All Thumbs: A New Trick For Dexterity In Prosthetic Hands
Researchers working on the next generation of prosthetic limbs have a few fundamental engineering problems to overcome. For starters, how can people using prosthetic limbs effectively signal what motions they want to perform?
In the case of hands, the options include reading the signals from muscle contractions in the remaining portion of a person’s arm, which may be insufficient in controlling all fingers, especially thumbs. Or there are the nerves, which can send signals to direct every finger in precise motions—but the signal itself is faint and difficult to translate into movement. Brain control is another technology that has many miles to go before it’s ready.
But now, a team of researchers may have a solution: A surgical technique that uses muscle tissue to amplify the nerve signals. Participants fitted with prosthetic hands after this surgery, described in Science Translational Medicine this week, reported being able to manipulate objects with a degree of control and dexterity not previously seen. Electrical engineer Cynthia Chestek at the University of Michigan explains why this muscle graft seems to be solving the engineering problem of reading nerve signals and what the next generation of prosthetic hands could be capable of.
Cindy Chestek is an associate professor of Biomedical and Electrical Engineering at the University of Michigan in Ann Arbor, Michigan.
Paul Cederna is Chief of Plastic Surgery and a professor of Biomedical Engineering at the University of Michigan in Ann Arbor, Michigan.
IRA FLATOW: This is Science Friday. I’m Ira Flatow. The quest continues for an artificial hand that can do everything a biological hand can. And while robotic hands can perform impressive feats when guided by computers, amputees still have limited options for communicating with those hands, especially in a way that gives them nuanced mobility. Simple opening and closing motions, yes, of course, but nothing near that three dimensional dexterity that biological hands are capable of.
But new research out of the University of Michigan this week could give the field a chance to get much, much closer with a surgical technique that makes nerve signals strong enough to control an artificial hand’s robotic fingers. Surgeon Paul Cederna, part of the research team, explains.
PAUL CEDERNA: A small piece of muscle, 3 centimeters, by 1.5 centimeters, by 5 millimeters thick. And you can take that small piece of muscle from anywhere, wrap that around the end of the nerve. What happens then is, when that nerve then starts sprouting those branches like it doesn’t when it forms a neuroma, in this case it sprouts those branches. But the branches go in and reinnervate the muscle.
Now, you have a piece of muscle on the end of the nerve. So we’ve known for decades that muscles create huge signals. So then when your brain thinks, move my thumb, the signal comes from the brain, down the spinal cord, down along the peripheral nerve. And that signal gets to the muscle. That little muscle on the end of that nerve contracts, and it creates a huge signal. So we have then the ability to amplify those peripheral nerve signals 10 to 100-fold.
IRA FLATOW: Surgeon Paul Cederna of the University of Michigan. Here to explain more about why that’s exciting and what the future generation of prosthetics could be capable of is Dr. Cindy Chestek, associate professor of Biomedical and Electrical Engineering, University of Michigan in Ann Arbor. Welcome to Science Friday.
CINDY CHESTEK: Thank you very much for having me.
IRA FLATOW: You probably were listening along with us as co-author Paul Cederna talked about his surgical technique in which he was amplifying the nerve signal that people use to communicate with their limbs. Tell us how this improves people’s experience with prosthetic limbs.
CINDY CHESTEK: Yeah. So as Paul mentioned, adding that piece of muscle around the end of the nerve just makes it so much easier on us as the engineering team to listen to that signal. It makes it 10 or 100 times bigger. And we’ve known how to listen to muscles for as long as we’ve had cardiac pacemaker leads. So that’s a very solved problem.
So what that means is that there’s always muscles that are missing in an amputation. And now, from the nerves, we can start to get those signals. So a really important signal, for example, that you’re missing is– if you look at your hands and you start moving your thumb, a lot of the muscles that move your thumb are actually in your hand. So if you lose your hand, it’s very hard to control the prosthetic thumb.
If we listen to some of these muscle grafts that now have these nice, big signals, we’ve been able to restore thumb movements. And both of our participants, Joe and Karen, were able to, for example, orient their thumb around an object. And it’s really hard to pick something up if you can’t move that thumb.
IRA FLATOW: Wow, so it actually was working in your test cases?
CINDY CHESTEK: Yep, absolutely. Our participants come into the lab. We’re so grateful for them to participate. And they have a small connector on their arm that we record those signals, we apply machine learning algorithms in real time, and then drive that into a prosthetic hand.
IRA FLATOW: I remember seeing a video of Dr. Hugh Herr at MIT showing a patient that was fidgeting with his foot. He had a prosthetic foot that was fitted. And I understand your study participants showed the same kind of natural adaptation or adoption of the prosthetics. They don’t even know that they’re fidgeting, do they?
