That's one reason.
And the second one is you have to build motivation with your
patients, right? It's important that they engage
so that they follow up with a rehab to the end.
Now that's really interesting because that second point is one
(00:26):
of the things that's part of theorigin story of Kinvent.
Like you were inspired by that as an as an athlete and patient
and you felt like your motivation faded if it wasn't
measured. So just tell that story for me,
if you would. Stories that I did 10 years of
competitive sports, rowing and rugby.
And actually, thanks to my favorite sports, I spent more
time with my physical therapies than with my, let's say code,
(00:48):
right? True.
I mean, knees, ankle, I mean, anything, not very serious ones.
I mean, I never had an injury, but every time it was a set of,
let's say, 1020 therapy sessions, right?
Physical therapy. And I, every time, every time
after three sessions, I just wanted to abandon and go do it
(01:10):
by myself at home. You know, you have the
impression of playing with elastics and gym balls in a gym
by yourself. You don't know why you're there.
You don't see your progress. It seems boring.
And I'm an engineer myself and you know, professors and
teachers always told me Athan, you have to base your decisions,
but numbers and curves and therewas none.
(01:32):
So this started as a frustration.
And after I actually stopped my sessions, that was also
frustrating for my physical therapist, of course, right.
You guys physiotherapist, you love your work.
You are among the most passionate people I've met for
their work. I mean, if if you just leave 2
(01:52):
physios together by themselves and in a group of people, they
will turn to each other and somestart discussing about the ACL
they treated the other day and what kind of test they did and
that kind of stuff. So the why behind Kinvent is to
build a system that will help both those guys, the patients
and the practitioners, so that you can measure progress, but
(02:16):
most importantly build engagement.
And that's why you need to measure it.
And so you're what's really interesting is that your
hardware and software really seem to speak to that dual aim,
which is great because the usability and the precision have
been really crucial to what you're doing.
So, so tell me about the engineering problem solving part
(02:37):
of you that then it was inspiredto to drive building what you've
built. So the, the whole story starts
when I was very, very, very young because actually my dad
was a bio mechanics professor. Now he's tired, he's happy with
his four or five grandkids. But at the time when I was very,
very young, like let's say one or two years old, I was in his
(03:00):
lab and he was building his own sensors because he didn't have
any budget back in Greece. So this is where bio mechanics
started for me and this is whereI learned the basic stuff and
then I did some engineering studies because I always wanted
to build something for let's sayathletes.
And then my physical therapy experiences helped me to go
(03:22):
towards that path. So the idea was how can we use
the science bio mechanics to give insights that practitioners
but even more patients or athletes understand?
So the way we tried to build oursystem is that is scientific.
So it measures the right thing, but also the interfaces are made
(03:44):
in a way. So even your patient, I mean,
let's say Grandma Michelle for example, she comes in for her
hip surgery. You want her to understand why
she's there. You want her to understand why
she actually her strength on thehip extension, for example, is
growing, right? So that's what does she want.
She just wants to walk back again and get her grandkid in
(04:08):
her, in her arms. And I think that that's one of
the things that's particularly interesting as I've got to know
the companies, I've got to know you a little better.
The engineering, sort of the physics of the matter, the
numbers of the matter, the measurement, the precision that
is rarely embodied in the same person.
That's also got a passion for the individual humans, right?
(04:30):
I know I'm, I'm speaking to cliches a little bit there.
But sometimes that it's reasonable in humans.
And we know the data on this by personality that sometimes it's
you've got those that are sort of really into the technical
things that are with the gadgetsand gizmos and stripping back
down parts. And then you've got those that
are sort of really motivated andempathizing with the individual
patient, especially those that are injured.
And then there's interesting where that Venn diagram can
(04:52):
intersect. You really speak to that middle
where you're motivated by both things.
Did you notice quite quickly in your career that that was quite
unique? Well, there was a journalist
once that said that there is a count of a thing called the
founder market Fit. And I'm happy to say that I love
this space. I mean, you know, you grow with
(05:13):
what you have. You are surrounded with, so I, I
think I was pretty good in, let's say, mechanics and
engineering. So, you know, I did my studies
between France, Greece, a bit inthe US, electronics, mechanics,
bio mechanics, all that was great.
And it was, let's say my passion.
And then the clinical expertise.I'm not the clinical guy for
(05:36):
human that we have physical therapists, coaches, doctors
working with us. So they know what's best.
But then the most important thing is to understand your
customer and the only way of understanding your customer,
specifically a market like physical therapists.
I'm not a physical therapist. I cannot just put myself in the
skin of you, Jack. But you have to spend a lot of
(05:59):
time with them. And once you spend time, you
also get the empathy because youspend time with people that love
what they do, that they have sessions that are not honored,
patients not not coming in, theyare not paid so well, but they
have a level of education that is extremely amazing.
(06:21):
And so you get the empathy and you get to understand those.
So once you do that, if you understand the engineering
behind, you can lead the team todo the right thing.
That's right. And I think that that clearly
has sort of motivated your passion to retain the interest
in both product and people. And so I want to get into
product with you because that was something that when we're
(06:43):
using your hardware. So particularly in in clinic at
the moment we're every, every day we are using the
dynamometers and the and the K deltas and the way in which that
is doing what you describe, which is motivating compliance
with patients, but also incentivizing our clinicians to
better get accurate outcome measures from patients and, and
(07:06):
helping them to helping the patient understand that the
software is assisting with that.And then the the user interface
is, is fantastic. What has been the driver for you
to be building the specifics of the hardware?
Like did you know where you definitely wanted to start?
And then do you, when you're creating a product pipeline like
what is sort of motivating the decisions and the priorities
(07:29):
there? So let's give people a bit of an
idea of what Quinvent is. Quinvent is 10 sensors, all of
them connected to one single appcalled Quinvent App to assess
and train strength, balance, motion, speed, explosiveness
etcetera. Not only assess but also train
because training is important. It's not assessment that will
(07:50):
bring patients forward, but training, assessment will show
them where they are and where they're getting right.
Yes. Now this is conventionally and
among those 10 sensors there arethe dynamometers, there is the
force plates, there is range of motion, speed, velocity based
training etcetera, etcetera. Now we didn't come up with all
(08:11):
the 10 at once, OK, that would be, that would be a miracle if
we did. But thing is that we started
with what the market was asking.So the market first was asking
for tools to measure strength. Why strength?
Because strength until that moment was pretty subjective in
(08:33):
physical physiotherapy. So do you remember the Oxford
scale from zero to five? Yeah, exactly.
So there was some things interest, some interesting
things to do there. And it's, I mean the mark, the
professionals will use what theyhave in their hands.
And until that moment, technologies wouldn't allow to
do a strength measurement fast in a clinical setting in less
(08:57):
than 5 minutes, right. You have to go to the lab with
the PhDs with the expensive equipment and spend hours there
and then weeks to get the results.
So Kinvent, we tried with Kinvent to change that, to make
those technologies easy to get in practice.
