2-Thumb Gesture: The Design & Evaluation of a Non-Sequential Bi-manual Gesture Based Text Input Technique for Touch Tablets Khai N. Truong1, Sen Hirano2, Gillian R. Hayes2, Karyn Moffatt3 2Donald Bren School of Information 1Department of Computer Science and Computer Sciences 3School of Information Studies University of Toronto University of California, Irvine McGill University Toronto, ON M5S 2E4 Irvine, CA 92697 Montreal, QB H3A 1X1 [email protected] {shirano,gillianrh}@ics.uci.edu [email protected]

ABSTRACT two smaller strokes that can be drawn by both thumbs over the keys We present 2-Thumb Gesture (2TG), a non-sequential bi-manual sequentially on their respective sides of the keyboard. gesture-based text input technique for touch tablets, which enables In this paper, we demonstrate that the two-stroke gestures for the user to enter text with both hands by using each thumb to draw entering English words are highly unique and hence each stroke can small strokes over the keys on their respective sides of the keyboard be drawn over the letters non-sequentially by the two thumbs. We without waiting for their turn in the letter sequence of a word. The describe our design and evaluation of a non-sequential bi-manual results of a study comparing 2TG to Swype (a 1-finger word gesture based text input technique for touch tablets, called 2-Thumb drawing method), suggest that the learning and use of the 2TG Gesture or 2TG (see Figure 1). Through a controlled experiment, technique to perform text input is comparable with the commercial we establish the feasibility of learning and using the 2TG technique Swype technique by those who had no prior experience with either. to perform text input alongside the commercial Swype keyboard (a Furthermore, participants were able to hold and use the tablet with 1-finger word drawing software). As expected, those with prior both hands without experiencing the substantial fatigue that results knowledge of Swype were faster using Swype than 2TG; however, from a one-handed approach. Only 60 minutes after being their learning and performance rates with our two-thumb version introduced to the technique, participants were able to use the 2- were not statistically different from those with no prior Swype Thumb Gesture keyboard to enter text at 24.43 wpm, with an knowledge. Furthermore, by the end of the study, participants had uncorrected error rate of 0.65%. begun to learn the gestures for many short words and were able to perform almost one fifth of the input by simultaneously gesturing Categories and Subject Descriptors with both thumbs and touching keys out of order with respect to H5.2 [Information interfaces and presentation]: User Interfaces. – their turn in the actual letter sequence. These results suggest that Input devices and strategies. as participants begin to remember more gestures over time, 2TG General Terms Design, Experimentation, Human Factors

Keywords Text entry, soft keyboard, gesture, bi-manual interaction 1. INTRODUCTION As mobile devices have grown in popularity, an increasing number of text-input techniques have been introduced. One such technique is shape writing—a method that allows the user to input text by drawing a stroke with a near unique shape containing points close to all letters found sequentially in a word. Drawing shapes enables faster input than traditional typing techniques that require the individual pressing of each letter in a word. As a result, it is now found commercially in a variety of products, including ShapeWriter [36], Swype [28], and SlideIT [26]. More recently, sales of large mobile touchscreen devices (i.e., those with screens four inches and larger) have increased. The size and weight of these devices make them hard to hold steady with only one hand while interacting with the other for long tasks, especially text input. As a result, commercial products such as Keymonk [10] and research efforts such as Bi et al.’s investigation of bimanual gesture keyboards [2] have begun to explore how words can be drawn on mobile touchscreen devices using both hands. In contrast Figure 1. The 2-Thumb Gesture keyboard. The user enters to previous shape writing techniques, which support text input as text by using both thumbs to perform drag gestures across the drawing of a single stroke containing points near the keys found the letters in a word on their respective half of the keyboard. sequentially in a word, these methods break the input of a word into can potentially extend similar performance benefits found in the 20 wpm with Cirrin after using the technique for over 2 months; traditional Swype technique to large-size touchscreen tablets, while Isokoski and Raisamo [9] showed that participants were able to enabling the user to hold and use the tablet with both hands to reach 16 wpm with Quikwriting using a stylus after 20 15-minute eliminate the substantial fatigue that results from a one handed sessions. In 2003, Zhai and Kristensson began to explore shape approach. writing as a way to allow users to perform shorthand gestures to input words on an optimized keyboard layout [12][13][34][35]. 