Articles
User-Age Classification Using Touch Gestures on Smartphones
Authors:
Suleyman AL-Showarah,
University of Buckingham, GB
About Suleyman
Applied Computing Department
Naseer AL-Jawad,
University of Buckingham, GB
About Naseer
Applied Computing Department
Harin Sellahewa
University of Buckingham, GB
About Harin
Applied Computing Department
Abstract
In this paper we investigated the possibility of classifying users’ age-group using gesture-based features on smartphones. The features used were gesture accuracy, speed, movement time, and finger/force pressure. Nearest Neighbour classification was used to classify a given user’s age-group. The 50 participants involved in this research included 25 elderly and 25 younger users. User-dependent and user-independent age-group classification scenarios were considered. On each scenario, two types of analysis were considered; using a single-feature and combined-features to represent a user-age group. The results revealed that classification accuracy was relatively higher for the younger age group than the elderly age group. Also, a higher classification accuracy was achieved on the small smartphone than on mini-tablets. The results also showed that the classification accuracy increases when combining the gesture features in to a single representation as opposed to using a single gesture feature.
How to Cite:
AL-Showarah, S., AL-Jawad, N. and Sellahewa, H., 2015. User-Age Classification Using Touch Gestures on Smartphones. International Journal of Multidisciplinary Studies, 2(1), pp.1–11. DOI: http://doi.org/10.4038/ijms.v2i1.57
Published on
30 Jun 2015.
Peer Reviewed
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