# Improving Two-Thumb Text Entry on Touchscreen Devices

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4 STEP 3: COMPUTATIONAL LAYOUT OPTIMIZATION Finding a mapping of letters to keyslots that minimizes finger travel time is an NP-complete problem that is best addressed by means of computational optimization methods [16]. In this section, we formulate this problem as a combinatorial optimization problem, extending existing keyboard optimization research from a single end-effector (i.e., a finger or stylus) to two end-effectors. In line with previous work [16], the keyboard is represented as a permutation of 26 letters, two spacebars, and four empty keyslots. Inclusion of empty slots allows the optimization algorithm to move them around the grids. The goal is to find a permutation that minimizes our cost function: average MT as defined by simulated typing of a representative corpus of sentences under equations 3 6. Representing keyboard as a permutation assigned to a fixed grid with keyslots yields a problem size of 4x We extend previous work in permutation-based optimization by creating a hybrid approach that utilizes both gradient descent and simulated annealing (Figure 7). Initially, gradient-descent search is performed from 5,000 random starting locations. We pick the 100 best permutations, assuming that these must have gotten some critical parts of the layout correct. Simulated annealing is performed 10 times for each candidate layout. This effectively searches around the promising keyboard before convergence at the best local optimum. In the final step, gradient descent is performed for the 10 best keyboards. For this step, we allow double and triple transpositions also. This modification is inspired by evolutionary algorithms wherein larger transpositions are allowed [28]. With the iteration counts given in Figure 7, this process yields a total of 5.6 million iterations. As our corpus we use the Mobile corpus, consisting of phrases written with mobile devices from the Enron dataset [12,29]. It has 2,109 sentences and 20,500 words, with, on average, 4.1 letters per word. We simulate letter-by-letter transitions by applying equations 3 6 as appropriate and keeping a record of thumb location and t elapsed. Double keypresses were assumed to follow equations 3 and 4 with D = 0 + ε. Following a recommendation from previous work [19], our spacebar policy is alternation: the thumb on the opposite side always presses the spacebar. In each iteration, the average cost C 1 of a permutation is calculated for a corpus by means of Eq After this step, two keys are transposed and the cost of the new layout C 2 is calculated. The only exception is the beginning of a new phrase, when the thumb starts at the third button from the edge on the second row (initial position). If C 2 <C 1, the new permutation is accepted. If not, there are two alternatives for acceptance or rejection of C 2. In gradient descent, we never choose a permutation with lower cost. In simulated annealing, we consult the Boltzmann distribution: P accept C! = 1/(1 + e!!!!!! ), (7) where T is the temperature parameter, which we set to 90 [16]. Decreasing the temperature parameter T prevents the search from getting stuck too early at a local optimum [28]. Our implementation follows existing work [16]: 1. set T = function (initial keyboard, T): Keyboard 3. repeat 4. choose two keys to transpose from initial keyboard 5. determine the modified keyboard s cost 6. if new_cost < old_cost then 7. accept the modified keyboard 8. else 9. use the Boltzmann distribution, Eq reduce T 11. increase i 12. until i = maximum_iterations 13. return keyboard with lowest cost Layout tuning: We conducted a row-tuning exercise for the best keyboard. This was inspired by recent split-keyboard designs such as dextr (see textwithdextr.com) in which key columns and rows are not aligned. Each row of keys (8 in all) was shifted 0, 20, 40, or 60 pixels horizontally, independent of all others, yielding 4 8 =65,536 layouts, from which we picked the best one. We looked at horizontal shifting only, because we learned that shifting vertically would require extending the thumb too far in attempts to reach the topmost keys. The resulting design shifts rows 2 4 on the left-hand side 20 pixels right). This change produced a gain of only 0.1 wpm. The negligible gain is due to the interconnectedness of keys: shifting a key decreases the distance to some keys but increases the distance to others. Outcome: KALQ KALQ (as in calculated ) is the best keyboard after 5.6M iterations and layout tuning with a predicted entry rate of 49.0 wpm. For comparison, we created a quasi-qwerty layout in a 4x4 grid, following QWERTY s division of buttons by hand and retaining their relative order (top-to-bottom, left-to-right). KALQ is superior to the quasi- QWERTY layout by 4.1% and to the alphabetical layout by 6.1%. The following observations were made about KALQ: 1. The division of work is almost equal, at 54% and 46% for the right and left thumb, respectively. 2. Alternation is rapid: 62% of the taps are switches. 3. Travel distances are short: On average, the left thumb moves 86 px, the right The spacebars are centrally located. 5. The right thumb handles all vowels except y. The clustering of vowels around the spacebar favors quick switches and minimizes travel distance. The right thumb is responsible for 64% of same-side taps. 6. The left thumb has most of the consonants, exploiting its ability to hover above the next button sooner. It has most first letters of words and most of the consonants.

