Protective computer passwords have some competition. Researchers at the Georgia Institute of Technology have developed a novel intelligent computer keyboard that not only cleans itself - but can identify users by the pattern and style of their fingertips and keystrokes. The “human-machine interfacing” device reported in the American Chemical Society’s academic journal “Nano,” could provide a foolproof way to prevent unauthorized users from gaining direct access to computers.
Enabled by a system of “contact electrification,” the keyboard senses typing patterns, the level of pressure applied to keys and speed - and it is accurate enough to distinguish one individual user from another. An additional feature will also please the eco-minded: the keyboard harnesses energy generated from all that typing to either power itself or another small device.
“Conventional security measures such as personal identification numbers, tokens, or passwords can provide only limited protection, since they themselves are subject to illegitimate activities,” the research team wrote.
“Based on contact electrification, which is ubiquitous but underexplored, between human fingers and keys, the intelligent keyboard (IKB) converts typing motions on the keyboard into locally electric signals that can be harnessed for either touch-sensing or energy-harvesting purposes. Most significantly, the IKB allows a direct identification of personality in data input using the dynamic electronic signals generated when striking keys,” the article stated.
The scientists anticipate their device can be potentially applied “not only to self-powered electronics but also to artificial intelligence, cyber security, and computer or network access control.”
The research team included scientists from the School of Materials Science and Engineering at the Georgia Institute, the Beijing Institute of Nanoenergy and Nanosystems at the Chinese Academy of Sciences, the Dept. of Optoelectronic Engineering at Chongqing University and the Department of Electrical Engineering at the University of California at Riverside. The project was funded by the U.S. Dept. of Energy.