Neuromag July 2018 - Page 13

Robotic arms: reality or fantasy? Written by Vinay Jayaram Any discussion on prosthetic arms must inevitably start (or end) with Luke’s hand from The Empire Strikes Back. It was, for any prosthetics researcher or aficionado, the holy grail of the field: a hand that looks like a human hand, moves like a human hand, and responds to stimuli like a human hand. Indeed, in the next movie, it’s almost as if Luke has a human hand after all, no post-processing required. Unfor- tunately, much like faster-than-light travel and light sabers, such a prosthetic exists today firmly in the realm of fantasy. Prosthetics is a fascinating field be- cause it is the intersection of so many different disciplines. Ask a dozen re- searchers why a perfect prosthetic hand doesn’t exist today and you’ll get just as many answers. The mechanical engineer would say: Have you seen how many different mo- tions the human hand can do? It’d be a miracle to make one that can move like that which doesn’t fall to pieces if you look at it wrong. The orthopedist would say: Comfort- ably carrying a prosthetic manipulator 24 hours a day isn’t easy, and in-bone implantation of prosthetics is not yet a solved problem. The neuroscientist would say: Repro- ducing spinal reflexes from a prosthetic hand? Ignoring the embodiment issues involved, that requires an interface to the peripheral sensory nerves that perfectly replicates what would have existed in the natural limb. Then all the scientists would propose totally conflicting ideas for the best way forwards. These days, however, people are look- ing more and more towards one par- ticular person to solve the problems that keep prostheses frustrating and often unusable: the machine learning expert. If you just record enough data, the thinking goes, and train the right predictive model, you can learn how to steer the prosthetic limb using the brain. With perfect control, even im- perfect sensory input or mechanical ingenuity may be surmountable. And so, in the past decade there has been an explosion in the number of people trying more and more complicated ap- proaches to control a prosthesis with the mind. While the performance met- rics in some of these papers keep get- ting higher and higher, however, even state-of-the-art commercial prosthe- ses use methods that the machine learning community stopped looking at decades ago. To get into this mystery a bit more, let’s back up and talk about how a prosthesis works in more detail. Let’s imagine you’ve gotten your arm (half- way between elbow and wrist) cut off in a freak light saber incident. Once the stump heals, you go to your lo- cal prosthetics company and ask for a new lower arm. At this point you have a few options: a passive arm, a body- powered arm, and a myoelectrically controlled arm. The passive arm is just that: a realistic replica that hangs on your forearm as a dead weight (or, for something both more sinister and more useful, you could choose a hook). Body-powered means something like a claw that you can open and shut by shrugging your shoulders. Myoelectric is what the machine learning commu- nity cares about. It uses the signals from the residual muscles to control the prosthesis. Even though your hand is gone, the muscles that used to con- trol it are all packed into your forearm, and are mostly still around – which means that when you imagine moving July 2018| NEUROMAG | 13