2. RELATED WORK This concept has since been adopted by several commercial The 2-Thumb Gesture keyboard was designed to allow the user to products for small mobile touchscreen devices, such as hold the tablet with both hands and draw two short stroke gestures ShapeWriter [36], Swype [28], and SlideIT [26]. These virtual with both thumbs simultaneously, touching keys non-sequentially. keyboards have been used on a variety of platforms, including small Bi-manual word level gestures for entering text on a soft-keyboard devices, tabletops and tablets. Expert performance of shape writing has been previously introduced in commercial products such as using a QWERTY layout on tabletops have been computed to reach Keymonk [10] and research efforts such as Bi et al.’s bimanual 40.7 wpm [24]; but have not been computed for the tablet form gesture keyboard [2]. Bi et al. [2] showed that a bi-manual gesture- factor. In Bi et al.’s study [2], participants reached an average of 30 based method can reduce the length of the stroke used to draw a wpm using the unimanual shape writing technique on a tablet. word into two shorter strokes which are drawn by the thumbs. Our However, tablet users often describe concerns of fatigue in the hand work builds upon these systems by allowing both thumbs to draw holding the device as well as in the finger performing the gestures small strokes over the keys on their side of the keyboard without back and forth across the large touchscreens [5][15]. waiting their turn in the letter sequence of a word. Though Bi et al. mention the possibility of concurrent input [p.144, 2], this was not 2.2 Thumb-Based Text Input explored; the user touches the keys in a word sequentially with both Although very few techniques have been altered or designed Bi et al.’s bimanual keyboard and Keymonk. specifically to support thumb-based text-input, users often are able Below, we discuss how our method builds upon other works. We to effectively employ their thumbs to perform input. For example, review prior research in gesture-based text input, thumb-based text Silfverberg et al.’s study shows that two-handed index finger and input, and two-handed text input methods for mobile devices. one-handed thumb use of multi-tap are ~27 wpm and ~25 wpm respectively [25]; their study also shows that two-handed index 2.1 Gesture-Based Text Input finger and one-handed thumb of T9 is ~46 wpm and 41 wpm. One Virtual keyboards can enable text-input on mobile devices that do notable exception is Gonzalez et al.’s work [7] which explored not have a physical mini-QWERTY keyboard. Methods that different ways of supporting text-entry while the user is driving and support text-entry using gestures—either through touch or stylus— her hands are on a steering wheel. Their work showed that enable users to enter text either at the character-level or at the word- EdgeWrite [33] can be adapted to work on a Synaptics Stampad level. A detailed review of such techniques can be found elsewhere that is embedded on steering wheel. The focus of these previous [19]. works has been to examine how use of the thumb to support one- handed interaction affects performance. Results from these works Character-level input techniques allow users to type a word by show that performance is minimally affected, but provides other making multiple stroke gestures that correspond to different letters, benefits (such as it frees up the other hand, and it requires less one a time. The most common character-level technique is Graffiti, visual attention because the targets are in fixed location relative to created by Palm, Inc. The Graffiti alphabet resembles the Roman the thumb’s anchor position). An important implication when letters, which makes it easy for users to recognize and learn [4]. designing thumb-based interactions, however, is to make target Likewise, unistroke maps a single stroke to a single character [6]. sizes large enough that they can be accurately selected by the thumb Unistroke gestures do not resemble Roman letters, but are designed [22]. to be well distinguishable from one another. One key distinction of Graffiti and unistroke from other character-level input techniques 2.3 Two-Handed Text Input is that the user does not need to perform target selection as a part of Because typing on a desktop QWERTY keyboard is typically their text-entry. Castellucci and MacKenzie’s evaluation [4] performed as a two-handed task, it is not surprising that users can comparing Graffiti and unistroke shows that participants were able effectively employ both hands with many existing text-entry to input 15.8 wpm using unistroke and 11.4 wpm using Graffiti; methods. For example, MacKenzie and Soukoreff [17] studied two however, there were no statistical differences in input speed handed (specifically thumb) use of physical mini QWERTY between the two. EdgeWrite uses a character set that resembles the keyboards and predicted that expert typing speed can reach ~61 Roman letters, but only requires the user to touch corners of a box wpm. Novel techniques for chording on a standard 12-key keypad in a particular sequence as they draw these gestures [33]. This also have been developed to leverage two-handed input, such as enables for users with motor problems to type using a lower ChordTap [30]. keystroke per character (KSPC) than with Graffiti. The Vector keyboard [11] and MessagEase [21] combine unistroke gestures On large touchscreen