Abstract:
The elderly population continues to grow everywhere and it finds difficulties to access websites
due to various reasons including functional impairments like lack in vision, hearing, mobility and
movement. Therefore, websites are usually made separately for elders to improve their user
experience. However, first it’s important to recognise whether a user is an elder or not, and for that
usually user profile information such as date of birth or age are used. Users may reluctant to feed
information or may even feed a wrong one. This research proposes a method using which elders
can automatically be recognised using behavioural biometrics of them. Based on the initial
observational study on elders it was noted that elders shake the mouse to identify the mouse pointer
location, do scrolling fast without much control, and the elders take a lot of time to click on a link
or button after moving over it. These three observation were considered as behavioural biometrics
to recognise elders. A data set was compiled in a control environment from 24 people of different
ages including 18 elders who are more than age of 65. All the people were asked to follow a same
set of tasks in two websites. Thereafter, the collected data were cleaned and a decision tree was
built to recognise elders using j48 algorithm and Weka tool. The results showed that elders move
the mouse faster than 5.7928 pixel/millisecond, scroll faster than 3.455561/millisecond, and take
more than 1, 158.6875 milliseconds to respond over a link or button. Thereafter more behavioural
biometrics were collected from random users in open environment in which users were asked to
fill a questionnaire with the intention of collecting their age. The collected data then were used to
validate the decision tree. It was found that speed of mouse movement recognises the elders with
84.51% accuracy, scrolling speed recognises with 96.08% accuracy, and response time recognises
elders with accuracy of 97.68%. The results show that instead of rely on user profiles, elders can
be recognised using user behavioural biometrics with significant accuracy. Though the response
time shows a high recognition rate, it is planned to explore the combination of different behaviour
biometrics together to see whether recognition rate can be improved.
