By
revealing loss of motor skills, typing patterns
may help
to identify early onset of Parkinson’s. By Anne Trafton | MIT News Office, April 1, 2015
Analyzing people’s keystrokes as
they type on a computer keyboard can reveal a great deal of information about
the state of their motor function, according to a new study from MIT.
In a paper appearing in
Scientific Reports, the researchers found that their algorithm for
analyzing keystrokes could distinguish between typing done in the middle of the
night, when sleep deprivation impairs motor skills, and typing performed when
fully rested.
The study, which grew out of the
Madrid-MIT M+Vision Consortium, is based on the premise that “there might be
hidden information in the way that we type,” says Ian Butterworth, one of the
authors and an M+Vision fellow. “At the moment, pretty much all of the other
information in typing is thrown out. We just pay attention to what keys are
being pressed, not when or for how long.”
While this study focused on the
effects of fatigue, it also represents a first step toward using keystroke
patterns to diagnose conditions that impair motor function, such as Parkinson’s
disease, much earlier than is now possible, the researchers say.
Preliminary results from a study of
about two dozen Parkinson’s patients suggest that the researchers’ algorithm
for analyzing keystrokes can also distinguish people who have the disease from
those who don’t. The team is now planning a larger study of Parkinson’s
patients.
M+Vision fellows Luca Giancardo,
Alvaro Sanchez-Ferro, and Carlos Sanchez Mendoza are also authors of the paper,
along with Jacob Hooker, an associate professor of radiology at Harvard Medical School
“Window into the brain”
To initiate movement, the brain’s
primary motor cortex sends signals through several other brain regions,
including the supplementary motor area, cerebellum, and basal ganglia. These
brain areas activate spinal neurons that stimulate muscles to execute the
movement.
Many factors can interfere with
motor ability, including sleep deprivation, which reduces dexterity. For the Scientific
Reports study, the MIT team designed a computer algorithm that can capture
timing information from computer keystrokes, allowing the researchers to detect
patterns that distinguish typing that occurs when motor skills are impaired.
Keystroke patterns have been used
previously as a biometric signature to identify individuals for security
purposes, but this is the first time that scientists have tried to extract
diagnostic information from typing. The primary feature that the researchers
analyzed is known as “key hold time” — a measure of how long a key is pressed
before being released.
To gather the data, the researchers
created a plug-in software component that could be incorporated into a web
browser to capture keystrokes; it does not capture the content of what is being
typed. The team is also working on smartphone apps that could be used to gather
the same kind of data from mobile devices.
“We thought this was a unique
opportunity to have a window into the brain using your normal interactions with
an electronic device,” Sanchez-Ferro says.
Sanchez Mendoza adds, “You already
have the hardware. You just have to let someone evaluate the information you
collect every day when you use the device, and try to pull it out for
health-related purposes.”
For the sleep deprivation study,
the researchers recruited 14 healthy volunteers from around MIT and had them
type a randomly chosen Wikipedia article. The participants were then told that
they would be awakened during the night to type another article. Participants
were awakened 70 to 80 minutes after going to sleep, when they should be in the
deepest part of their sleep cycle.
The researchers found that after
the late-night awakening, students’ keystrokes showed much more variation in
timing, as opposed to the typing they did when alert, which was very consistent
in its timing.
Potential for Parkinson’s
diagnosis
This approach could also be
applicable to Parkinson’s disease, a degenerative neurological disorder that
kills dopamine-producing cells in the brain’s substantia nigra, leading to
tremors, slowness of movement, and difficulty walking.
Preliminary results from a study of
21 Parkinson’s patients and 15 healthy subjects suggest that there is greater
variation in the keystrokes of Parkinson’s patients than in control subjects,
Sanchez-Ferro says. If the findings are validated in larger studies, the
researchers believe this approach could lead to earlier diagnosis for Parkinson’s
and aid in the development of better treatments. Currently, there is no way to
diagnose Parkinson’s in early stages of the disease.
“People are usually diagnosed five
to 10 years after the beginning of the disease, and lot of the damage has
already been done,” says Giancardo, the paper’s first author, who is also
supported by the MIT Translational Fellows Program.
John Growdon, director of the
Memory and Movement Disorders Unit at Massachusetts General Hospital
“Overall, it strikes me that this
has great potential to detect subtle motor impairments even in advance of a
clinician’s ability to find them,” adds Growdon, who was not involved in the
study.
The researchers believe this
strategy could also be used to evaluate patients with other diseases that
affect motor skills, such as rheumatoid arthritis. “This might have
applications in any disease producing a motor impairment, whether it’s in your
hands and muscles or the brain,” Sanchez-Ferro says.
The research was funded by the
Community of Madrid.
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