So that's why the first thing that people asked for was
strength. The second thing was balance.
(09:19):
You have someone coming in aftera century surgery and you want
to simply, for example, see how they load left and right and try
to work on that. And that's how we started force
plates, small ones for balance, strength for body, for the whole
body and grip. And then just listening to what
(09:41):
the market would ask took us to get range of motion, bigger
strength. So for for bigger bus groups,
then explosiveness, biometrics, jumps, sprints, gate velocity
based training, all those things, we added them
(10:01):
progressively. Yeah.
And I think that that's something that really makes
sense then for the question of prioritising is that you're
listening carefully to what the market needs, what the patients
and and therapists are requiringto best enhance their treatment
and their care. One of the things that's been
and I'll admit now to the audience that it took for us to
(10:23):
have the devices and to be usingthem and using them to good
effect. But then realising that we
weren't optimising them. Jump on a call with Ethan and he
he clearly shows that I was overseparating tests that was
actually taking. I still thought it was pretty
quick because it was better thanmost devices we've used before.
But they they this is the thing that you have optimized the
software and the detection mechanism, especially with the
(10:45):
with the K mode and things. So just tell me a little bit
about that. And because that that's that's
been a really interesting way that that even I thought that we
were doing well, but actually we're only using it so.
So First things first is you guys, practitioners, you don't
have time. So even one click, one click
more can be one too much. So we have an obsession to make
(11:10):
the system to have as less clicks as possible.
OK, so it's not always easy, butthis is let's say one of the
main specifications that we put to our system as less clicks as
possible. So, but, but to do that,
sometimes you need to enhance the technology.
So you talk about, for example, auto recognition like you, you,
(11:30):
you, you go on the first plate and you do a counter moment jump
and it detects or you do a drop jump.
But on the same time you have your dynamometers working around
and you have to be able to connect all those together.
But the app should do the job tomake things easy.
Then once you finish it so that you get your report.
(11:52):
So that was a hassle, my dear Jack.
And I'm very happy to say that way that right now it seems so
simple, but truly it was a huge engineering challenge to make.
But we got it. I mean, we for example, in, in,
back in three years ago, there was a big challenge for us to be
able to, to make all sensors work together and we struggled
(12:15):
in making them wirelessly to be,to be synchronized.
So actually then we bought a company that knew how to do that
and that worked out. So that's one example that
allowed us then to be able to, for example, synchronize force
plates with an EMG sensor, the KM IO plus a motion sensor plus
(12:37):
on the side having the grip on the same time.
And all those events are detected, detected
automatically. So that was a game changer for
us. And I think, you know, for you,
for for you, Jack, I think it was great as well.
And thanks for mentioning it, because yeah.
Yeah, well, he was. He was something that I did.
You know, I can be called many names, but it's rarely naive.
(12:59):
And I think with me, I was beingnaive in assuming that these
devices needed to. Well, it was great that they
were speaking to a similar app. I definitely was selecting
individual tests and just thinking that I needed to wait
around. So just to give the audience a
little bit of a clue as to what I'm specifically getting at here
with the K Delta force plates, Iwas then setting up for a series
of tasks. Might just try and get an
understanding of a bipedal forceforce analysis as how they're
(13:22):
standing. Especially if someone say a
recent orthopaedic patient or anankle sprain that's overloading
one side and you want to give them some data to see that they
are offloading that. They might not even realise
that. So you might just do a static
stand. I was then doing that test,
sometimes even repeating it twice, eyes closed.
Then I was resetting and doing some squats analysis to see if
(13:43):
that pressure feedback was different in under motion.
And it wasn't taking forever, but I was just setting a series
of tests. I had my little test battery
under the under the K mode. It was then clear and I've been
using it since. It's been fantastic.
Where you just then the IT will detect OK, now they're on one
leg. Now they're moving.
Now that it's just the ability to then just set it up and let
(14:04):
it go. And then I just instruct and
just happen to be asking the patient to do a few things
whilst on the plates means that that then he's being measured as
accurately as it was before. That was and is phenomenal.
And I think that people don't realize just how much time that
can save them without compromising the amount of tests
that you do. In fact, it buys you more time
to be able to do more tests and the accuracy isn't compromised.
(14:26):
And that that has been an absolute game changer.
And it's something that I've been so excited to then get get
all the colleagues using not just in our clinic, but beyond,
you know, getting them using that.
And it doesn't stop there because then we came up with
Cassandra. So Cassandra or AI, our
artificial intelligence engine that we built in the last six
months and we integrated in the system.
(14:48):
What, you know, the first challenge that we had is once
you do the testing, then the question is, OK, So what now?
What now? What do I do with the results or
how do I explain those easily tomy patient?
And then we started thinking andthinking and trying and
brainstorming. At the end we came up with
(15:09):
Cassandra. So what Cassandra does in that
specific case is it takes your whole report and choose it by
the way like you Jack. So it actually takes all the
results and builds you a prompt that you can actually change if
you when you can edit it and it gives you a prompt that you can
give to your patients straight away, which we saw it saves 7 to
(15:32):
8 minutes pair assessment session, which is good I suppose
for you guys. And try Cassandra out, you will
be thrilled. I think one of the things that
we've been, we've been playing around with it in recent weeks
and I think that one of the things that's really interesting
about it is not knowing there's two layers, layers to the
(15:53):
interpretation of the data. There's one that like, what are
the variables that I want to document myself under clinical
records? And then the second piece is how
do I want to communicate that toa patient without it completely
intimidating them with jargon and, and those layers, the AI
can assist with both of those, those parts.
And I think it's worth me mentioning now as well, is one
of the things we've, we've said,but just not today is the way in
(16:16):
which that integration, particularly with Clinico has
been fantastic. Because I think that one of the
things that happened and I was noticing with other devices
historically is that I had clinicians that were then having
to sort of, they were never going to make a note of every
precise detail into the into theCRM, sorry, into the
documentation system. And so therefore they were being
(16:37):
selectively biased with what gotnoted or they're even in certain
tests abbreviating without decimal points unnecessarily.
So when sometimes that measurement meant that it was
just getting washed out, whereasnow give event app does not just
give it give the note system everything, but it gives it
enough to be able to be comparable.
(16:57):
And that again, it's not even inone click, it's without click,
right? That is a seamless integration
that can't have been easy either.
So how do you differentiate whatthe hardware and software tell
the documentation system well? First things first is you speak
with, you talk with practitioners and they give you
ideas. Well, the, the hardest part is
(17:19):
when you have talked to 50 people and each one of those has
hear their different opinion andyou have 50 opinions.
And in the end you could just have to decide.
But we are pretty lucky on that because education being in that
level for physiotherapist, well,it helps, right it, I mean, you
guys are mostly aligned generally.
(17:41):
So that's, that's great. And the other thing is about
priority prioritization. I mean clinical is just one of
the integrations we have, but you know maybe every month or
every two months we have someoneproposing an integration with
another system. So the question is where do I
put my engineering force? Am I putting into that
(18:03):
integration or this one, or do Iput it to build a new sensor or
do I put it to put it to bring anew protocol or content?
