A new algorithm that analyses a person’s typing patterns on a keyboard may help identify early onset of Parkinson’s disease, a new MIT study has found.
The researchers from Massachusetts Institute of Technology found that their algorithm for analysing 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, from the Madrid-MIT M+Vision Consortium, is based on the premise that “there might be hidden information in the way that we type,” said Ian Butterworth, one of the study authors and an M+Vision fellow.
While the 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 said.
Preliminary results from a study of about two dozen Parkinson’s patients suggest that the algorithm for analysing keystrokes can also distinguish people who have the disease from those who don’t.
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 study published in the journal Scientific Reports, 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.
The primary feature that the researchers analysed 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.
The researchers found that after the late-night awakening, study participants’ keystrokes showed much more variation in timing, as opposed to the typing they did when alert, which was very consistent in its timing.
This approach could also be applicable to Parkinson’s disease 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.