When can accessibility help?

Abstract

Numerous accessibility features have been developed and included in consumer operating systems to provide people with a variety of disabilities additional ways to access computing devices. Unfortunately, many users, especially older adults who are more likely to experience ability changes, are not aware of these features or do not know which combination to use. In this paper, we frst quantify this problem via a survey with 100 participants, demonstrating that very few people are aware of built-in accessibility features on their phones. These observations led us to investigate accessibility recommendation as a way to increase awareness and adoption. We developed four prototype recommenders that span diferent accessibility categories, which we used to collect insights from 20 older adults. Our work demonstrates the need to increase awareness of existing accessibility features on mobile devices, and shows that automated recommendation could help people fnd benefcial accessibility features. CCS CONCEPTS• Human-centered computing → Accessibility technologies; Ubiquitous and mobile devices.

Exploration of a method for insight into accessibility design flaws based on touch dynamics

“…Incomplete cognition of smart systems and diverse touch behavior habits are the main reasons for the issue. Even though smart systems are equipped with settings and assistive tools to handle various situations, there remains controversy among user groups about the rationality of these interface elements' design, as their flaws are considered to hinder the information browsing process during user interaction (Wu et al 2021). Consequently, the assistive tools added to smart systems to improve accessibility are not easily found and used by users, leading to their ineffectiveness.…”

“…Among these, emotions are closely linked to user behavior and are thus considered one of the core elements influencing user experience (Watson et al 1988). In user experience, emotions can help maintain user loyalty to a product or brand and can also benefit designers or researchers in identifying design flaws in a product (Wu et al 2021;Ou and Verhoef 2017). Positive emotions are associated with users' positive perceptions of their experience outcomes, while negative emotions or experiences can serve as a basis for need, providing opportunities for product iteration based on user experience assessments (Partala and Kallinen 2012).…”

“…However, the quality of user experience is not entirely dominated by emotional changes, and studying user experience using emotions involves complex constraints. Some scholars have attempted to use negative emotions to identify flaws in technology or products (Wu et al 2021;Partala and Saari 2015), but there is still a lack of detailed research on the mutual influence between emotions and interaction events. Therefore, this paper aims to analyze user interaction behaviors in specific contexts, minimizing the impact of diverse environmental factors, and exploring the correlation between emotions and user interaction behaviors.…”

“…Taking the elderly as an example, smart systems often provide accessibility tools for different sensory characteristics, but they do not always clearly display the methods for activating these tools on the interface. This leads to difficulties in effectively utilizing these accessibility tools (Wu et al 2021;Lu et al 2023), thereby preventing smart systems from being fully inclusive of those who rely on these aids, resulting in deficiencies in accessibility and inclusivity in design.…”

Comprehensive Analysis for Intelligent Recommendation on Mobile Applications

“…For example, Ertuğrul et al recommend the health case information for users through learning the data collected from health care mobile apps [4]. Wu et al investigate accessibility recommendations within mobile apps which provide other ways for people with various disabilities to access computing devices [5]. Almudena et al improve user engagement and children's experience on a mobile EdTech platform through a typical recommender system by the number of games and play time [7].…”