devices, the 1Line Keyboard is a technique with target selection to simplify the gesture set. Although no that supports touch-typing on a tablet with a reduced layout [16]. numbers were given within the paper [11], it can be derived from Most other work, such as LucidTouch [31], SwiftKey [27], Thumb the presented graph that 9 participants were able to input text with Keyboard [29], and Samsung’s S1 tablet support typing by dividing the Vector keyboard at ~10.3 wpm. Using a Fitt’s Law analysis of the keyboard interface into two halves, allowing users to press the the MessagEase design, Nesbat [21] concludes that a soft-key keys with the hand closest to each. The 2-Thumb Gesture keyboard version of the keyboard potentially can support expert text input presented in this paper, along with the previously mentioned performance between 30 and 37 wpm. Keymonk [10] and Bi et al.’s bimanual gesture keyboard [2], differ from these other works in that users employ their thumbs to draw Word-level gesture-based input techniques, such as Cirrin [20] and stroke gestures across the keys on each side of the keyboard instead Quikwriting [23], allow users to enter a word with a single stroke. of performing individual key-presses. Bi et al. [2] investigated two Mankoff and Abowd [20] reported that one user was able to achieve modes for completing the entry of a word: finger-release and space-

Figure 2. The 2-Thumb Gesture keyboard interface. The design draws inspiration from the Microsoft Natural keyboard; it enlarges the keys at the center (T, G, B, Y, H, N) to split the layout into two distinct halves while preserving the familiar QWERTY layout. Keys on each side are intended to be swiped only by the thumb on that side of the screen. required. They showed that after 1.5 hours, participants were able we only explore finger-release in this work. Furthermore, we to enter text at 26 wpm and 21 wpm with the finger-release and explore allowing the user to draw the strokes non-sequentially, that space-required methods respectively. Although participants were is, the thumbs do not need to wait until their turn in the word’s letter slower with the bimanual techniques, they perceived that it was sequence to continue drawing their respective stroke. By allowing more comfortable and less physically demanding than a unimanual the two thumbs to non-sequentially draw short stroke gestures on gesture-based technique. The benefits and challenges associated their side of the screen to touch all the letters in a word, it is possible with two-handed input have been well studied [1][3][8][14], the to also reduce the amount of time required to enter a word. research question that this paper seeks to answer specifically is how to extend previous work to enable the user to perform simultaneous 3.1 Key Layout & Size word drawing gestures on large touchscreen tablets while holding Because the size and weight of a tablet make it hard to hold single- the device in both hands. handed for long tasks, we devoted special attention to enabling the user to hold it steady with both hands during text input. Given that 3. DESIGN & IMPLEMENTATION both hands would be holding the device, we designed the The 2-Thumb Gesture soft keyboard is a non-sequential bi-manual keyboard’s layout and size specifically to support comfortable gesture based text input technique for touch tablets. We developed thumb-based input. 2TG as an input method editor (IME) for Android OS version 2.3 The 2-Thumb Gesture keyboard preserves the QWERTY layout to or higher. In contrast to prior work designed to optimize input with avoid the overhead associated with learning a new letter the tablet placed on a table (e.g., the 1Line keyboard [16], we arrangement. To prevent the user from straining to reach any key designed 2TG for use while holding the tablet (i.e., with the device with the thumbs—which happens most often with keys towards the cupped in both hands and using the thumbs for input). In particular, middle of the keyboard (T, G, B, Y, H, and N), we enlarged these we are hoping to enhance experiences in which users must stand center keys, similar to the Microsoft Natural keyboard. Thus, the and hold the tablet (e.g., a nurse taking notes on her rounds, a coach layout of the keys on both halves remains visually similar to taking notes on the field, a user typing on a bus). Similar to Bi et traditional desktop keyboards and allows easier access of these al.’s bimanual keyboard [2] and Keymonk [10], the user draws two frequently used keys (see Figure 2). separate strokes that together touch all the letters in a word. Because Bi et al. have previously showed that the finger-release 3.2 Input Language and space-required gesture completion modes resulted in Users input text by drawing stroke gestures over the letters comparable entry speed, with finger release being slightly faster, sequentially (see Figure 3). However, because the thumbs only

