![]() ![]() ![]() The algorithms span a vast range of families: basic statistical features 7, 8, 9, Bayesian analysis 10, 11, autoregressive models 12, hidden Markov models 13, 14, artificial neural networks 15, 16 and other machine learning techniques 17, 18. Our hypothesis is that while physical or psychological variations may undermine accuracy for biometric applications, they may be leveraged to infer the psychomotor status of the subject typing.Ĭurrent algorithms are highly specialized for biometric identification and are not tuned to characterize health-related variations. The main challenge that these methods face is the need of taking into account the user variability due to physical or psychological variations, an aspect reported in various publications 5, 6. Two recent reviews 4, 5 compare various keystroke dynamics classification methods, some of them achieve an excellent accuracy, with an identification rate higher than 95%. 1 shows examples of time-based features commonly employed for user identification. In the last 30 years, many have proposed the development of pattern recognition algorithms to certify the identity of a person from typing-derived features 4, 5, now known as keystroke dynamics. Garcia filed the first patent describing a method able to identify a person via their style of typing on a computer keyboard 3. With the advent of the computer, the same identification concept was adapted to a computer keyboard. During World War II, American intelligence developed a methodology called “The Fist of Sender” to distinguish telegraph messages coming from allies or enemies 2. Experienced telegraph operators were able to identify their colleagues by listening to patterns in the rhythm of Morse code being communicated 1. It was soon discovered that operators using this machine, the telegraph, were unwillingly disclosing more information than the message itself. In 1844 the first finger operated electrical device for long distance communication was invented. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed and perform consistently with different keyboards. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. ![]()
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