CINDY CHESTEK: Yeah, no. I think that, on a normal day, we asked are participants to do a lot of fairly boring tasks. They’re touching targets with their fingers. And some of the coolest things we’ve seen is between experiments where we’re leaving the camera running. So yeah, our participant, Karen, she does tend to talk with their hands a lot. And so if we leave the hand running, she will gesture with it.
Our other participant, Joe, Phil, my student, left the camera running. And he was playing with the thumb and touching it at different spots on the index finger and was like, this is nuts. I can put it wherever I want it to be. And we asked him if it felt natural. And he said it did.
IRA FLATOW: OK. Let me give you the $64 question, which is, how soon can everybody who needs one?
CINDY CHESTEK: Yeah. So right now, for safety reasons, we’re only doing this in the lab. And all of our people are within six feet of a cart at all times. And we’re using medical grade amplifiers. But honestly, nothing that we’re doing here couldn’t be done on an implantable device.
So we do want to get that connector off of their arm, and make this something that they can take home. But we do need to have an implantable device that can record these signals, and then use that to drive the prosthetic hand. But there’s nothing we’re doing on this cart that wouldn’t fit on an implantable device.
IRA FLATOW: But first, wouldn’t you need to teach surgeons how to do that muscle transplant, too, when they’re working on a patient?
CINDY CHESTEK: Yeah. And I should say that Paul is an excellent teacher. He’s been evangelizing this technique far and wide. And it’s also really helpful to people who are having phantom limb pain, for example. So there’s actually– he’s done this on over 200 people. And I know he’s been teaching it all over the place.
IRA FLATOW: Do we need a new prosthetic that would go along with this? Or do the ones that we have, or are in the works good enough?
CINDY CHESTEK: So I think we do. I think we just passed what we can do with existing prosthetic hands. So one of our participants was able to use the Deka hand, which is a marvel of engineering. It moves nice and fast. But unfortunately, it’s pretty heavy. And so it’s really hard to use outside the lab.
Our other participant, she’s able to use the Ossur i-Limb. And she can control all of the five fingers. But interestingly, the software modes on the hand aren’t even really set up to do that right now. So we’re hoping we can work with them and unlock those capabilities.
But we need more from the hands. And we can make the hand do a lot more animation than the prosthetic hand can actually do at this point. We can have people spread their fingers now, since that’s also something you can get from the nerves.
IRA FLATOW: So if you’re doing that in animation, how do you translate that into the patient?
CINDY CHESTEK: Well, so that’s actually how we started doing these experiments. At first, we have them come in, and they’re controlling an animated hand. And the animated hand does exactly what we tell it to. There’s no delays, or anything. So at first, we have them learn to move the fingers around. I should say our algorithms are doing the learning. We just check that it works.
And so they move the fingers around in animation. And then we were able to have them fit with sockets. Our prosthetist does a great job, Alicia. And then they were able to also use those algorithms to walk around and control the hand.
IRA FLATOW: So you need to– so a couple of challenges to you then to make this really practical device is one yet to shrink it, the electronics, down in size, make it portable, wireless, and improve the hand itself, the artificial hand itself.
CINDY CHESTEK: Absolutely, yeah. So I think that we have more signals available, if I can challenge the engineers out there to give us a lighter, faster hand with more degrees of freedom.
IRA FLATOW: And are they meeting that challenge?
CINDY CHESTEK: Well, I mean, I think the signals haven’t been available until recently. I mean, I think this is these highest amplitude signals previously recorded from a nerve in a human being to my knowledge. So I mean, I’d like to think that we just edged past what’s available right now.
IRA FLATOW: And your patients must be ecstatic about something like this.
CINDY CHESTEK: Yeah. I mean, I think we ask them all the time like, how does it feel? How does the control feel? And I mean, I think they like it a lot better than what they can walk out of the clinic with today. And I think both of them would be really happy to be able to use this at home.
IRA FLATOW: All right. I’m going to give you this Science Friday blank check question. If you could have a blank check– I have it right here in my back pocket, not signed yet– for any amount of money, what would you do with it? What do you need to make?
CINDY CHESTEK: So I need a high channel count wireless implantable device. So that’s a– yeah.
IRA FLATOW: More bandwidth is what you’re saying.
CINDY CHESTEK: Yeah, I need more signal. So right now, I think everything you saw in the videos could be done with 16 channels of I/O. But machine learning is good. But nothing helps you more than having more signals in the brain. We also do this research. And we have 100 signals, give or take. And so yeah, we need more signal. And with neural interfaces broadly, if we– as you see the channel count of these systems expand, what we’re going to be able to do with them will expand greatly as well.
IRA FLATOW: Well, we hope we can give you more signal, Dr Chestek.
CINDY CHESTEK: Great.
IRA FLATOW: If it were up to us, we would help you out. Thank you very much for taking the time to be with us. Dr. Cindy Chestek, associate professor of Biomedical and Electrical Engineering at the University of Michigan in Ann Arbor.