So this is actually where the biggest challenge is because you
can easily go all over the placeand try to do everything, which
is not possible. But clinical eventually was a
good point for us because actually as soon as we started
(18:23):
getting into the UK or Australia, lots of people
started asking about clinical. It's like, it's like on sports,
it was for example, smarter base.
So we decided we had to do the integration.
And then what kind of metrics toshow?
You start by saying, OK, let's send everything.
And then once you send everything, a physiotherapist,
(18:46):
we might say, guys, I don't understand.
I don't get the point here. I have 100 metrics, which one
should they look it? And then gradually you take and
you end up with three because generally 3 is a magic number
and because the three key metrics generally are more than
(19:08):
in, let's say, enough to define a test for a doctor, a medical
doctor, and the patient on the other side.
Use a physiotherapist. You can dig in if you want, but
three? Generally it's what?
You yeah, that makes sense on onthe on the data and what it
captures. One of the things I wanted to
(19:29):
mention is that our our friend and colleague, Claire Minshaw is
a strength and conditioning coach who's who's been on our
channel many times and on this show has explained about the
frustration that she'd had over some of the claims that were
being made by hardware that couldn't measure what it was
suggesting it would measure. So the things like sampling
frequency, she's explained and unpacked and I can link that
(19:51):
date details to this. So we don't need to go into the
details of what that means in the physics of it.
But people presenting force curves of which literally would
not have a sampling frequency sufficient to create that they
were making assumptions. One of the things that has been
shown in Claire's side by side testing, which of which has a
virgin version 2 just came into my e-mail today, which is really
(20:11):
interesting. So she's is that Kim convinced
devices are measuring accuratelyenough to make make comparisons
in a much more precise. Way what I wondered is when,
when we're thinking about the the pipeline and what is coming
next for Convent, you have developed devices that unlike
(20:32):
other manufacturers are not needing to then revamp their
model in order to make sure thatthey've got accurate sampling
frequencies to meet these standards of the market.
So I'm excited as to where you feel just potentially where do
you think that maybe some of theshortcomings are in Convent
system and what is next for you in terms of development?
Are you able to give us any little spot clues or spoilers?
(20:53):
Sure. Let's well, first of all to say
that I'm very happy with Claire's work, Claire.
Claire is one of the most objective people I met in
judging technology and that is something we appreciate look
here because we we believe that what we make, what it measures
should be the measurement shouldbe transparent.
We should be able to say out there how we would do things
(21:15):
because, well, you know what? It's a scientific work and we
talk about people's health. It's important that the
decisions that some a practitioner will make are based
on facts that those practitioners understand where
they come, they come from. So you talk about acquisition
frequency, it's like how many times in a second we're getting
(21:37):
a measurement from the sensor. We are at 1000 Hertz or more.
It depends on the on the application.
But it was important to be able to put that kind of acquisition
frequency. For example, if you want to
measure rate of force development, if you want to
measure rate of force development, as Claire says, try
to have a big acquisition frequency, 1000 is good.
(21:59):
I mean generally above 500 is can can show you some pretty
good stuff below it's not that good.
So it was a challenge for us to be able to get to 1000 because
you know when we started Bluetooth technology wasn't
where it is today because convert converters of signals of
(22:19):
electric signals were and where they are today.
So we had to work hard on that. But at the end you have to, I
mean you have to get there because this is what technician
needs. So just to say that transparency
is very important. Now what comes next?
Yeah, so recently we came up with K power.
(22:39):
So K power is a sensor that doesdistance, speed, acceleration
and power both horizontally and vertically.
Today what K power does is gate Sprint and velocity based
training. Tomorrow it will also do
positioning. So 1 great thing.
(23:00):
Second great thing is AI. So today we use AI for reports,
for motion capture. Imagine tomorrow AI with two or
three phones giving you a full analysis, 3D analysis of your
human movement in your practice,not in a lab.
(23:21):
So this is something that is coming very, very, very soon.
Wow. And if I were and knowing how
well these sensors integrate, amI right to assume that then a
motion analysis can capturing kinematic forces that you're
describing it done on the K deltas would mean that I get the
kinetic variables as well. Is that right?
Yeah, that's exactly. Yes, that's what we're talking
about that's amazing so so watchthis space folks, that's
(23:46):
amazing. I look forward to that.
I can't wait to I'm already we're already thinking about how
we can demonstrate that and I can't wait to get a play with
that just on the on the on the Kpower.
Tell me how a wearable sensor also being able to to measure
position, that must be as how have you managed to achieve the
(24:07):
sensitivity required for that tobe as precise as I know you?
You wouldn't let anything out there if it wasn't precise for
the reasons we've just talked about before.
You see this as a scientific problem.
So how have you solved that? Because that vagueness has
eluded people for a long time. 3PhDs, that's one, that's one.
So second disc, you know, generally wearable sensors
mostly are mostly based on accelerometers, OK.
(24:31):
An accelerometer can give you acceleration and then with some
math you may get speed in short periods of time, like one second
and and distance forget it. So it wasn't enough.
So we could use an accelerometer, you'll get for
example a range of motion or very, very short movements, but
we wouldn't be able to do it in a Sprint.
(24:51):
So we actually combined accelerometers with ultra wide
band technology. Ultra wide band is actually the
technology people use on their iPhones, for example, when they
do a drop or when your iPhone and your Mac know each other.
So it's actually, it was it was enhanced by Apple in the recent
(25:12):
years. So it's a great, it's a great
technology that gives you distance.
So we were able to do pretty complicated math behind it, but
that's why we were able to fusion those sensors and get the
distance, speed, acceleration and power, and that's how it
works. I, I love the, I love the fact
(25:34):
that you've, you've done that work because it's something
that, as I say, it's evaded manypeople for a long time now
because it was something that technically, you know, there's
many things that can be done in physics because it's the, the
art of the possible. But it's how you actually, then
is it a worthwhile exercise to actually get there for MSK
professionals? That is going to be really
useful. And I think it's exciting to
(25:55):
see. And obviously I can't, I can't
speak to it. I haven't tested it yet, but I
look forward to getting my teethinto that.
I will give you just one exampleon what we discussed here.
We talked about kinematics and kinetics, OK, For example,
change of direction, change of direction.
I mean we talk about ACL's all the time.
When does an ACL happen, an ACL tier?
(26:15):
Is it when I do a counter movement jump?
Of course not. Is it when I do a drum jump?
Of course not. Right now, Until now it was what
we were able to measure. But if you are able to to
actually define the whole changeof direction movement with the
torques on the knee, with the forces and the, let's say the
(26:36):
activation of the quadriceps, then you are able to get factors
against the risk. So This is why, for example, we
worked on the three axis kinematics and kinetics.
That's an example. But at the end, it's what the
market needs that will drive your innovation, not the
opposite. This is the, this is the sort of
stuff that is the the reserve ofmotion labs in research tech.
