Professors Martha Crosby and Curtis Ikehara talk about their biometric mouse, and how it can be used for authentication, cognitive load, lie detection, and more.

Hi Martha and Curtis. What is a “biometric mouse”, and what can you do with it?

biometricmouseOur biometric mouse (Patent #7,245,218) can detect the pattern of pressure applied to a computer mouse while clicking. The pressure pattern allows for the detection of the user’s identity after three clicks. Also, distortions in the pressure pattern can also be used to determine the relative difficulty of a task (i.e., cognitive load).

How did you come up with this idea, and what is the status of the project?

curtisikeharabiolabThe Adaptive Multimodal Interaction Lab has been using a variety of high precision physiological sensors such as eye-tracking to detect task difficulty. Eye tracking is relatively expensive so we wanted to develop alternative lower cost physiological sensors that could provide complementary data indicating task difficulty. Our biometric mouse, now in mass production, has the potential to meet both cost and capability requirements. The ability to detect identity was an outcome of identifying pressure patterns unique to individuals. The biometric mouse complements initial access by other means (such as passwords or fingerprints) by providing continuous authentication of the user with every mouse click.

If students want to get involved with the project, what opportunities are available, and how can they learn more about it?

Students can contact Dr. Martha Crosby ( to learn more about possible ICS499 or ICS699 capstone projects.

What are some possible future research directions for this project?

More research needs to be done to validate results and explore other possible applications such as with multi-touch devices. Recently, researchers at the University of Utah have been exploring using the biometric mouse for lie detection.

Anything else you’d like to add?

In addition to distinguishing between different users, the biometric mouse can distinguish levels of task difficulty. This could be a great tool for use in applications such as distance education.