And today I'm delighted to be joined by Ian Howard from the University of Plymouth. And Ian, your research is about rehabilitation and recovery, is that right?

I'm also interested in, you know, how can we learn much more novel things? So controlling things in a way that we don't normally control it. So actually tool use is one example, but also more complicated things would constitute things like, you know, how do you learn to drive?

You know, you've got to learn a different kind of mapping from your arms to actually move the vehicle or the application I like the most at the moment is how would you learn to control an excavator? Now, you've got two levers and you've got to move them in a certain way, which don't really correspond to what you normally do. And I think in that kind of task, there is going to be some more structural learning in terms of remapping what you're already doing.

But yeah, I think it is to do with neural plasticity in as much as this information has got to be represented somewhere in your brain. And the only way it can be represented really long term is in connections. And that's what this some of these learning mechanisms will result in.

So you're kind of finding some way to accommodate so that you can adapt.

And how can we actually maybe increase the way that it actually adopts using these new areas? And that's kind of where rehabilitation, I guess, comes into these things.

So it's one of those things that you do need to be focused and applying your your, if you like, your attention to these tasks. Some things you do actually learn a lot more subconsciously. It has to be said some there are subconscious things.

But generally speaking, if you don't care about what you're doing, you won't learn as much as fast.

And now it's really challenging and difficult.

And I think probably what happens when we get a bit older is we don't make as much effort. And certainly in another aspect of learning, actually, which I've also experienced is learning another language. So as a child, you're very good at picking things up.

And I think part of the reason is you try a lot harder and you don't care if you make mistakes. You haven't got this embarrassment. Whereas an adult, certainly I've noticed this learning languages, which is also a motor task in a large to a large degree, people are frightened of saying the wrong thing and looking silly.

So I think I think the motivation aspect of this is a really key, you know, and the intensity that you do things in is really, really key to these things. And I think as children, you know, we are not so bothered about making mistakes and doing things wrong and prepared to fall over. Whereas probably we're not quite so prepared.

Another thing, of course, being smaller and closer to the ground. If you're a child, you know, not as much damage can take place if something goes wrong. So, you know, you're learning, if you like, lots of things at an age where it's much more difficult to do anything, you know, which is potentially damaging to yourself as well.

Measure kind of movement of arms.

So it'd be a mouse with force feedback. So that's the first aspect of it. So we can actually quantify movement very well using these devices, but we can also change the nature of the task in an online fashion. So you could be moving something which is just basically like a like an empty cup. And then we could simulate lots of mass, for example, and you'd be moving this very heavy cup around full of full of a liquid or something.

Or we can do other simulations. We could actually change the way you look at the world by rotating it slightly. Or we can do things like put in some, if you like, less natural dynamics, like a classic one is like a curl field.

So it's like moving in a viscous field, like moving underwater, but it pushes you at 90 degrees. And then we can look at how you learn on these things. So all of these techniques have been heavily developed and used in the scientific research in motor control.

And the idea of this project is, you know, we've got these big systems that we've got in the laboratory. Wouldn't it be nice if you had a little tiny one you could put in a suitcase and take to a clinic or to take to the patients? In fact, we're planning on doing that kind of thing for events.

So it's really an application of this technology, which has been developed a lot for scientific measurement, to actually then get it out there a bit more in the field. And the idea is to use a small, tiny little robot, which is the thing that this project, my project is all about with my colleagues, which is then relatively cheap to produce. It's all 3D printed.

And of course, rather than being in a two dimensional space, we've actually built a three dimensional one as well, which is also fairly novel in this field. So the idea is you've got this portable system which you can take out to people, which isn't too expensive, and hopefully quantify their movement, not just in two but three dimensions. And the idea is also to let you change some of the qualities of what you're actually doing with the movement by giving force feedback as well.

She was always explaining that, you know, one of the very basic things that they do quite often to quantify how good people are at moving after they've had an accident or had a stroke is watch them make a cup of tea. Right. Which involves a lot of motor skills.

