To make useful wearable robotic devices that can help stroke patients or people with amputated limbs, the computer brains driving the systems must be trained. That takes time and money - lots of time and money. And researchers need specially equipped labs to collect mountains of human data for training.
Even when engineers have a working device and brain, called a controller, changes and improvements to the exoskeleton system typically mean data collection and training start all over again. The process is expensive and makes bringing fully functional exoskeletons or robotic limbs into the real world largely impractical.
Not anymore, thanks to Georgia Tech engineers and computer scientists.
They've created an artificial intelligence tool that can turn huge amounts of existing data on how people move into functional exoskeleton controllers. No data collection, retraining, and hours upon hours of additional lab time required for each specific device.
Their approach has produced an exoskeleton brain capable of offering meaningful assistance across a huge range of hip and knee movements that works as well as the best controllers currently available. Their worked was published Nov. 19 in Science Robotics.
