Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.651301
Title: Effects of age on smartphone and tablet usability, based on eye-movement tracking and touch-gesture interactions
Author: Al-Showarah, Suleyman
ISNI:       0000 0004 5357 9335
Awarding Body: University of Buckingham
Current Institution: University of Buckingham
Date of Award: 2015
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Abstract:
The aim of this thesis is to provide an insight into the effects of user age on interactions with smartphones and tablets applications. The study considered two interaction methods to investigate the effects of user age on the usability of smartphones and tablets of different sizes: 1) eye-movements/browsing and 2) touch-gesture interactions. In eye movement studies, an eye tracker was used to trace and record users’ eye movements which were later analysed to understand the effects of age and screen-size on browsing effectiveness. Whilst in gesture interactions, an application developed for smartphones traced and recorded users’ touch-gestures data, which were later analysed to investigate the effects of age and screensize on touch-gesture performance. The motivation to conduct our studies is summarised as follows: 1) increasing number of elderly people in our society, 2) widespread use of smartphone technology across the world, 3) understanding difficulties for elderly when interacting smartphones technology, and 4) provide the existing body of literature with new understanding on the effects of ageing on smartphone usability. The work of this thesis includes five research projects conducted in two stages. Stage One included two researches used eye movement analysis to investigate the effects of user age and the influence of screen size on browsing smartphone interfaces. The first research examined the scan-paths dissimilarity of browsing smartphones applications or elderly users (60+) and younger users (20-39). The results revealed that the scan-paths dissimilarity in browsing smartphone applications was higher for elderly users (i.e., age-driven) than the younger users. The results also revealed that browsing smartphone applications were stimulus-driven rather than screen size-driven. The second study was conducted to understand the difficulties of information processing when browsing smartphone applications for elderly (60+), middle-age (40-59) and younger (20-39) users. The evaluation was performed using three different screen sizes of smartphone and tablet devices. The results revealed that processing of both local and global information on a smartphone/tablet interfaces was more difficult for elderly users than it was for the other age groups. Across all age groups, browsing on the smaller smartphone size proved to be more difficult compared to the larger screen sizes. Stage Two included three researches to investigate: the difficulties in interacting with gesture-based applications for elderly compared to younger users; and to evaluate the possibility of classifying user’s age-group based on on-screen gestures. The first research investigated the effects of user age and screen size on performing gesture swiping intuitively for four swiping directions: down, left, right, and up. The results revealed that the performance of gesture swiping was influenced by user age, screen size, as well as by the swiping orientation. The purpose of the second research was to investigate the effects of user age, screen sizes, and gesture complexity in performing accurate gestures on smartphones and tablets using gesture-based features. The results revealed that the elderly were less accurate, less efficient, slower, and exerted more pressure on the touch-screen when performing gestures than the younger users. On a small smartphone, all users were less accurate in gesture performance – more so for elderly – compared to mini-sized tablets. Also, the users, especially the elderly, were less efficient and less accurate when performing complex gestures on the small smartphone compared to the mini-tablet. The third research investigated the possibility of classifying a user’s age-group using touch gesture-based features (i.e., gesture speed, gesture accuracy, movement time, and finger pressure) on smartphones. In the third research, we provide evidence for the possibility of classifying a user’s age-group using gesture-based applications on smartphones for user-dependent and user-independent scenarios. The accuracy of age-group classification on smaller screens was higher than that on devices with larger screens due to larger screens being much easier to use for all users across both age groups. In addition, it was found that the age-group classification accuracy was higher for younger users than elderly users. This was due to the fact that some elderly users performed the gestures in the same way as the younger users do, which could be due to their longer experience in using smartphones than the typical elderly user. Overall, our results provided evidence that elderly users encounter difficulties when interacting with smartphones and tablet devices compared to younger users. Also, it was possible to classify user’s age-group based on users’ ability to perform touch-gestures on smartphones and tablets. The designers of smartphone interfaces should remove barriers that make browsing and processing local and global information on smartphones’ applications difficult. Furthermore, larger screen sizes should be considered for elderly users. Also, smartphones could include automatically customisable user interfaces to suite elderly users' abilities to accommodate their needs so that they can be equally efficient as younger users. The outcomes of this research could enhance the design of smartphones and tablets as well the applications that run on such devices, especially those that are aimed at elderly users. Such devices and applications could play an effective role in enhancing elderly peoples’ activities of daily lives.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.651301  DOI: Not available
Keywords: QA75 Electronic computers. Computer science
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