Accessibility for Older Users through Adaptive Interfaces: Opportunities, Challenges and Achievements

Edlin-White, Rob; Cobb, Sue; D’Cruz, Mirabelle; Floyde, Anne; Lewthwaite, Sarah; Riedel, Johann

doi:10.1007/978-3-642-21616-9_54

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…The system specially focuses in motor-impaired and blind user, failing to meet the requirements for moderate visual impaired people and people with cognitive decline which is most common among older adults [35]. The MyUI project proposes an AUI for interactive TV and mobile devices [11]. The system is capable of recognizing the most suitable interface components and elements according to the users needs and context.…”

Section: B Existing Solutions In Adaptive User Interface (Aui)mentioning

confidence: 99%

A Conceptual framework for Adaptive User Interfaces for older adults

Machado

Singh

Cruciani

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Nowadays, information and communication technologies (ICT) have become part of our everyday life, enhancing the quality of life and promoting new forms of social interaction. Despite the numerous benefits of ICT, older adults still present low rates of ICT adoption compared to other population segments. The lack of accessible User Interfaces has been identified as a major barrier. Traditional User Interfaces follow a design for all approach, typically ignoring the needs of older adults. Recent research in Human-Computer Interaction (HCI) proposes adaptive User Interfaces to suit the individual users abilities. Nevertheless, most of the existing approaches perform adaptation based on user profile groups and do not provide personalized adaptation in real-time. This paper introduces a conceptual framework for developing real-time adaptive User Interfaces. The system aims to target most common issues among older adults, i.e. cognitive decline and vision loss. The developed conceptual framework also presents novel strategic techniques to assess cognitive load and vision related issues in an unobtrusive manner for the user.

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Section: B Existing Solutions In Adaptive User Interface (Aui)mentioning

confidence: 99%

A Conceptual framework for Adaptive User Interfaces for older adults

Machado

Singh

Cruciani

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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“…While these guidelines are very useful, needs can differ among individuals [13]. To respond to the various needs of older adults, researchers have considered adaptive solutions based on user profiles such as behaviors [14], knowledge [15], and skills [16].…”

Section: Interface Design For Older Adultsmentioning

confidence: 99%

Typing Tutor: Individualized Tutoring in Text Entry for Older Adults Based on Statistical Input Stumble Detection

Hagiya

Horiuchi

Yazaki

et al. 2018

Journal of Information Processing

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Many older adults are interested in smartphones. However, most of them encounter difficulties in selfinstruction and need support. Text entry, which is essential for various applications, is one of the most difficult operations to master. In this paper, we propose Typing Tutor, an individualized tutoring system for text entry that detects input stumbles using a statistical approach and provides instructions. By conducting two user studies, we clarify the common difficulties that novice older adults experience and how skill level is related to input stumbles with a 12-key layout for Japanese. Based on the study, we develop Typing Tutor to support learning how to enter text on a smartphone. A two-week evaluation experiment with novice older adults (65+) showed that Typing Tutor was effective in improving their text entry proficiency, especially in the initial stage of use. In addition, we demonstrate the applicability of Typing Tutor to other keyboards and languages with the QWERTY layout for Japanese and English.

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“…While these guidelines are very useful, needs can differ among individuals [13]. To respond to older adults' various needs, researchers have considered adaptive solutions based on user profiles such as user behaviors [14], knowledge [15], and skills [16].…”

Section: Interface Design For Older Adultsmentioning

confidence: 99%

Assistive Typing Application for Older Adults Based on Input Stumble Detection

Hagiya

Horiuchi

Yazaki

et al. 2017

Journal of Information Processing

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Smartphones offer new opportunities to improve the lives of older adults. Although many older adults are interested in smartphones, most of them face difficulties in self-instruction and need support. Text entry, which is essential for various applications, is one of the most difficult operations to master. Therefore, we propose an assistive typing application that detects input stumbles and provides instructions for typing presented sentences, instead of having human tutors help older adults resolve the input stumbles by themselves. First, we investigated the ways that novice older adults have problems with text entry on smartphones. Next, we confirmed the acceptability of being provided with instructions for text entry by Wizard-of-Oz (WoZ). Then, we constructed an assistive typing application based on the collected data from two user studies. An evaluation with novice older adults (60+) showed that the assistive typing application increased typing speed by 17.2% and reduced input stumble incidence by 59.1% compared with the users' initial performance. Improvement rates were almost the same as those achieved with human tutors.

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Accessibility for Older Users through Adaptive Interfaces: Opportunities, Challenges and Achievements

Cited by 7 publications

References 11 publications

A Conceptual framework for Adaptive User Interfaces for older adults

A Conceptual framework for Adaptive User Interfaces for older adults

Typing Tutor: Individualized Tutoring in Text Entry for Older Adults Based on Statistical Input Stumble Detection

Assistive Typing Application for Older Adults Based on Input Stumble Detection

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