Figure 3. Typing love as a novice user. The user starts by placing her right thumb on the L key and then swipes it to O. Next, she places her left thumb on V and then swipes it to E. Once she lifts both thumbs from the keyboard, the keyboard interprets the gesture into a list of candidate words and automatically selects the highest frequency word. draw on their side of the screen, the stroke gestures are inherently 10,000 words, 97.19% can be typed without the need for different from the unistroke gestures supported by counterpart disambiguation and 99.96% appear in the top 4 most likely words systems. First, although a novice user may still swipe her thumbs for any gesture sequences. Overall, 99.51% of the words in the to different keys in the sequential order defined by the respective entire corpus appeared in the top 4 most likely words for its gesture letter’s appearance in the word, the strokes themselves no longer sequences (see Figure 5). visually capture the full sequential order of the keys across both We note that other approaches for performing gesture recognition sides of the keyboard. This property of the gesture language results can be adopted, such as an adaptation of the shape writing in a feature that allows expert users who are familiar with the algorithm [13] or machine learning. In this paper, we investigate gestures to input the strokes on each side of the keyboard non- the user’s ability to learn and perform the strokes, without sequentially and even simultaneously (i.e., without needing to wait introducing system support for inferring the user’s intent; this for that side’s turn in the word’s letter sequence). Figure 4 allows us to then analyze how much effect adopting a different illustrates how an expert user would type love using both thumbs. gesture recognition method and relaxing the requirement for the 3.3 Gesture Recognition users to accurately and precisely draw a stroke could potentially have on the user’s text input performance. Our method performs a dictionary lookup based on the gestures drawn using each hand. However, because keys for letters not 4. EVALUATION METHOD found in a word are crossed while the user swipes between the We evaluated the 2-Thumb Gesture technique in a laboratory study letters in the word, gestures must be interpreted as a set of likely focused on the learnability and usability of the technique as a text candidate words. As mentioned above, we removed all entries in input method on tablet computers, with Swype [28] as a reference COCA that contained non-alphabetic characters as well as those technique. As previously noted, in Bi et al.’s study [2], participants entries not found on dictionary.com. We then extracted the list of reached an average of 30 wpm using the unimanual shape writing keys that must be entered sequentially on the left side of the technique on a tablet, while reaching between 21 and 26 wpm using keyboard as well as its right side equivalents. We next built an the bimanual approach. Similarly, we included Swype as a SQLite database holding the word, its frequency, and the left and reference technique to develop a rich understanding of the right key sequences for entering it. advantages and drawbacks of both one-finger and two-thumb shape To determine the word the user entered through the thumb gestures, writing on touch tablets. the keyboard computes the list of key points from each stroke, We note that our decision to compare against a fully implemented including the first and last points. Key points are points in a gesture commercial unimanual shape writing technique has advantages as where the thumb changes its direction either vertically (between up well as drawbacks. We, of course, did not expect to outperform a and down) or horizontally (between left and right). We used these fully implemented Swype; however, we hoped to demonstrate that strict criteria to lower the false detection of key points. However, our implementation’s performance is promising in relation to a this approach then only extracts from the strokes a set of key fully implemented system that has error-handlings. A drawback to sequences that partially matches those in the database for the word this decision is that it does not allow us to fully compare the 2- that the user wants to enter. The input key sequence entered may handed approach in which keys do not have to be touch sequentially also partially match with the key sequences for a few other words. against a 1 finger approach implemented using the same gesture The system retrieves a full list of the possible candidates, sorts the recognition method. That issue and controlling for other design responses based on the frequency of the words in the COCA corpus, parameters (e.g., support for word prediction, typo detection/auto- and displays the top four as “candidate words” for the gestures, with correction, and so forth) are outside of the scope of this specific the top one automatically selected and shown in the editor field. investigation and can be points for future studies. To evaluate the coverage of the words in COCA supported by our algorithm, we computed the percentage of words that appear as the 4.1 Study Design & Procedure most likely candidate for the associated gesture sequences, as well A 3-factor mixed design was used with input technique (1-finger as the percentage of words that appear as the 2nd, 3rd, and 4th most Swype or 2TG) and session (1, 2, or 3) as within-subjects factors, likely candidates for those sequences. Again, these calculations and prior experience (No Experience, Prior Swype Experience) as assume a perfect gesture that travels through the center of each key in the word’s letter sequence. The results showed that in the top

