We present a novel method of dynamic C-D gain adaptation that improves target acquisition for users with motor impairments. Our method, called the Angle Mouse, adjusts the mouse C-D gain based on the deviation of angles sampled during movement. When angular deviation is low, the gain is kept high. When angular deviation is high, the gain is dropped, making the target bigger in motor-space. A key feature of the Angle Mouse is that, unlike most pointing facilitation techniques, it is target-agnostic, requiring no knowledge of target locations or dimensions. This means that the problem of distractor targets is avoided because adaptation is based solely on the user’s behavior. In a study of 16 people, 8 of which had motor impairments, we found that the Angle Mouse improved motor-impaired pointing throughput by 10.3 % over the Windows default mouse and 11.0 % over sticky icons. For able-bodied users, there was no significant difference among the three techniques, as Angle Mouse throughput was within 1.2 % of the default. Thus, the Angle Mouse improved pointing performance for users with motor impairments while remaining unobtrusive for able-bodied users. Author Keywords: Mouse pointing, pointing facilitation, pointing techniques, control-display gain, dynamic gain adjustment, target acquisition, cursor control. ACM Classification Keywords: H.5.2. [Information interfaces and presentation]: User interfaces—input devices and strategies. K.4.2. [Computers and society]: Social issues—assistive technologies for persons with disabilities.

Understanding how pointing performance varies in real world computer use and over time can provide valuable insight about how systems should accommodate changes in pointing behavior. Unfortunately, pointing data from individuals with pointing problems is rarely studied during real world use. Instead, it is most frequently evaluated in a laboratory where it is easier to collect and evaluate data. We developed a technique to collect and analyze real world pointing performance which we used to investigate the variance in performance of six individuals with a range of pointing abilities. Features of pointing performance we analyzed include metrics such as movement trajectories, clicking, and double clicking. These individuals exhibited high variance during both supervised and unsupervised (or real world) computer use across multiple login sessions. The high variance found within each participant highlights the potential inaccuracy of judging performance based on a single laboratory session.

Since not all persons interact with computer systems in the same way, computer systems should not interact with all individuals in the same way. This paper presents a significant step in automatically detecting characteristics of persons with a wide range of abilities based on observing their user input events. Three datasets are used to build learned statistical models on pointing data collected in a laboratory setting from individuals with varying ability to use computer pointing devices. The first dataset is used to distinguish between pointing behaviors from individuals with pointing problems vs. individuals without with 92.7% accuracy. The second is used to distinguish between pointing data from Young Adults and Adults vs. Older Adults vs. individuals with Parkinson’s Disease with 91.6% accuracy. The final data set is used to predict the need for a specific adaptation based on a user’s performance with 94.4 % accuracy. These results suggest that it may be feasible to use such models to automatically identify computer users who would benefit from accessibility tools, and to even make specific tool recommendations.

Current approaches to accessible computing share a common goal of making technology accessible to users with disabilities. Perhaps because of this goal, they may also share a tendency to centralize disability rather than ability. We present a refinement to these approaches called ability-based design that consists of focusing on ability throughout the design process in an effort to create systems that leverage the full range of human potential. Just as user-centered design shifted the focus of interactive system design from systems to users, ability-based design attempts to shift the focus of accessible design from disability to ability. Although prior approaches to accessible computing may consider users ’ abilities to some extent, ability-based design makes ability its central focus. We offer seven ability-based design principles and describe the projects that inspired their formulation. We also present a research agenda for ability-based design. Categories and Subject Descriptors: K.4.2 [Computers and society]: Social issues – assistive technologies for persons with disabilities.

Accurate pointing is an obstacle to computer access for individuals with motor impairments. One of the main barriers to assisting individuals with pointing problems is a lack of frequent and low-cost assessment of those pointing problems. We are working to build technology to automatically assess pointing problems during every day (or real world) computer use. To this end, we have studied real world pointing use from older adults and individuals with motor impairments and developed novel techniques to analyze their performance. Our investigation contributes to a better understanding of real world pointing performance, and how to assess pointing performance with machine learning.