Discussion on Maximizing Access

In this section, we review several projects that attempted to maximize access to computers by designing interfaces that cope with a wide range of abilities and situations. In this sub- section, we compare the surveyed solutions following five criteria: adaptability,adaptation source,context,effect, and ability range. Table 2.1 presents the overall comparison. Each one of the criteria is explained and discussed below.

Adaptability

Adaptability refers to the approach used to accommodate users’ abilities: self-adaptive or user-adaptable. Each of these approaches has its own strengths and weaknesses (H¨o¨ok, 2000), however most of presented projects take an adaptive approach (see Table 2.1).

Indeed, intelligent user interfaces seem to be the future of human-computer interaction.

More than ever, most of us spend our day near computerized devices, both desktop and mobile, which now have the capabilities to sense and understand usage patterns. Adapting interfaces to each user is the next step. For what matters for this dissertation, adaptive interfaces have the potential to measure, model and automatically adjust themselves to users’ abilities. SUPPLE (Gajos and Weld, 2004) is an example of a system with high adaptability, since it can transform the entire user interface, while others, such as trackball EdgeWrite (Wobbrock et al., 2009), only deal with small local adjustments.

On the other hand, some projects are user-adaptable; that is, they provide useful recom- mendations to activate or tune certain interaction parameters (Trewin et al., 1997) that will improve users’ performance. End-users have complete control of how, when, and if they want to perform these changes. Privacy issues are also minimized when using this ap- proach as users may choose what information that want to provide to the system (Trewin, 2000) .

Finally, while we acknowledge that interfaces can greatly benefit from adaptation, either self-adaptive or user-adaptable, this is not a crucial requirement in order to deal with a wide range of abilities. For example, SteadyClicks (Trewin et al., 2006) allow both motor- impaired and older adults to improve their clicking performance. Even though, both these user populations vary greatly in their range of abilities. Similarly, work by Hurst et al.

(Hurst et al., 2008) showed that it is possible to automatically identify pointing problems from a wide range of user populations (young, older adults, motor-impaired, and users with Parkinson’s disease) using a common solution.

Adaptation Source

All adaptive or adaptable works have to be able to measure, model and apply changes to the interfaces. Adaptation source represents the data that is used to perform those actions.

In fact, most successful examples have something in common: they take advantage of users’

performance with technology in order to improve it (see Table 2.1).

Obviously, the source of adaptation is tightly related with the task at hand. Moreover, data collection can be performed with acontrolled set of tasks or “in the wild”. Reliably measuring users’ abilities outside a controlled test battery is a challenging task. While in a controlled test designers know how to identify success and errors, accurately measur- ing tasks in the wild introduces uncertainty and requires inferring the users’ intentions.

For instance, knowing what users are looking at, trying to accomplish, trying to select, attempting to write, and so forth.

The automatic mouse pointing assessment utility (Hurst et al., 2008), needs to know where targets in the interface are located, segment mouse movements into discrete aimed pointing movements, and infer what mouse behaviors constitute an error. In the same way, understanding text-entry errors without knowing `a priori what users intended to write;

that is, without presenting to participants a well-known set of phrases, can be a daunting task. Some efforts have been made in this direction, using heuristics and language-based approaches (Wobbrock and Myers, 2006)(Baldwin and Chai, 2012).

Context

The context criterion refers to the ability to leverage the device’s sensors to collect data and improve users’ performance. Overall, this research area is still in its infancy, since inferring context through sensors such as accelerometer, GPS, or gyroscope can be a challenging task. Still, this is a very promising research and application field for the near future (Hinckley et al., 2000), mostly due to the ubiquity of mobile devices.

Sensing the context in which users are in can also be of great value to infer their abilities, particularly if they are the target of situational impairments. Furthermore, using that knowledge to change the interface and cope with their needs has great appeal. Current sensors can be the vehicle to deal with the dynamic and heterogeneous nature of SIID.

Some projects have started to emerge in this area (Kane et al., 2008b)(Rahmati et al., 2009)(Goel et al., 2012); however, results are mixed: some are able to compensate for the problems imposed by context, while others showed to be ineffective.

In our research work, in addition to sensing context to compensate input errors, we also demonstrate that it can be an effective approach to unify the domains of SIID and HIID (see Chapter 7). Future work should investigate the effect of different contextual factors, while “on the go”, such as environmental factors (light, temperature, noise, rain, wind,

etc.), dual-tasking (meetings, working, walking in a busy street), or even social contexts (parties, concert, movie theater).

Effect

Accessible computing solutions can have a visible or invisible effect. If we consider self- adaptive approaches, especially continuous and dynamic adaptations, visual feedback can be unsettling and have a negative effect that counteract the effects of adaptations (Find- later and Wobbrock, 2012). Indeed, when visual adaptations are employed one should consider the effect of those adaptations on users’ performance, which means that their abilities should be again measured and modeled (accounting for the adaptation effect).

This creates an adaptation cycle that should be treated carefully, by ensuring that user performance converges rapidly.

In the case of invisible adaptations, prior work have reported benefits (Paymans et al., 2004)(Findlater and Wobbrock, 2012); still, transparency and predictability are important factors to consider. Users may not understand how the interface is adapting and may have trouble in predicting what the system’s response will be to their actions. Nonetheless, it is not clear how much of the underlying adaptive process should be exposed to the user.

Proving visual feedback can lead to increase trust towards the system, but not all users gain a greater understanding or even want that information (Bunt et al., 2007).

From all reviewed works, only Findlater and Wobbrock (Findlater and Wobbrock, 2012) compared visual versus non-visual adaptations. The remaining surveyed authors opted for one condition. For instance, SUPPLE++ used a visible approach, as it performed long-term changes to the interface. Kane et al. (Kane et al., 2008b) also opted for a visual change by dynamically increasing target sizes. On the other hand, the invisible keyguard (Trewin, 2002), or anglemouse (Wobbrock et al., 2009) did not make their adaptations visible.

Ability Range

As stated in the beginning of this section, our goal was to present some of the most relevant projects that aimed to address a wide range of abilities. Indeed, several interesting works were reviewed, some using adaptive or adaptable functionality, while others used static interfaces. Yet, many focused on the variability of abilities within a user group (Table 2.1), either motor-, visual- or situationally-disabled. Some explored more than one type of impairment, but still focused on health-induced conditions (e.g TrueKeys (Kane et al., 2008a), SteadyClicks (Trewin et al., 2006)).

Only Yesilada et al. (Yesilada et al., 2010a) investigated the use solutions for users with HIID and SIID, using a technology transfer approach. This dissertation extends this work

focusing on touch-based devices and taking advantage of their full potential. Moreover, we use the device’s sensors to access users’ abilities, either affected by SIID or HIID, and improve their performance. From our knowledge, no one has ever explored this approach.

We analyze each user group individually (Chapters 4, 5) and then assess their differences and similarities (Chapter 6) in order to accommodate tremor-induced impairments in a unifying manner (Chapter 7).

Table 2.1: Overall comparison of reviewed projects. In the ability range column:

MI (Motor-Impaired), VI (Visually-Impaired), AB (Able-Bodied), SI (Situationally- Impaired).