Figure 4. Typing love as an expert user. The user starts by Figure 5. Coverage of the gesture recognition algorithm. placing her right thumb on L and her left thumb on V, and Each line shows the coverage of the words returned within then simultaneously drags her right thumb to O and her left the first, second, third and fourth most likely candidate for thumb V. its gesture sequences. a between subjects factor. Presentation order was fully 5.1 Performance counterbalanced and participants were randomly assigned to a Overall, text input rate was impacted by input technique, indicating condition. that overall the more familiar 1-finger Swype method outperformed 2 During the experiment, we asked participants to enter short phrases the novel 2-Thumb Gesture technique (F1,8 = 47, p = .0001,  = presented on the screen as fast and accurately as possible using the .855). At the 3rd session, participants averaged 32.4 wpm (SD: 8.51) given text entry method. We prepared phrases based on MacKenzie with Swype and 24.43 wpm (SD: 4.29) with 2TG. However, there and Soukoreff‘s set [18]. We randomized the order of the phrases was also a significant interaction between technique and expertise 2 and grouped them into six blocks of phrases. No phrase repeated (F1,8 = 16.29, p = .004,  = .671) Pair-wise comparisons revealed within a block. that the effect of technique only held for those with prior Swype Each person participated in three sessions. Consecutive sessions experience (p < .001). For those with no prior experience with the were scheduled 2–72 hours apart around the participants’ techniques, no significant difference was found (p = .081). There schedules. At the first meeting, participants also learned about and was, however, no main effect of expertise (p =.079). Figure 6 shows practiced each text entry method. words-per-minute for each technique by level of expertise. Each session consisted of two 20-minute half-sessions (one 20- A main effect of session on words-per-minute (F1.22,9.72 = 44.02, p 2 minute half-session for each technique). Each half-session started < .0001,  = .846), confirmed by pair-wise comparisons (Session with three practice phrases, followed by the phrases pre-arranged 1–2, p = .001; Session 2–3, p < .001), showed that performance for that block. Participants had 20 minutes to type as many phrases improved with each session. This effect was consistent across as they could. We counter-balanced the presentation order of the techniques and expertise, as shown in Figure 7 and indicated by the text entry methods across the participants for the first session and absence of interaction effects (session  technique: p = .271, alternated their presentation for subsequent sessions with the same session  expertise: p = .716, and session  technique  expertise: participant. p = .62). 4.2 Participants The average uncorrected error rate (the rate of errors remaining in the transcribed text, including insertion, substitution, and deletion We used word of mouth, flyers, and online posts to recruit right- errors [32]) over all sessions was 0.4% (SD=1.8%) and 0.7% handed participants. Because of procedural inconsistencies, data (SD=3.2%) for the Swype and 2-Thumb Gesture techniques, for 2 participants were discarded from analysis, leaving 10 respectively. These low rates are not surprising once we look at participants (6 female and 4 male; with a median age of 29 years, their corrected equivalents. On average, participants had a SD=5.3) for this experiment. All participants had at least a high- corrected error rate of 9.8% (SD: 15.4%) with the Swype technique school level of English literacy. All participants reported no motor and 14.8% (SD: 14.7%) with the 2-Thumb Gesture technique, disability in their arms and hands, and reported no visual disability. indicating few errors remained in the input, because participants Half of the participants reported having prior knowledge of how to were correcting their errors in this study. use Swype (and either currently used it or had used it in the past); the remaining 5 participants had no prior experience with the As with input rate, expertise and technique impacted the corrected technique. error rate, yielding a main effect of technique (F1,8 = 21.36, p = 2 .002,  = .727) and an interaction effect between technique and 2 4.3 Apparatus expertise (F1,8 = 12.98, p = .007,  = .619). Pairwise comparisons We used an Asus Eee Pad Transformer for the text entry interfaces. revealed that those with prior Swype experience had higher We asked participants to hold the tablet during the study. For the corrected error rates with 2TG (p < .001), suggesting a possible Swype condition, we used the Beta 3.26 version of the Swype soft negative transfer effect. There was no difference between the keyboard. The height and width of the keys in the Swype soft techniques for those without Swype experience (p = .492), as keyboard are the same as those in the 2-Thumb Gesture keyboard shown in Figure 8. There was also a significant interaction between 2 (with the exception of t, a, g, h, and n keys, which are wider for session and technique (F1,8 = 5.69, p = .014,  = .416). Pairwise 2TG as shown in Figure 2). comparisons revealed that the main effect of technique only held in sessions 1 (p < .001) and 3 (p = .001), but not in session 2 (p = .11). 