A paralysed woman who controlled a robotic arm using
just her thoughts has taken another step towards restoring her natural
movements by controlling the arm with a range of complex hand movements.
Thanks to researchers at the University of
Pittsburgh, Jan Scheuermann, who has longstanding quadriplegia and has been
taking part in the study for over two years, has gone from giving "high
fives" to the "thumbs-up" after increasing the manoeuvrability
of the robotic arm from seven dimensions (7D) to 10 dimensions (10D).
The extra dimensions come from four hand
movements--finger abduction, a scoop, thumb extension and a pinch--and have
enabled Jan to pick up, grasp and move a range of objects much more precisely
than with the previous 7D control.
It is hoped that these latest results, which have
been published today, 17 December, in IOP Publishing's Journal of Neural
Engineering, can build on previous demonstrations and eventually allow robotic
arms to restore natural arm and hand movements in people with upper limb
paralysis.
Jan Scheuermann, 55, from Pittsburgh, PA had been
paralysed from the neck down since 2003 due to a neurodegenerative condition.
After her eligibility for a research study was confirmed in 2012, Jan underwent
surgery to be fitted with two quarter-inch electrode grids, each fitted with 96
tiny contact points, in the regions of Jan's brain that were responsible for
right arm and hand movements.
After the electrode grids in Jan's brain were
connected to a computer, creating a brain-machine interface (BMI), the 96
individual contact points picked up pulses of electricity that were fired
between the neurons in Jan's brain.
Computer algorithms were used to decode these firing
signals and identify the patterns associated with a particular arm movement,
such as raising the arm or turning the wrist.
By simply thinking of controlling her arm movements,
Jan was then able to make the robotic arm reach out to objects, as well as move
it in a number of directions and flex and rotate the wrist. It also enabled Jan
to "high five" the researchers and feed herself dark chocolate.
Two years on from the initial results, the
researchers at the University of Pittsburgh have now shown that Jan can
successfully manoeuvre the robotic arm in a further four dimensions through a
number of hand movements, allowing for more detailed interaction with objects.
The researchers used a virtual reality computer
program to calibrate Jan's control over the robotic arm, and discovered that it
is crucial to include virtual objects in this training period in order to allow
reliable, real-time interaction with objects.
Co-author of the study Dr Jennifer Collinger said:
"10D control allowed Jan to interact with objects in different ways, just
as people use their hands to pick up objects depending on their shapes and what
they intend to do with them. We hope to repeat this level of control with
additional participants and to make the system more robust, so that people who
might benefit from it will one day be able to use brain-machine interfaces in
daily life.
"We also plan to study whether the
incorporation of sensory feedback, such as the touch and feel of an object, can
improve neuroprosthetic control."
Commenting on the latest results, Jan Scheuermann
said: ""This has been a fantastic, thrilling, wild ride, and I am so
glad I've done this."
"This study has enriched my life, given me new
friends and co-workers, helped me contribute to research and taken my breath
away. For the rest of my life, I will thank God every day for getting to be
part of this team."
A video
of Jan controlling the robotic arm.
Story Source:
The above story is based on materials provided
by Institute of Physics. Note:
Materials may be edited for content and length.
Journal Reference:
B Wodlinger, J E Downey, E C Tyler-Kabara, A B
Schwartz, M L Boninger, J L Collinger. Ten-dimensional anthropomorphic arm
control in a human brain−machine interface: difficulties, solutions, and
limitations. Journal of Neural Engineering, 2015; 12 (1): 016011 DOI: 10.1088/1741-2560/12/1/016011
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