(27:00):
That is, that is the only place.And even then they've, they've
been, there's been, they've been, they've been lacking in
that direction to some extent. So this is incredible that this
might be accessible to, to clinicians.
I think one of the things just to give people a bit of a clue
is are we rehabilitating? Obviously, I've, I've, I've been
using only some, not all the, the, the, the convent sensors,
but the rehabilitating a, a female, female ACL, just soberly
(27:28):
footballer who on a kinematic variable, sorry, on a kinetic
variable on the force plates andstuff.
It's just, it's squeaky clean. But the way in which the on
observation of movement, we can link the fact that then there's
a hip really hip dominant on thecut, you know, not really
trusting that knee as much as weknow they can.
(27:49):
Fortunately, this is someone that had many, many twists and
turns associated to having been filmed before.
So even though I'd only met her since her injury, the fact that
we could then look across that at the moment, I'm taking the
subjectivity and the skill that comes from, from my analysis of
her movement pre and post injuryto recognize that relevant
difference. And the fact that there's then
both confidence and the decreaserate of force development
(28:10):
associated to her injury force post ACL reconstruction.
And I'm, I'm having to link thatpartly because I've observed it.
What Athan is describing is thatthe technology is going to be
there for us to still be able toapply that skill, but also be
able to numerate that as well and start to notice the
relationship between those kinematic factors, such as a
more hip dominant strategy mightbe breaking at the torso when
(28:33):
she cuts. And the fact that I'm having to
link that at the moment subjectively becomes objective
as well. And we can start to then use our
therapy skill to drill down on how much of this is confidence,
how much of this is tissue capacity.
And the, the art in the science means that the both of those
things can be more accurate. We can, we can zone in, zoom in
much closer to that which is, which is brilliant.
(28:55):
I wanted to just speak to another use case I've noticed
where I admittedly started to use measurement tech
dynamometers many years ago and have gone from everything from
an imprecise crane gauge just topull on stuff through to the
accuracy we're talking about now.
I, I definitely thought that these things were more
applicable in sporty populations.
(29:16):
And of course this they are in many ways they're brilliant.
But rate of force development's a good example where I just
assumed that that would be something where I'd be really
interested in someone's rate of force development purely on a
sort of tissue muscular level, right?
And I thought that in athletic populations that made more
sense. The more I have used it and more
I've been able to rely on the precision that convent have been
able to provide through these sensors, the more I've realized
(29:38):
that we don't necessarily alwaysneed to care why someone might
not be getting a steep rate of force development curve and
being able to generate force quickly.
The underlying fact that they are on say a knee extension test
on, on a cable device, the fact that it might be that they're
building it slowly because of pain and apprehension rather
(29:59):
than it being that they haven't got the capacity.
As a therapist, it's still giving me that data to say we
don't necessarily we need to still unpack why, but the fact
that that curve has been able toshift over time is, is an
important variable for that thatpatient cares about.
And that I didn't realise how many of those things I was
(30:22):
missing. Because you might get a peak
talk that might show the fact that yes, you might they might
have got to a level and be shownto be pretty symmetrical.
And they might say, yeah, it hurt.
But the fact that that curve over the course of that three
seconds was was not a steep is relevant clinical information
that I was otherwise missing in a patient that was not
(30:43):
necessarily athletic. This might be an OANE.
And I had to use them to realisethat.
And I had to use them across thesuite of my patient caseload to
realise what I thought were patients that didn't necessarily
need that level of precision. The precision has still enhanced
my care. And I think I wasn't.
That's an area where I admittedly was naive, I think.
(31:05):
Well, you, you, you are so right.
I mean, let's take the example of fall prevention or Grandma
Michelle that we talked about inearlier.
Grandma Michelle, she, she doesn't want to fall.
Actually, if she falls, that's adisaster because most probably
she will get the surgery. So we'll spend like 6 months on
bed. So Grandma Michelle is already
80 years old. So I mean, her life will change.
(31:27):
So you don't want her to fall. Fall prevention comes from
strength, but it's not just about the strength that you are
able to develop in 345 seconds. If you are going to fall, it
takes one second or half a second.
So Grandma Michelle has to be able to react not to fall.
And that's why we're not talkingabout rate of force development
at 100 milliseconds, but maybe at 500 milliseconds.
(31:50):
But again, once again, it's it'sactually the same story.
And then this is the thing that's it's easy to be
complacent and the thing that these are nice to haves, but
once you start integrating them into your care and also seeing
these exact same variables motivate that compliance with
patients. That's one of the things that's
been interesting either interesting back and forth on
(32:11):
social media with a friend and colleague of ours, Greg Layman
recently about this. And it was he's he's someone
that feels that sometimes these things can be over determined.
But he said, yeah, I get one of the best arguments for it is the
way in which it might motivate compliance with people.
And he understood that that might be something that matters
more to some people than others.And so the fact that we can do
(32:32):
that consistently and have that reliable measurement, it's it's
just something that's really enhancing our care here.
And, and I think that being ableto rely on quality hardware and
software to do that is, is fantastic.
I want to just make sure that people, if the amount of MSK
clinicians that are in a position now where they're
starting to think in that direction, they're starting to
(32:52):
dabble with it. They might be realizing the
limitations of of some of the devices that they they're using,
particularly those that are like, I was a few years ago
trying to use a crane gauge and just sort of pull and push on
stuff. And then just to get a number
out that I was having to then scribble down on a piece of
paper. You know, they need to start,
right? And so anyone that's wanting to
think in this direction wants tounderstand more about what Kim
(33:13):
Venter doing, then click the links surrounding this video
that's going to take you to our dedicated landing page.
Huge thanks to Sarah, Jordan, Melinda and team at Kim Vent for
working on our partnership. It's been a pleasure to work
with. I'm excited for what we do for
next steps because we want to start making sure we can
translate like this video is done today.
Some of the use cases and some of the ways in which the
(33:34):
engineering precision at Conventhave been able to create the
accessible software of which means that you're new and the
patient can understand this better.
As well as the fact that it applies through Physio matters
to NSK cases. And we will try and be as
precise as we can to explain howthat can enhance your care.
So click the link surrounding this and we will be in touch
(33:55):
with more information about how you can better improve the care
for your patients and also to make your life easier in clinic,
which is what Athens team are really passionate about from
from their point of view. But of course that speaks
especially to Physio matters values.
We just care about enhancing best practice in whatever which
way we can and when we can partner with companies whose
values align, that's where the magic happens.
(34:16):
So thank you so much, Ethan, foryour time today.
Really appreciate it. Anything else you want to add
before we wrap up? Pleasure, Jack.
No. Keep safe and keep
rehabilitating. It's very important.
OK. And keep Grandma Michelle happy.
Absolutely. It's all about Grandma Michelle.
Take care mate, and we'll speak soon.
Cheers, bye.
Nathan, welcome to chewing it over.
Why should we measure stuff in MSK anyway?
