“Our results are promising. We know we can help people who have suffered a stroke. In Washington, D.C., we want to demonstrate our success so we can encourage Congress to maintain this critical funding mechanism for robotics in the US.”
On June 9, Marcia O’Malley, professor of mechanical engineering (MECH) and director of the Mechatronics and Haptic Interfaces (MAHI) Lab at Rice University will take her brain-machine interface (BMI) to Capitol Hill. The occasion is the fifth anniversary of the National Robotics Initiative (NRI) launched by President Obama in 2011.
With collaborators at the University of Houston (UH) and The Institute for Rehabilitation and Research Memorial Hermann (TIRR), O’Malley received a $1.17 million grant from the National Institutes of Health (NIH) and NRI. Their research aims at helping stroke survivors to “think” their disabled limbs into motion.
"In most cases, if you want to engage the patient, the robot has to know what the patient is doing. If the patient tries to move, the robot has to anticipate that and help. But without sophisticated sensing, the patient has to initiate movement,” said O’Malley, the project’s principal investigator.
Accompanying O’Malley to the Washington event will be Jennifer Sullivan, who graduated from Rice in 2011 with a B.S. in MECH, earned an M.S. in the same major from the University of British Columbia in 2015 and now works as a MECH research assistant at Rice.
Along with roughly a dozen other NRI-funded research groups from around the country, O’Malley will attend an expo, luncheon and panel discussion in the Rayburn House Office Building. Also in attendance will be representatives of the Congressional Robotics Caucus, congressional staffers and robotics researchers seeking to understand the impact of this federally-funded research program.
O’Malley and her collaborators have developed a robotic orthotic device that revolutionizes upper-limb rehabilitation. The neurotechnology interprets brain waves and makes it possible for stroke patients to operate an exoskeleton surrounding the arm from fingertips to elbow.
O’Malley’s lab is developing the exoskeleton and the UH team works on the electroencephalograph-based neural interface. The device has been tested by UTHealth physicians at TIRR Memorial Hermann on volunteer patients. Funded through the National Institute of Neurological Disorders and Stroke, it’s among the small number of projects selected by the NRI, a collaborative partnership that includes the NIH, National Science Foundation, NASA and the Department of Agriculture, to encourage development of robots that work closely with humans.
O’Malley explained that repetitive motion has proven effective at retraining motor nerve pathways damaged by a stroke, but patients must be motivated to do the work. The team led by José Luis Contreras-Vidal, director of UH’s Laboratory for Noninvasive Brain-Machine Interface Systems and a professor of electrical and computer engineering, was the first to successfully reconstruct 3-D hand and walking motions from brain signals recorded noninvasively with an EEG brain cap.
The technology permits users to control, using their thoughts, robotic legs, and below-elbow amputees to control neuroprosthetic limbs. The new project is one of the first to design a BMI system for stroke survivors.
EEG devices translate brain waves from healthy subjects into control outputs to operate the MAHI-EXO II robot, and then from stroke survivors who have some ability to initiate movements, to prompt the robot into action. This permits the team to refine the EEG-robot interface before moving to stroke patients without residual upper-limb function.
The intelligent exoskeleton uses thoughts to trigger repetitive motions and retrain the brain’s motor networks. An earlier version of the MAHI-EXO II developed by O’Malley, already in validation trials to rehabilitate spinal-cord-injury patients at the UTHealth Motor Recovery Lab, uses feedback that permits patients to work as hard as possible while assisting, and sometimes resisting, motions to build strength and accuracy.