The Institute for Robotics and Intelligent Machines presents Merging Humans and Machines to Assist Legged Locomotio” by Daniel Ferris of the University of Florida. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.
Robotic technologies have greatly advanced in recent years, enabling the creation of new wearable sensors and motorized devices. Robotic exoskeletons for human performance augmentation or neurological rehabilitation are in development and testing at many locations around the globe. Bionic lower limb prostheses are becoming practical solutions for amputees. However, one of the fundamental roadblocks for both robotic exoskeletons and bionic prostheses is the control. Better control approaches are needed to make the devices move in smooth coordination with the human users. One possibility to get better control of wearable robotic devices is to obtain feedforward neural commands from the user. Ferris will present on research aimed at merging humans and machines, outlining the major obstacles remaining to produce truly cooperative human-machine systems.
Dan Ferris’ research focuses on the biomechanics and neural control of human locomotion. Most of his research involves human-machine interactions, mechanically and electrically. Specifically he studies human walking and running using mobile brain imaging, robotic lower limb exoskeletons, and bionic lower limb prostheses. The goal is to identify principles of how humans control their movements, how humans and machines interact, and develop better devices and controllers for machines assisting human locomotion.
His laboratory has developed a number of robotic lower limb exoskeletons to determine how assistance at the ankle, knee, and hip can reduce the energetic cost of locomotion, and might be used to assist gait rehabilitation after incomplete spinal cord injury. They have used the principles they have learned from robotic lower limb exoskeletons to develop a bionic lower limb prosthesis under proportional myoelectric control. More recently, Ferris and his group are pioneering the use of high-density electroencephalography (EEG) to perform mobile brain imaging with high temporal resolution. This last effort includes both new hardware and software innovations to facilitate removal of motion and muscle artifacts from EEG during walking and running.