Actually, it's because if you can measure it, then you can
improve it. That's one reason.
And the second one is you have to build motivation with your
patients, right? It's important that they engage
so that they follow up with a rehab to the end.
Now that's really interesting because that second point is one
(00:26):
of the things that's part of theorigin story of Kinvent.
Like you were inspired by that as an as an athlete and patient
and you felt like your motivation faded if it wasn't
measured. So just tell that story for me,
if you would. Stories that I did 10 years of
competitive sports, rowing and rugby.
And actually, thanks to my favorite sports, I spent more
time with my physical therapies than with my, let's say code,
(00:48):
right? True.
I mean, knees, ankle, I mean, anything, not very serious ones.
I mean, I never had an injury, but every time it was a set of,
let's say, 1020 therapy sessions, right?
Physical therapy. And I, every time, every time
after three sessions, I just wanted to abandon and go do it
(01:10):
by myself at home. You know, you have the
impression of playing with elastics and gym balls in a gym
by yourself. You don't know why you're there.
You don't see your progress. It seems boring.
And I'm an engineer myself and you know, professors and
teachers always told me Athan, you have to base your decisions,
but numbers and curves and therewas none.
(01:32):
So this started as a frustration.
And after I actually stopped my sessions, that was also
frustrating for my physical therapist, of course, right.
You guys physiotherapist, you love your work.
You are among the most passionate people I've met for
their work. I mean, if if you just leave 2
(01:52):
physios together by themselves and in a group of people, they
will turn to each other and somestart discussing about the ACL
they treated the other day and what kind of test they did and
that kind of stuff. So the why behind Kinvent is to
build a system that will help both those guys, the patients
and the practitioners, so that you can measure progress, but
(02:16):
most importantly build engagement.
And that's why you need to measure it.
And so you're what's really interesting is that your
hardware and software really seem to speak to that dual aim,
which is great because the usability and the precision have
been really crucial to what you're doing.
So, so tell me about the engineering problem solving part
(02:37):
of you that then it was inspiredto to drive building what you've
built. So the, the whole story starts
when I was very, very, very young because actually my dad
was a bio mechanics professor. Now he's tired, he's happy with
his four or five grandkids. But at the time when I was very,
very young, like let's say one or two years old, I was in his
(03:00):
lab and he was building his own sensors because he didn't have
any budget back in Greece. So this is where bio mechanics
started for me and this is whereI learned the basic stuff and
then I did some engineering studies because I always wanted
to build something for let's sayathletes.
And then my physical therapy experiences helped me to go
(03:22):
towards that path. So the idea was how can we use
the science bio mechanics to give insights that practitioners
but even more patients or athletes understand?
So the way we tried to build oursystem is that is scientific.
So it measures the right thing, but also the interfaces are made
(03:44):
in a way. So even your patient, I mean,
let's say Grandma Michelle for example, she comes in for her
hip surgery. You want her to understand why
she's there. You want her to understand why
she actually her strength on thehip extension, for example, is
growing, right? So that's what does she want.
She just wants to walk back again and get her grandkid in
(04:08):
her, in her arms. And I think that that's one of
the things that's particularly interesting as I've got to know
the companies, I've got to know you a little better.
The engineering, sort of the physics of the matter, the
numbers of the matter, the measurement, the precision that
is rarely embodied in the same person.
That's also got a passion for the individual humans, right?
(04:30):
I know I'm, I'm speaking to cliches a little bit there.
But sometimes that it's reasonable in humans.
And we know the data on this by personality that sometimes it's
you've got those that are sort of really into the technical
things that are with the gadgetsand gizmos and stripping back
down parts. And then you've got those that
are sort of really motivated andempathizing with the individual
patient, especially those that are injured.
And then there's interesting where that Venn diagram can
(04:52):
intersect. You really speak to that middle
where you're motivated by both things.
Did you notice quite quickly in your career that that was quite
unique? Well, there was a journalist
once that said that there is a count of a thing called the
founder market Fit. And I'm happy to say that I love
this space. I mean, you know, you grow with
(05:13):
what you have. You are surrounded with, so I, I
think I was pretty good in, let's say, mechanics and
engineering. So, you know, I did my studies
between France, Greece, a bit inthe US, electronics, mechanics,
bio mechanics, all that was great.
And it was, let's say my passion.
And then the clinical expertise.I'm not the clinical guy for
(05:36):
human that we have physical therapists, coaches, doctors
working with us. So they know what's best.
But then the most important thing is to understand your
customer and the only way of understanding your customer,
specifically a market like physical therapists.
I'm not a physical therapist. I cannot just put myself in the
skin of you, Jack. But you have to spend a lot of
(05:59):
time with them. And once you spend time, you
also get the empathy because youspend time with people that love
what they do, that they have sessions that are not honored,
patients not not coming in, theyare not paid so well, but they
have a level of education that is extremely amazing.
(06:21):
And so you get the empathy and you get to understand those.
So once you do that, if you understand the engineering
behind, you can lead the team todo the right thing.
That's right. And I think that that clearly
has sort of motivated your passion to retain the interest
in both product and people. And so I want to get into
product with you because that was something that when we're
(06:43):
using your hardware. So particularly in in clinic at
the moment we're every, every day we are using the
dynamometers and the and the K deltas and the way in which that
is doing what you describe, which is motivating compliance
with patients, but also incentivizing our clinicians to
better get accurate outcome measures from patients and, and
(07:06):
helping them to helping the patient understand that the
software is assisting with that.And then the the user interface
is, is fantastic. What has been the driver for you
to be building the specifics of the hardware?
Like did you know where you definitely wanted to start?
And then do you, when you're creating a product pipeline like
what is sort of motivating the decisions and the priorities
(07:29):
there? So let's give people a bit of an
idea of what Quinvent is. Quinvent is 10 sensors, all of
them connected to one single appcalled Quinvent App to assess
and train strength, balance, motion, speed, explosiveness
etcetera. Not only assess but also train
because training is important. It's not assessment that will
(07:50):
bring patients forward, but training, assessment will show
them where they are and where they're getting right.
Yes. Now this is conventionally and
among those 10 sensors there arethe dynamometers, there is the
force plates, there is range of motion, speed, velocity based
training etcetera, etcetera. Now we didn't come up with all
(08:11):
the 10 at once, OK, that would be, that would be a miracle if
we did. But thing is that we started
with what the market was asking.So the market first was asking
for tools to measure strength. Why strength?
Because strength until that moment was pretty subjective in
(08:33):
physical physiotherapy. So do you remember the Oxford
scale from zero to five? Yeah, exactly.
So there was some things interest, some interesting
things to do there. And it's, I mean the mark, the
professionals will use what theyhave in their hands.
And until that moment, technologies wouldn't allow to
do a strength measurement fast in a clinical setting in less
(08:57):
than 5 minutes, right. You have to go to the lab with
the PhDs with the expensive equipment and spend hours there
and then weeks to get the results.