5. RESULTS In session 2, the error rate spiked for the Swype technique, but To analyze collected data, we modified Wobbrock and Myer’s remained consistent in 2TG. For uncorrected error rate, no StreamAnalyzer software [32] to account for word-level operations significant differences were found, which is not surprising given like word deletion and word corrections. We removed any data the low rates observed. point over 3SD from the average typing time and the average error rate for each participant in each session as an outlier. In total, 3.0% 5.2 Non-Sequential Bi-Manual Input of the data points were removed. We designed the 2-Thumb Gesture technique so that both thumbs We ran a 232 (technique  session  expertise) repeated could non-sequentially draw stroke gestures. The challenge with measures ANOVA on words-per-minute, and the corrected and being able to perform text input with the 2-Thumb Gesture uncorrected error rates. In the presentation below, where df is not keyboard in this manner is being able to identify or recall the an integer, we have applied a Greenhouse-Geisser adjustment for correct left and right gesture sequences for a word, behavior that non-spherical data. All pairwise comparisons were protected we expect to develop with practice. against Type I error using a Bonferroni adjustment. Along with As expected, participants initially found the idea of performing 2- statistical significance, we report partial eta-squared (η2), a measure Thumb Gesture to be daunting. of effect size. A preliminary analysis, which included presentation order as a between-subjects factor, yielded no significant main or “It seemed hard at first to kinda cut the words and type them interaction effects involving order, giving us confidence that in halves. I had to be more conscious of what each thumb was counterbalancing the techniques sufficiently accounted for any supposed to do. But it’s a cool idea and technique. I got used learning or fatigue effects. to it.” –P12 (22, F) Thus, when entering a word for the first time, the thumbs would each draw a stroke over the keys on their side of the keyboard to sequentially spell each letter in a word. However, an interesting outcome of the initial mental and motor requirements associated with learning the 2-Thumb Gesture method is how participants began to remember the gestures, more so than with Swype. Participants commented that they began to remember gestures for short words such as “the” and “this.” “I liked the two hand method better for shorter words! I know the gestures for words like ‘the’ and ‘water.’” –P2 (23, F) Not surprisingly, as participants began to learn the gestures for short words and common letter sequences, they also began to draw both strokes simultaneously, touching keys out of order with respect to their turn in the actual letter sequence. Participants perceived that doing so improved their input rate. “I don’t have to do things simultaneously, but I feel like I would be slower [sequentially].” –P2 (23, F) Figure 6. Words per minute for 2 Thumb Gesture and Swype, by prior experience with Swype. Error bars show At the 3rd session, 19.0% (SD: 8.0%) of the input happened in this 95% confidence intervals (N=10). manner. We expect that as participants begin to remember more gestures over time, they will continue to improve their text-input rates. For example, participants commented that for longer words, they had begun to remember the gestures for portions that made up of common letter sequences, such as “ing” and “pre.” 5.3 User Feedback Although mentally dividing a word into a two stroke gesture seemed difficult to the participants initially, participants started to describe it as being “intuitive” and even fun to use as they continued to learn the technique,. For example, after initially struggling to use the 2-Thumb Gesture keyboard for the first 5 minutes, P7 (38, M) declared “I got it.” As a person without prior knowledge of Swype, P7 was able to learn both at the same rate; however, he preferred the 2-Thumb approach. “Dude, this (Swype) sucks in comparison to the new school way (2TG).”—P7 (38, M)

“The two hand method seems more intuitive. Even though two Figure 7. Words per minute across each session, by hand is less accurate, the user experience feels enhanced.” – technique and experience with Swype. Error bars show 95% P5 (30, M) confidence intervals (N=10). Furthermore, although a few participants suggested ways of improving the comfort of 2TG (e.g., by changing the placement and size for some of the keys), participants uniformly reported discomfort when using Swype. In particular, participants noted that holding the tablet with one hand can be tiring on the hand and the wrist. “Oh my g-d, my hand!” –P2 (23, F) “This gets really heavy after a while! My hand…the one that holds the tablet is dead. The fatigue is definitely less with the two hand method.” –P4 (31, F) “After 5 minutes or so, my finger started hurting and my wrists were cramping!” –P11 (20, F) However, all participants described the Swype technique as being “more forgiving” and some indicated that with time, the two handed technique might improve.

