the concept of using myoelectric signals to interface is simple enough the armband reads the electrical signals the brain sends to the muscles to figure out what it is that the muscles are trying to do however developing a system capable of doing that is an extremely complicated process the electromyographic signal is if you look at it just a huge mess said langari it just looks like a bunch of noise he compared it to standing in the middle of a loud factory with a supervisor giving you instructions nearby but in a language you dont understand its taken sungtae almost two years to get to the point where he can command the robot relatively smoothly langari said thats a fairly significant achievement nobody else has been able to do that at this point you have to really use some sophisticated noise-canceling algorithms to eliminate all the unwanted information how you decipher what they say even after you have eliminated all the background noise is not trivial how do you make sure that what youre eliminating is noise and not important information shin did his undergraduate work at sejong university in seoul in his home country of south korea it was there that he first learned about texas a&m from some of the faculty who are former students he also spent some time at the korean institute of science and technology and a year at texas a&m university at qatar before coming to college station now two years into the project shin believes the potential for the technology extends far beyond the original idea he sees the armband-like accessory as a potential input device for a number of systems and says that while there has been significant research into virtual and augmented reality systems not as much work has been done on input devices for those systems i think in five or 10 years this type of technology will be really big shin said i think virtual reality and augmented reality needs an interface device like dynamic motion this system could be used instead of a computer mouse or a controller a system that tracks dynamic motion has several advantages over many input systems most commonly used now they are smoother and more precise than commonly used controllers and have wider capabilities than typed commands some dynamic motion technologies are currently on the market but they rely on visual tracking using cameras like an xbox kinect a myoelectric system would eliminate some of the challenges that those systems have a myoelectric interface comes from the gesture itself shin said all gestures involve muscle activity so if we can catch this signal and recognize this pattern its more valuable than using a vision system vision systems have obstacles you have to use a terminal the lighting can make a difference the size of the hands matters the point of view of the camera matters…but with myoelectric signal you dont have those limits gesturing is universal in addition to being used for input devices and human assistance langari also sees potential in dynamic motion being used to teach new tasks to robots if you teach a child to do something you dont just tell them how to do it you also actually show them how to do it he said we want to have the capability for a robot to do that in factories you might want to show a robot how to do new tasks that have been done by humans to translate what the human does you first have to understand what the human is doing with their arms and hands and then program the robot to do it this way were going directly from human physical motion to robotic physical motion note: as shin is developing the control side other members of the lcras exoskeleton project are working on the exoskeleton itself click here to read a story about their work which was featured recently in the conversation