So Kinvent, we tried with Kinvent to change that, to make
those technologies easy to get in practice.
So that's why the first thing that people asked for was
strength. The second thing was balance.
(09:19):
You have someone coming in aftera century surgery and you want
to simply, for example, see how they load left and right and try
to work on that. And that's how we started force
plates, small ones for balance, strength for body, for the whole
body and grip. And then just listening to what
(09:41):
the market would ask took us to get range of motion, bigger
strength. So for for bigger bus groups,
then explosiveness, biometrics, jumps, sprints, gate velocity
based training, all those things, we added them
(10:01):
progressively. Yeah.
And I think that that's something that really makes
sense then for the question of prioritising is that you're
listening carefully to what the market needs, what the patients
and and therapists are requiringto best enhance their treatment
and their care. One of the things that's been
and I'll admit now to the audience that it took for us to
(10:23):
have the devices and to be usingthem and using them to good
effect. But then realising that we
weren't optimising them. Jump on a call with Ethan and he
he clearly shows that I was overseparating tests that was
actually taking. I still thought it was pretty
quick because it was better thanmost devices we've used before.
But they they this is the thing that you have optimized the
software and the detection mechanism, especially with the
(10:45):
with the K mode and things. So just tell me a little bit
about that. And because that that's that's
been a really interesting way that that even I thought that we
were doing well, but actually we're only using it so.
So First things first is you guys, practitioners, you don't
have time. So even one click, one click
more can be one too much. So we have an obsession to make
(11:10):
the system to have as less clicks as possible.
OK, so it's not always easy, butthis is let's say one of the
main specifications that we put to our system as less clicks as
possible. So, but, but to do that,
sometimes you need to enhance the technology.
So you talk about, for example, auto recognition like you, you,
(11:30):
you, you go on the first plate and you do a counter moment jump
and it detects or you do a drop jump.
But on the same time you have your dynamometers working around
and you have to be able to connect all those together.
But the app should do the job tomake things easy.
Then once you finish it so that you get your report.
(11:52):
So that was a hassle, my dear Jack.
And I'm very happy to say that way that right now it seems so
simple, but truly it was a huge engineering challenge to make.
But we got it. I mean, we for example, in, in,
back in three years ago, there was a big challenge for us to be
able to, to make all sensors work together and we struggled
(12:15):
in making them wirelessly to be,to be synchronized.
So actually then we bought a company that knew how to do that
and that worked out. So that's one example that
allowed us then to be able to, for example, synchronize force
plates with an EMG sensor, the KM IO plus a motion sensor plus
(12:37):
on the side having the grip on the same time.
And all those events are detected, detected
automatically. So that was a game changer for
us. And I think, you know, for you,
for for you, Jack, I think it was great as well.
And thanks for mentioning it, because yeah.
Yeah, well, he was. He was something that I did.
You know, I can be called many names, but it's rarely naive.
(12:59):
And I think with me, I was beingnaive in assuming that these
devices needed to. Well, it was great that they
were speaking to a similar app. I definitely was selecting
individual tests and just thinking that I needed to wait
around. So just to give the audience a
little bit of a clue as to what I'm specifically getting at here
with the K Delta force plates, Iwas then setting up for a series
of tasks. Might just try and get an
understanding of a bipedal forceforce analysis as how they're
(13:22):
standing. Especially if someone say a
recent orthopaedic patient or anankle sprain that's overloading
one side and you want to give them some data to see that they
are offloading that. They might not even realise
that. So you might just do a static
stand. I was then doing that test,
sometimes even repeating it twice, eyes closed.
Then I was resetting and doing some squats analysis to see if
(13:43):
that pressure feedback was different in under motion.
And it wasn't taking forever, but I was just setting a series
of tests. I had my little test battery
under the under the K mode. It was then clear and I've been
using it since. It's been fantastic.
Where you just then the IT will detect OK, now they're on one
leg. Now they're moving.
Now that it's just the ability to then just set it up and let
(14:04):
it go. And then I just instruct and
just happen to be asking the patient to do a few things
whilst on the plates means that that then he's being measured as
accurately as it was before. That was and is phenomenal.
And I think that people don't realize just how much time that
can save them without compromising the amount of tests
that you do. In fact, it buys you more time
to be able to do more tests and the accuracy isn't compromised.
(14:26):
And that that has been an absolute game changer.
And it's something that I've been so excited to then get get
all the colleagues using not just in our clinic, but beyond,
you know, getting them using that.
And it doesn't stop there because then we came up with
Cassandra. So Cassandra or AI, our
artificial intelligence engine that we built in the last six
months and we integrated in the system.
(14:48):
What, you know, the first challenge that we had is once
you do the testing, then the question is, OK, So what now?
What now? What do I do with the results or
how do I explain those easily tomy patient?
And then we started thinking andthinking and trying and
brainstorming. At the end we came up with
(15:09):
Cassandra. So what Cassandra does in that
specific case is it takes your whole report and choose it by
the way like you Jack. So it actually takes all the
results and builds you a prompt that you can actually change if
you when you can edit it and it gives you a prompt that you can
give to your patients straight away, which we saw it saves 7 to
(15:32):
8 minutes pair assessment session, which is good I suppose
for you guys. And try Cassandra out, you will
be thrilled. I think one of the things that
we've been, we've been playing around with it in recent weeks
and I think that one of the things that's really interesting
about it is not knowing there's two layers, layers to the
(15:53):
interpretation of the data. There's one that like, what are
the variables that I want to document myself under clinical
records? And then the second piece is how
do I want to communicate that toa patient without it completely
intimidating them with jargon and, and those layers, the AI
can assist with both of those, those parts.
And I think it's worth me mentioning now as well, is one
of the things we've, we've said,but just not today is the way in
(16:16):
which that integration, particularly with Clinico has
been fantastic. Because I think that one of the
things that happened and I was noticing with other devices
historically is that I had clinicians that were then having
to sort of, they were never going to make a note of every
precise detail into the into theCRM, sorry, into the
documentation system. And so therefore they were being
(16:37):
selectively biased with what gotnoted or they're even in certain
tests abbreviating without decimal points unnecessarily.
So when sometimes that measurement meant that it was
just getting washed out, whereasnow give event app does not just
give it give the note system everything, but it gives it
enough to be able to be comparable.
(16:57):
And that again, it's not even inone click, it's without click,
right? That is a seamless integration
that can't have been easy either.
So how do you differentiate whatthe hardware and software tell
the documentation system well? First things first is you speak
with, you talk with practitioners and they give you
ideas. Well, the, the hardest part is
(17:19):
when you have talked to 50 people and each one of those has
hear their different opinion andyou have 50 opinions.
And in the end you could just have to decide.
But we are pretty lucky on that because education being in that
level for physiotherapist, well,it helps, right it, I mean, you
guys are mostly aligned generally.
