Abilities
In this dissertation, we hypothesize that hand movement decreases keying accuracy. Situ- ational constraints, as well as health conditions, are two common settings where this effect can be observed.
Preliminary results, reported in Section 3.1, revealed some similarities between tetraplegic users and able-bodied within a set of touch techniques. Although these users experience some form of hand tremor, their dexterity and reach restrictions also play a significant role in target selection tasks. In order to assess the effect of hand motion on users’ performance in an isolate manner, we investigated physiological tremor. This type of tremor occurs to everyone, but is especially visible in older adults, who experience increased physiological tremor (Van Den Eeden et al., 2003)(Strickland and Bertoni, 2004)(Benito-Leon et al., 2003). Thus, in the remainder of this dissertation, we investigate how mobile solutions can be designed for users with tremor-related impairments, due to either situational constraints or health conditions.
Bridge the Gap between Situationally- and Health-Induced Impairments
Our research approach views each user as a set of abilities, which can vary within a limited range. These abilities can be affected by either (or both) situational restrictions or health conditions. This framework of thinking allows us to abstract from the cause of impairment and focus on the effect on users’ performance. More importantly, it enables us to bridge the gap between these two groups by designing mobile solutions that can fit users’ abilities whether they are being affected by SIID or HIID. Bridging the gap between these user groups brings several advantages to the research community:
Avoid the duplication of work. Technology is constantly and rapidly evolving. New devices, products, and ideas are announced every day. Due to the dynamic nature of our research field, it is very likely that several authors do know about related or even overlap- ping works created by others. This leads to the well known phenomenon of “reinventing the wheel”. Raising awareness and showing a relationship between SIID and HIID will mitigate the duplication of work.
Promote the reuse of knowledge. In addition to avoid doing work already created by others, this approach provides the means to a higher and more effective reuse of knowl- edge. Particularly, if common issues are to be found between users with situationally- and health-induced impairments, then similar solutions can be applied to both groups. This enables the unification of several domains, such as accessibility, mobile human-computer interaction, and general HCI, causing researchers to discuss common challenges as well as
opportunities.
Leverage research. Building a relationship between HIID and SIID will create a “broader and better” research community. A larger number of researchers, with distinct back- grounds and skills, will enable the creation of interdisciplinary solutions. Research will be guided by common problems; however, seen through different perspectives, which will reduce the time taken to address these issues and leverage the quality of proposed solu- tions.
Reduce costs and increase availability. One of the main problems of solutions es- pecially designed to users with health-impairments and disabilities is their target market.
The cost that is inherent to these products is typically high and supported by end-users.
By allowing solutions to be used by broader user populations, they will be available as
“mainstream” products resulting in a decrease of prices.
Remove the negative connotation of the word accessibility. The field of acces- sibility or accessible computing is sometimes the target of negative connotations. It is usually seen as a very specific domain in the HCI field, which only addresses the problems of those with very specific motor or cognitive impairments. The introduction of situational impairments brings the opportunity to demystify this myth and build solutions to a wide range of abilities. Accessible computing isNOT a research field to the minorities, because at some point in our lives, we all experience some form of impairment or disability.
Concluding, although this approach can be applied to several human abilities, our research work focuses on tremor-related impairments. Particularly, we investigate the effect of walking and increased physiological tremor on typing performance, using hand motion as a unifying measure.
Assessment of Abilities
A common need of any successful accessible system is the assessment of users’ abilities.
Since we want to design common solutions to both users with SIID and HIID, we need to objectively assess users’ abilities on common ground; that is, using the same “ability- scale”. Clinical tests or non-computer oriented procedure are usually subjective/open to interpretation or difficult to generalize and relate with users’ performance. Moreover, due to the dynamic and ephemeral nature of SIID, these approaches are inadequate.
Instead, we take an ability-based approach (Wobbrock et al., 2011) by characterizing users’ abilities through their performance with technology. Particularly, in Chapters 4 and 5, we focus most of our analysis on accuracy results. We believe that a thorough understanding of issues and challenges encountered when using current devices is a valid proxy to characterize users’ abilities. Three of the most commonly observed difficulties in typing are (MacKenzie and Soukoreff, 2002a):
Insertion errors. These occur when a key is unintentionally pressed resulting in additional characters. Users often press adjacent keys in addition to the intended key, especially if targets are small and no physical affordances are available. These errors can also occur when a key is unintentionally pressed more than once.
Substitutions. Users can miss the intended key and inadvertently press an adjacent one, resulting in an incorrect character. Similarly, the users’ fingers can slip on the screen during a key press action and the wrong letter is inserted.
Omissions. These errors occur when users omit characters. A wrong mental model of words or forgetfulness can be the cause of this type of error. On the other hand, the device’s inability of recognize a key press can also originate omission errors.
The proposed approach to measure users’ abilities, as they interact with technology, has the advantage that it can be used in real-time and on-the-go. Additionally, results can inform interface design directly, since measurement takes place with the current interface. More importantly, it enables a true and fair comparison between SIID and HIID, as abilities are objectively and independently assessed of external factors (e.g. demographic profile, experience, and so on). Chapter 6 presents a comparative analysis between user groups, where their main differences and similarities are analyzed.
Context-Sensing
We believe that the main cause of disability of both health and situational impaired users is hand tremor. The overall movement of the device introduces new interaction challenges that decrease users’ typing performance. Sensing capabilities of current mobile devices have the potential to measure this movement and counteract its effect.
Taking advantage of the tri-axis accelerometer to compensate for hand tremor is a very promising research opportunity and still in its early stages. This information can be used as a common measure of tremor for both user groups, enabling the development of broader and cause-agnostic solutions.
Moreover, leveraging the mobile device’s accelerometer is also advantageous in order to deal with the dynamic nature and variability of SIID. Motion data can be used to pre- dict the users’ abilities in a multitude of situations. Together with touch information, context-sensing can provide an effective way to compensate entry errors and enhance typ- ing accuracy.
Modeling Users’ Abilities
After assessing users’ performance and sensing the context, we need to model users’ abili- ties. This is a particularly challenging task if we take into account the general variability
of human behavior. This phenomenon is even worse when considering users with impair- ments. Two persons, even with the same health condition, can differ drastically on their abilities. In our case, we can only expect a higher variability since we are dealing with situationally- and health-induced impairments. We mitigate this effect by assessing users’
abilities based on their performance with technology, thus no generalizations are needed to inform interface design/adaptation.
Regarding the modeling of users’ abilities, in Chapter 7, we propose and evaluate different approaches. First, we describe their abilities considering the tremor-related errors they perform when typing: insertion and substitution errors. We propose the use of different typing features for each type of error in order to characterize their behavior. Second, the modeling itself is done by recurring to machine learning techniques and using real typing data. We opted to use machine learning algorithms, since they are typically more effective in unveiling hidden patterns in large amounts of data than simple decision procedures or heuristics.
Finally, users’ abilities are modeled through the combination of different sub-models, each characterizing one type of error. Both touch and motion information is used to describe users’ typing abilities.
Application of Knowledge
After assessing and modeling users’ abilities, our goal is to apply that knowledge to improve typing accuracy of both user groups. All built and “learned” models are evaluated in Chapter 7 in order to maximize the gain of our final solution. We assess and compare the models’ ability to describe users’ typing skills by analyzing their accuracy results.
Finally, we perform a simulation using previously collected data in order to assess the effectiveness of our solution. We decided to apply our adaptations in a non-visual manner;
that is, individual keys and keyboard adaptations are hidden from users. This approach has shown to prevent confusion and reduce cognitive load relatively to a visual-adaptive interface (Findlater and Wobbrock, 2012).