“Two hand seems more intuitive, but the technology is not quite Figure 8. Corrected error rate for 2 Thumb Gesture and there.” –P5 (30, M) Swype, by prior experience with Swype. Error bars show 95% confidence intervals (N=10). Participants noted that the commercial Swype keyboard always Because our system disallowed this behavior, participants’ input tried to guess what they were gesturing. For example, the gestures were rejected by the recognizer when they included keys participants could miss some keys (instead only touching their from the other half of the screen. To address this problem, the neighboring keys) when performing a gesture and the system may gesture database can be built to treat the middle keys as ones that still return what was intended as a potential candidate word. can be input as a part of the gestures from either side of the screen. Furthermore, they described being able to continue with a gesture Alternately, the keyboard could be further split, leaving a space and correct it even if they messed up. Although the gesture with invisible keys in between, as on the iOS5 split keyboard and recognition approach that we adopted also allows the participants Bi et al.’s bimanual keyboard [2]. to adjust their gesture if it is incorrect, it requires the participants to accurately touch all the keys in the word. 6.1.2 Typos Overall, the average corrected error rate for 2TG was 14.9%. In this By developing and evaluating a keyboard without any tolerance for work, the corrected error rate reflects the rate at which two types of input error, we were able to gain an understanding of the actions could have occurred: 1) the manual deletion of incorrectly effectiveness of the 2-Thumb Gesture technique by itself alone. typed text and re-entering correct text, and 2) the automatic Thus, the results of the study demonstrate the feasibility of the correction of incorrect text that was returned by the gesture method. Though 2TG did not outperform Swype, adding tolerance recognizer through the selection of another word from the for input error to our method (similar to that already provided by candidates list (i.e., the user performed a disambiguation action). the commercial Swype technique used as a comparison) can only As mentioned earlier, for the 10,000 highest frequency words in the improve its user performance. COCA, 97.19% can be typed with the 2-Thumb Gesture keyboard 6. DISCUSSION without the need for disambiguation and 99.96% appear in the top 4 most likely word for any gesture sequences. When we examine In this section, we discuss some of the factors that could have rd affected the text input rate observed in the study. Additionally, we the disambiguation rate, at the 3 session, participants on average discuss some possible ways to improve upon this work. performed disambiguations for only 2.7% (SD: 1.4%) of the text that they entered. This means that the remaining amount of the 6.1 Input Errors corrected errors (~12.2%) resulted from correcting typos. Participants made two types of errors when using 2TG that limited By developing and evaluating a keyboard without any tolerance their input rates. First, the system rejected some of the participants’ for input error, we were able to gain an understanding of the gestures. Whenever the system was unable to recognize a gesture, effectiveness of the 2-Thumb Gesture technique by itself alone. The the participant had to perform that gesture again. Second, much like results from our study establish that the 2-Thumb Gesture keyboard typing on a normal keyboard, participants sometimes entered typos, is a feasible method that can be learned and used. Adding tolerance which resulted in the keyboard returning the wrong words. The for input error to the 2-Thumb Gesture keyboard can only improve design of our recognition algorithm did not recognize and fix typos its user performance. With feasibility established, an obvious next automatically. The low uncorrected error rate observed indicates step is to collect the data necessary to model common input errors. that participants corrected these typos, thereby reducing the input For example, participants sometimes touch keys from the same side rate. of the keyboard in the wrong order (e.g., gesturing EV instead of VE for LOVE), or miss the last key by a few pixels 6.1.1 Rejection Errors (undershooting the target). Using such data, we can develop an The system rejected 5.9% (SD: 2.6%) of the gestures inputted by error model that can be used to automatically correct typos. participants. Although 5.9% is a low rate, it has a noticeable effect on the participants. 6.2 Learnability “I re-enter text more in 2TG because when it's wrong, there Participants commented that the visual feedback provided by our aren't any choices.” –P3 (31, F) keyboard implementation currently does not help them remember the gesture patterns for long words. In contrast, participants did not report experiencing any rejection errors when using the Swype technique. All participants “I think one of the problems is that for long words, you start commented that they wished the technique would be as forgiving losing track of what (the letters in) the words are.” as Swype, which did not require them to directly touch all the keys –P5 (30, M) but only be in proximity of those keys. Future versions of the Currently, our keyboard implementation paints the full gestures keyboard can relax the requirement that the users must accurately until both thumbs are lifted. In future versions of the software, we touch the keys in the gesture sequence and apply a proximity will design the keyboard to better highlight parts of the gestures in threshold against which it accepts user input. which high-frequency letter sequences were inputted. As A common problem that many participants encountered was participants learn the common gestures, we can begin to adapt the touching keys towards the middle of the keyboard (T, G, B, Y, H, visualization to continue to teach them the lower frequency