(17:41):
So that's, that's great. And the other thing is about
priority prioritization. I mean clinical is just one of
the integrations we have, but you know maybe every month or
every two months we have someoneproposing an integration with
another system. So the question is where do I
put my engineering force? Am I putting into that
(18:03):
integration or this one, or do Iput it to build a new sensor or
do I put it to put it to bring anew protocol or content?
So this is actually where the biggest challenge is because you
can easily go all over the placeand try to do everything, which
is not possible. But clinical eventually was a
good point for us because actually as soon as we started
(18:23):
getting into the UK or Australia, lots of people
started asking about clinical. It's like, it's like on sports,
it was for example, smarter base.
So we decided we had to do the integration.
And then what kind of metrics toshow?
You start by saying, OK, let's send everything.
And then once you send everything, a physiotherapist,
(18:46):
we might say, guys, I don't understand.
I don't get the point here. I have 100 metrics, which one
should they look it? And then gradually you take and
you end up with three because generally 3 is a magic number
and because the three key metrics generally are more than
(19:08):
in, let's say, enough to define a test for a doctor, a medical
doctor, and the patient on the other side.
Use a physiotherapist. You can dig in if you want, but
three? Generally it's what?
You yeah, that makes sense on onthe on the data and what it
captures. One of the things I wanted to
(19:29):
mention is that our our friend and colleague, Claire Minshaw is
a strength and conditioning coach who's who's been on our
channel many times and on this show has explained about the
frustration that she'd had over some of the claims that were
being made by hardware that couldn't measure what it was
suggesting it would measure. So the things like sampling
frequency, she's explained and unpacked and I can link that
(19:51):
date details to this. So we don't need to go into the
details of what that means in the physics of it.
But people presenting force curves of which literally would
not have a sampling frequency sufficient to create that they
were making assumptions. One of the things that has been
shown in Claire's side by side testing, which of which has a
virgin version 2 just came into my e-mail today, which is really
(20:11):
interesting. So she's is that Kim convinced
devices are measuring accuratelyenough to make make comparisons
in a much more precise. Way what I wondered is when,
when we're thinking about the the pipeline and what is coming
next for Convent, you have developed devices that unlike
(20:32):
other manufacturers are not needing to then revamp their
model in order to make sure thatthey've got accurate sampling
frequencies to meet these standards of the market.
So I'm excited as to where you feel just potentially where do
you think that maybe some of theshortcomings are in Convent
system and what is next for you in terms of development?
Are you able to give us any little spot clues or spoilers?
(20:53):
Sure. Let's well, first of all to say
that I'm very happy with Claire's work, Claire.
Claire is one of the most objective people I met in
judging technology and that is something we appreciate look
here because we we believe that what we make, what it measures
should be the measurement shouldbe transparent.
We should be able to say out there how we would do things
(21:15):
because, well, you know what? It's a scientific work and we
talk about people's health. It's important that the
decisions that some a practitioner will make are based
on facts that those practitioners understand where
they come, they come from. So you talk about acquisition
frequency, it's like how many times in a second we're getting
(21:37):
a measurement from the sensor. We are at 1000 Hertz or more.
It depends on the on the application.
But it was important to be able to put that kind of acquisition
frequency. For example, if you want to
measure rate of force development, if you want to
measure rate of force development, as Claire says, try
to have a big acquisition frequency, 1000 is good.
(21:59):
I mean generally above 500 is can can show you some pretty
good stuff below it's not that good.
So it was a challenge for us to be able to get to 1000 because
you know when we started Bluetooth technology wasn't
where it is today because convert converters of signals of
(22:19):
electric signals were and where they are today.
So we had to work hard on that. But at the end you have to, I
mean you have to get there because this is what technician
needs. So just to say that transparency
is very important. Now what comes next?
Yeah, so recently we came up with K power.
(22:39):
So K power is a sensor that doesdistance, speed, acceleration
and power both horizontally and vertically.
Today what K power does is gate Sprint and velocity based
training. Tomorrow it will also do
positioning. So 1 great thing.
(23:00):
Second great thing is AI. So today we use AI for reports,
for motion capture. Imagine tomorrow AI with two or
three phones giving you a full analysis, 3D analysis of your
human movement in your practice,not in a lab.
(23:21):
So this is something that is coming very, very, very soon.
Wow. And if I were and knowing how
well these sensors integrate, amI right to assume that then a
motion analysis can capturing kinematic forces that you're
describing it done on the K deltas would mean that I get the
kinetic variables as well. Is that right?
Yeah, that's exactly. Yes, that's what we're talking
about that's amazing so so watchthis space folks, that's
(23:46):
amazing. I look forward to that.
I can't wait to I'm already we're already thinking about how
we can demonstrate that and I can't wait to get a play with
that just on the on the on the Kpower.
Tell me how a wearable sensor also being able to to measure
position, that must be as how have you managed to achieve the
(24:07):
sensitivity required for that tobe as precise as I know you?
You wouldn't let anything out there if it wasn't precise for
the reasons we've just talked about before.
You see this as a scientific problem.
So how have you solved that? Because that vagueness has
eluded people for a long time. 3PhDs, that's one, that's one.
So second disc, you know, generally wearable sensors
mostly are mostly based on accelerometers, OK.
(24:31):
An accelerometer can give you acceleration and then with some
math you may get speed in short periods of time, like one second
and and distance forget it. So it wasn't enough.
So we could use an accelerometer, you'll get for
example a range of motion or very, very short movements, but
we wouldn't be able to do it in a Sprint.
(24:51):
So we actually combined accelerometers with ultra wide
band technology. Ultra wide band is actually the
technology people use on their iPhones, for example, when they
do a drop or when your iPhone and your Mac know each other.
So it's actually, it was it was enhanced by Apple in the recent
(25:12):
years. So it's a great, it's a great
technology that gives you distance.
So we were able to do pretty complicated math behind it, but
that's why we were able to fusion those sensors and get the
distance, speed, acceleration and power, and that's how it
works. I, I love the, I love the fact
(25:34):
that you've, you've done that work because it's something
that, as I say, it's evaded manypeople for a long time now
because it was something that technically, you know, there's
many things that can be done in physics because it's the, the
art of the possible. But it's how you actually, then
is it a worthwhile exercise to actually get there for MSK
professionals? That is going to be really
useful. And I think it's exciting to
(25:55):
see. And obviously I can't, I can't
speak to it. I haven't tested it yet, but I
look forward to getting my teethinto that.
I will give you just one exampleon what we discussed here.
We talked about kinematics and kinetics, OK, For example,
change of direction, change of direction.
I mean we talk about ACL's all the time.
When does an ACL happen, an ACL tier?
(26:15):
Is it when I do a counter movement jump?
Of course not. Is it when I do a drum jump?
Of course not. Right now, Until now it was what
we were able to measure. But if you are able to to
actually define the whole changeof direction movement with the
torques on the knee, with the forces and the, let's say the
(26:36):
activation of the quadriceps, then you are able to get factors
against the risk. So This is why, for example, we
worked on the three axis kinematics and kinetics.
