One robotic arm is holding a fork and knife. An expertly controlled computerized voice announces each action, including the phrase “moving fork to food” and the words “retracting knife.” The expertly controlled robot arm holds portions of food but only allows you to eat when you say certain commands, like the phrase “select cut location,” so that it can slice off bite-sized pieces to serve up your meal.
This person, with limited upper body mobility who hasn’t been able to use their fingers in about 30 years, just had a meal using thought to finish and some smart robotic hands.
A team led by researchers at the Johns Hopkins Applied Physics Laboratory in Laurel, Maryland, and the Department of Physical Medicine and Rehabilitation (PMR) in the Johns Hopkins School of Medicine published a paper in the journal Frontiers in Neurorobotics describing this latest feat using a brain-machine interface (BMI) and two modular prosthetic limbs.
BMI systems provide a direct communication link between the brain and a computer, which translates neural signals to perform external functions. In this particular experiment, muscle movement signals from the brain helped control the robotic prosthetics.
Researchers at the Applied Physics Laboratory (APL) and medical school are making innovative advancements in prosthetic technology with reductions in mental effort. Research led by APL, on cooperation between humans and robots, was initially sponsored by DARPA. With the new paper, the results of the collaborative study outline an innovative model for shared control which enables a human to manoeuvre a pair of robotic prostheses with minimal mental input.
Dr Francesco Tenore, the senior researcher in APL’s Research and Exploratory Development Department, said “this shared control approach is intended to leverage the intrinsic capabilities of the brain machine interface and the robotic system, creating a ‘best of both worlds’ environment where the user can personalize the behaviour of a smart prosthesis.” The paper’s senior author focuses on neural interface research and applied neuroscience.
Despite their preliminary findings, the study’s lead researcher is excited about giving users who struggle with complex tasks an idea of a true sense of control over increasingly intelligent assistive machines.