You know, we take it for granted, but it involves a lot of motor skill. But obviously that's a subjective measurement. So the idea really of this project was to say, can we use some of these technologies and quantify things in a way that we're doing for our scientific research?

Can we use that as a metric for saying how good a particular person is maybe on a particular day? And if we can do that, then we can also look at the progress that they're making and say whether or not, you know, there is any in a quantitative way. You know, it's not a matter of opinion anymore.

It's like it's, you know, the computers working out your trajectories. So that's really the that was the main driving force of this to try to quantify things in a way and get away really from sort of these subjective judgments that we've had in the past.

So but I think just to start off with, you know, just how accurate are you? How hard can you push and how can you move around the you know, your local workspace? Can you do that with without too much trouble?

You know, how jerky is your movement? This is another course. This is another issue that we we will come across a bit more since at the moment we've been we've been, you know, in the past running on fairly normal people or maybe elderly people who also volunteer and which means they're generally very good.

You know, when you come across people really with with serious issues, they can't even move in a straight line without stopping a few times. So but I think all of this is a is a very useful tool for quantifying all of this kind of behaviour. I have to say, I think also it's very interesting from a scientific perspective as well, understanding what's going on more, because I think there's probably not as much done from my community, sensori-motor community on people with injuries as there could be.

And always when something goes wrong in the system, it's also a good way of trying to understand how it's working. So I think it will feed back in both directions. We can do some of this thing, this kind of things, you know, and the more we understand about motor control and how the brain controls movement, obviously probably the more we can actually do to help people improve when they've something's gone wrong.

And also, I guess it's going to be different for everybody. So some people make quite quick progress. Other people, it's going to be much, much slower.

You know, it could be years before they really start to regain function.

That seems to be quite an important issue.

And I think another interesting aspect is, you know, we've also lots of people have done work on it now. You know, the haptic feedback is actually very important for us to operate because, first of all, it's much faster to get back to our nervous system than vision, right? So vision is actually running with quite a large latency because it's got a lot of processing stages to go through.

So I think an advantage of having a system like this, as opposed to these sort of accelerometer based systems or tracking systems, is that we'll be able to actually get feedback, you know, generate haptic feedback on people by pushing back on them as well. So hopefully that will be another nice aspect of this of this little.

And we get some response for them. And you're saying your system can provide that, right? You can give us a bit of a pushback.

That was the idea. And I know they're using them a lot now and they're selling them a lot for designers and stuff so they can actually even feel their designs and stuff like that. You can actually have interactions with these things.

So I think it's a very useful way of interacting rather than rather than just using a mouse in two dimensions, actually having something you can actually feel as well in three.

But in fact, we're going to be meeting my colleagues shortly and have a big discussion about this. How do we move it forward? The other thing, actually, I would also be interested in doing is this is obviously one little tiny system.

I'd be interesting in looking at, you know, more general systems that can actually look at both arms and both legs at the same time. Right. So you've got a complete, if you like, a body assessment kind of system.

So you could actually, you know, you can get somebody basically standing there or sitting there maybe. So you can actually assess their entire body and how they coordinate that as well at the same time, not just one particular arm. And I think also underpinning a lot of this this kind of research is we do need sort of to have some very, if you like, maybe very, very reliable, bigger systems to actually support testing of the smaller ones.

So, you know, you need a sort of a ground zero kind of approach that we have a really big system. We've got a really big version of this robot already in two dimensions and three dimensions, which isn't portable. So I'm kind of thinking, you know, there should be sort of a parallel development between things that you can take out into the field and really what you consider to be almost like the ultimate you can do in order to show that this is a really effective way of actually doing some recordings.

And can we then see the same kind of trends using a much smaller, more portable system?

And I'll put some links to some of the things that Ian's been talking about in this chat. So do check those out, too.