your hand( which is no longer there), the muscles of your forearm( which are) still twitch accordingly. Even better, these twitches are linked to the electrical potential recorded on the surface of the skin, so they are easy to read with an electromyogram( EMG).
To a machine learning expert the problem then becomes simple: Muscles twitch in accordance with thoughts. Therefore if a machine can learn a mapping from muscle twitches to intended movements, the problem is solved! The question then is how – if there is no remaining hand – how do I know the subject’ s exact intended hand position( called‘ pose’) in order to feed that to my algorithm? At first, people tried something very simple: A single muscle, if it is activated, can twitch somewhere between 0 % and 100 % of its maximum capacity. Therefore, if one records the EMG over that exact muscle, the rest of the hand’ s pose is no longer necessary. This system of proportional control( the activation of the robot hand is proportional to the activation of discrete anatomical muscles, as measured by the EMG) was sufficiently reliable and effective to become the current standard for myoelectric prostheses. However, it comes with downsides: If the electrode shifts a little bit on the skin, then the signal can quickly disappear. Plus, this forces the control to be from the most accessible muscles, which are not necessarily the most intuitive or the most comfortable to activate. Lastly, the human hand is not controlled through isolated muscle contractions, making more complex
control( more than two degrees of freedom of the hand) impossible.
To solve these problems, the field looked towards pattern recognition. It’ s still too hard to give my algorithm the exact imaginary position of the hand in order to train it, the thinking goes, but if I ask the user to choose a small number of poses – say 8 – that they generate use reliably, I don’ t need to be as precise in what I tell the algorithm. All I need to trust is that the user is capable of doing the same imagined pose reliably. While not quite that sim- ple in practice, the idea proved to allow for more reliable control in prosthetics both within and outside the laboratory. While more and more complicated machine learning has been attempted in the research community, however – deep learning, non-negative matrix factorization and other new methods – industry sticks resolutely with the simplest classification strategies. It is here that the difference in metrics comes into play.
For a machine learner, task success is measured by decoding accuracy. If I can decode the correct pose from the EMG 95 % of the time, then my classifier is better than one that can only do it 90 % of the time. Intuitively, this is also quite reasonable – if a classifier is right more often, it is probably the one you want. However, this is not the metric that an amputee uses to rate the usability of his device, as no movement is ever done in isolation. A real arm is used to perform complex sequences of movements( opening doors, grasping and turning keys) in situations one could never exhaustively test in a lab( trying to open your front door when there’ s a grocery bag on your arm and your kid is hanging off your leg screaming for ice cream).
14 | NEUROMAG | July 2018