letter and N) with the opposite hand. Our implementation only allows sequences as well. users to touch keys from one side of the keyboard with the respective thumb. However, participants mentioned that they 7. CONCLUSION typically do not abide by such a strict input model when using In this paper, we presented the design and evaluation 2-Thumb desktop keyboards. As a result, while entering text with the 2- Gesture, a technique that breaks the input of a word into two smaller Thumb Gesture keyboard, there was a tendency for them to reach stroke gestures that can be performed non-sequentially by both for some keys with the opposite thumb. thumbs on each side of the keyboard. Our evaluation results show that after 40-60 minutes of use, participants were able to use the 2- “I have to think of the halves of the keyboard. My biggest Thumb keyboard to enter text at 24.43 wpm, with an uncorrected problem was that I had to divide the keyboard in my head and error rate of 0.65% and a corrected error rate of 14.9%. These I couldn’t go over.” –P2 (23, F) overall average performance results were similar to those reported [9] Isokoski, P., Raisamo, R. Quikwriting as a multi-device text entry by Bi et al. [2], who also showed that participants reported a method. In Proc. of NordiCHI 2004, ACM Press, 105-108. difference in comfort and physical demand between the unimanual [10] Keymonk. http://keymonk.com/ and bimanual input approaches; similarly, our study confirmed [11] Klima, M., Slovacek, V.: Vector Keyboard for Touch Screen Devices. participants were able to hold and use the tablet with both hands In Proc. of HCII 2009: Ergonomics and Health Aspects of Work with without the substantial fatigue that results from the one-handed Computers, 250–256. approach. [12] Kristensson, P.O. Discrete and Continuous Shape Writing for Text Entry and Control. Ph.D. Dissertation, Department of Computer and Beyond those results, our implementation and study show that at Information Science, Linköping University (Linköping, Sweden), the third session, participants had begun to learn the gestures for 2007. many short words and were able to perform on average 19.0% (SD: [13] Kristensson, P.O., Zhai, S. SHARK2: a large vocabulary shorthand 8.0%) of the input by simultaneously gesturing with both thumbs writing system for pen-based computers. In Proc. of UIST 2004, ACM and touching keys out of order with respect to their turn in the actual Press, 43-52. letter sequence while only needing to disambiguate 2.7% of their [14] Leganchuk, A., Zhai, S., Buxton, W., Manual and Cognitive Benefits input. As participants begin to remember more gestures over time, of Two-Handed Input. TOCHI, 5(4), 1998, 326-359. they will continue to improve their text-input rates. [15] Le Pan Life. Multi-touch Screen. Additionally, our study shows that the learning and use of the 2- http://lepanlife.com/forum/showthread.php?tid=266 Thumb Gesture technique to perform text input was comparable to [16] Li, F.C.Y., Guy, R.T., Yatani, K., Truong, K.N. The 1line keyboard: that of the commercial Swype technique by those who had no prior a QWERTY layout in a single line. In Proc. of UIST 2011, ACM experience with either method. Although input by those with prior Press, 461-470. [17] MacKenzie, I.S., Soukoreff, R.W. A model of two-thumb text entry. knowledge of Swype was faster using Swype than 2TG, their In Proc. of Graphics Interface 2002, 117-124. learning and performance rates with our two-thumb version were [18] MacKenzie, I.S., Soukoreff, R.W. Phrase sets for evaluating text entry not statistically different from those with no prior Swype techniques. In Ext. Abs. of CHI 2003, ACM Press, 754-755. knowledge. Furthermore, the current 2-Thumb Gesture keyboard [19] MacKenzie, I.S., Soukoreff, R.W. Text entry for mobile computing: does not support erroneous input in the same manner as the Models and methods, theory and practice. Human-Computer commercial Swype keyboard, against which it was evaluated. It is Interaction, 17, 147-198. important to note that these results were achieved despite the fact [20] Mankoff, J.C., Abowd, G.D. Cirrin: A Word-Level Unistroke rd that at the 3 session, 18.1% of the participant’s input were errors, Keyboard for Pen Input. In Proc. of UIST 1998, ACM Press, 213-214. which required the user to re-input the text (5.9% were gestures [21] Nesbat, S.B. A system for fast, full-text entry for small electronic rejected by the recognizer; 12.2% were typos not recognized by the devices. In Proc. of ICMI 2003, ACM Press, 4-11. system). [22] Parhi, P., Karlson, A.K., and Bederson, B.B. Target size study for one- For future work, we will improve the interface to address issues that handed thumb use on small touchscreen devices. In Proc. of prevented the participants’ input rates from being closer to the MobileHCI 2006, ACM Press, 203-210. predicted value. First, we aim to help users learn how to input many [23] Perlin, K. Quikwriting: Continuous Stylus-Based Text Entry. In Proc. common letter sequences by modifying the way that the strokes are of UIST 1998, ACM Press, 215-216. drawn on the screen to highlight those gestures. Additionally, we [24] Rick, J. Performance optimizations of virtual keyboards for stroke- will modify the interface to give users more freedom for how to based text entry on a touch-based tabletop. In Proc. of UIST 2010, input letters towards the middle of the keyboard and to remove the ACM Press, 77–86. [25] Silfverberg, M., MacKenzie, I.S., Korhonen, P. Predicting text entry requirement that users must directly touch the keys (to make the speeds on mobile phones. In Proc. of CHI 2000, ACM Press, 9-16. technique more “forgiving” like Swype). Finally, we will develop [26] SlideIT. http://www.mobiletextinput.com/ a model capturing how users incorrectly type different words in [27] SwiftKey. http://www.swiftkey.net/ order to automatically correct typos. [28] Swype. http://www.swype.com/ [29] Thumb Keyboard. 8. 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