That's an example. But at the end, it's what the
market needs that will drive your innovation, not the
opposite. This is the, this is the sort of
stuff that is the the reserve ofmotion labs in research tech.
(27:00):
That is, that is the only place.And even then they've, they've
been, there's been, they've been, they've been lacking in
that direction to some extent. So this is incredible that this
might be accessible to, to clinicians.
I think one of the things just to give people a bit of a clue
is are we rehabilitating? Obviously, I've, I've, I've been
using only some, not all the, the, the, the convent sensors,
but the rehabilitating a, a female, female ACL, just soberly
(27:28):
footballer who on a kinematic variable, sorry, on a kinetic
variable on the force plates andstuff.
It's just, it's squeaky clean. But the way in which the on
observation of movement, we can link the fact that then there's
a hip really hip dominant on thecut, you know, not really
trusting that knee as much as weknow they can.
(27:49):
Fortunately, this is someone that had many, many twists and
turns associated to having been filmed before.
So even though I'd only met her since her injury, the fact that
we could then look across that at the moment, I'm taking the
subjectivity and the skill that comes from, from my analysis of
her movement pre and post injuryto recognize that relevant
difference. And the fact that there's then
both confidence and the decreaserate of force development
(28:10):
associated to her injury force post ACL reconstruction.
And I'm, I'm having to link thatpartly because I've observed it.
What Athan is describing is thatthe technology is going to be
there for us to still be able toapply that skill, but also be
able to numerate that as well and start to notice the
relationship between those kinematic factors, such as a
more hip dominant strategy mightbe breaking at the torso when
(28:33):
she cuts. And the fact that I'm having to
link that at the moment subjectively becomes objective
as well. And we can start to then use our
therapy skill to drill down on how much of this is confidence,
how much of this is tissue capacity.
And the, the art in the science means that the both of those
things can be more accurate. We can, we can zone in, zoom in
much closer to that which is, which is brilliant.
(28:55):
I wanted to just speak to another use case I've noticed
where I admittedly started to use measurement tech
dynamometers many years ago and have gone from everything from
an imprecise crane gauge just topull on stuff through to the
accuracy we're talking about now.
I, I definitely thought that these things were more
applicable in sporty populations.
(29:16):
And of course this they are in many ways they're brilliant.
But rate of force development's a good example where I just
assumed that that would be something where I'd be really
interested in someone's rate of force development purely on a
sort of tissue muscular level, right?
And I thought that in athletic populations that made more
sense. The more I have used it and more
I've been able to rely on the precision that convent have been
able to provide through these sensors, the more I've realized
(29:38):
that we don't necessarily alwaysneed to care why someone might
not be getting a steep rate of force development curve and
being able to generate force quickly.
The underlying fact that they are on say a knee extension test
on, on a cable device, the fact that it might be that they're
building it slowly because of pain and apprehension rather
(29:59):
than it being that they haven't got the capacity.
As a therapist, it's still giving me that data to say we
don't necessarily we need to still unpack why, but the fact
that that curve has been able toshift over time is, is an
important variable for that thatpatient cares about.
And that I didn't realise how many of those things I was
(30:22):
missing. Because you might get a peak
talk that might show the fact that yes, you might they might
have got to a level and be shownto be pretty symmetrical.
And they might say, yeah, it hurt.
But the fact that that curve over the course of that three
seconds was was not a steep is relevant clinical information
that I was otherwise missing in a patient that was not
(30:43):
necessarily athletic. This might be an OANE.
And I had to use them to realisethat.
And I had to use them across thesuite of my patient caseload to
realise what I thought were patients that didn't necessarily
need that level of precision. The precision has still enhanced
my care. And I think I wasn't.
That's an area where I admittedly was naive, I think.
(31:05):
Well, you, you, you are so right.
I mean, let's take the example of fall prevention or Grandma
Michelle that we talked about inearlier.
Grandma Michelle, she, she doesn't want to fall.
Actually, if she falls, that's adisaster because most probably
she will get the surgery. So we'll spend like 6 months on
bed. So Grandma Michelle is already
80 years old. So I mean, her life will change.
(31:27):
So you don't want her to fall. Fall prevention comes from
strength, but it's not just about the strength that you are
able to develop in 345 seconds. If you are going to fall, it
takes one second or half a second.
So Grandma Michelle has to be able to react not to fall.
And that's why we're not talkingabout rate of force development
at 100 milliseconds, but maybe at 500 milliseconds.
(31:50):
But again, once again, it's it'sactually the same story.
And then this is the thing that's it's easy to be
complacent and the thing that these are nice to haves, but
once you start integrating them into your care and also seeing
these exact same variables motivate that compliance with
patients. That's one of the things that's
been interesting either interesting back and forth on
(32:11):
social media with a friend and colleague of ours, Greg Layman
recently about this. And it was he's he's someone
that feels that sometimes these things can be over determined.
But he said, yeah, I get one of the best arguments for it is the
way in which it might motivate compliance with people.
And he understood that that might be something that matters
more to some people than others.And so the fact that we can do
(32:32):
that consistently and have that reliable measurement, it's it's
just something that's really enhancing our care here.
And, and I think that being ableto rely on quality hardware and
software to do that is, is fantastic.
I want to just make sure that people, if the amount of MSK
clinicians that are in a position now where they're
starting to think in that direction, they're starting to
(32:52):
dabble with it. They might be realizing the
limitations of of some of the devices that they they're using,
particularly those that are like, I was a few years ago
trying to use a crane gauge and just sort of pull and push on
stuff. And then just to get a number
out that I was having to then scribble down on a piece of
paper. You know, they need to start,
right? And so anyone that's wanting to
think in this direction wants tounderstand more about what Kim
(33:13):
Venter doing, then click the links surrounding this video
that's going to take you to our dedicated landing page.
Huge thanks to Sarah, Jordan, Melinda and team at Kim Vent for
working on our partnership. It's been a pleasure to work
with. I'm excited for what we do for
next steps because we want to start making sure we can
translate like this video is done today.
Some of the use cases and some of the ways in which the
(33:34):
engineering precision at Conventhave been able to create the
accessible software of which means that you're new and the
patient can understand this better.
As well as the fact that it applies through Physio matters
to NSK cases. And we will try and be as
precise as we can to explain howthat can enhance your care.
So click the link surrounding this and we will be in touch
(33:55):
with more information about how you can better improve the care
for your patients and also to make your life easier in clinic,
which is what Athens team are really passionate about from
from their point of view. But of course that speaks
especially to Physio matters values.
We just care about enhancing best practice in whatever which
way we can and when we can partner with companies whose
values align, that's where the magic happens.
(34:16):
So thank you so much, Ethan, foryour time today.
Really appreciate it. Anything else you want to add
before we wrap up? Pleasure, Jack.
No. Keep safe and keep
rehabilitating. It's very important.
OK. And keep Grandma Michelle happy.
Absolutely. It's all about Grandma Michelle.
Take care mate, and we'll speak soon.
Cheers, bye.
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