5 M! B! W! H! P! X! C! R! Y! S! Z! D! N! F! V! G! T! O! I! E!! Figure 8. A visualization of two statistical properties of typing with KALQ: Average MT to key-targets (hue: slow fast) and frequency (transparency 1 100). Figure 8 depicts the average movement times and the frequency of taps on letters. It demonstrates how the right thumb s side has quick-to-operate, frequently pressed keys clustered around the spacebar, whereas the left thumb has only a few fast-action keys while the rest are more diffuse. This exploits the unique switching characteristics observed in the N-return study. A typing example is given in Table 1. STEP 4: ERROR CORRECTION Previous work has shown improvements in text-entry accuracy on mobile devices through error-correction techniques that consider linguistic context and movement characteristics [6,9,13]. Ideally, error correction should operate in real time, correcting erroneous characters as they are typed. Building on previous work [13], we constructed an errorcorrection technique for KALQ that utilizes both linguistic information and the movement model for two-thumb text entry. For each touch point T, the error-correction model finds the key K that maximizes the posterior probability: K = arg max! P K T P K. (8) Movement Model Since KALQ is a new keyboard layout there is no straightforward method to collect representative touch point data. We could not train a likelihood model on the evaluation study s touch point data as this would mean we would train the model on the same subjects. Therefore, we instead estimated the likelihood P(K T) by using a prescriptive model that assumes normal distribution of touch points [13], which is justified by existing evidence [9]. The probability of a touch point belonging to a particular key is P K T = exp!!!!!!, (9) where d! is the Euclidean distance between the touch point and the center of the key and σ! is an estimate of the variance of the touch point distribution around that particular key s center. This parameter was estimated from training data of Step 2 that is disjoint from the evaluation (Step 5). Language Model The prior probability P(K) was estimated using a statistical language model trained on a large corpus. Our characterbased n-gram model estimated the probability of the next key based on up to the previous six characters of context: J! U! K! A! L! Q! Letter Hand D (px) t elapsed (ms) MT (ms) S L O R U R N L D L S L _ R G R O R O R D L Table 1. Predicted typing performance with KALQ. P K = P K C P K C!!!!!! ), (10) where C is all previously written text and C!!!!!! are the last six characters written. We trained our model on a sample of 778M messages sent via Twitter (12/2010 6/2012). Duplicate tweets, retweets, and non-english-language tweets were eliminated via a language-identification module [18, 19] (with a CI of 95%). We included only tweets written on mobile devices as judged from a tweet s source string. We split each tweet into one or more sentences and kept only sentences wherein all words (after removal of punctuation such as commas) were in a list of 330K English words. The latter word list was obtained by concatenation of a number of humanedited dictionaries (Wiktionary, Webster s dictionary, the CMU pronouncing dictionary, and GNU aspell). After filtering, the training data consisted of 94.6M sentences, 626M words, and 2.56G characters. Our language model used a vocabulary of the letters A Z plus space, apostrophe, comma, period, exclamation point, and question mark. Using the SRILM toolkit, we trained a 7-gram language model, using Witten Bell smoothing and no count cutoffs. In response to resource constraints of our mobile device, we entropy-pruned the model to reduce its size. Our final model had 1.4M parameters (all n-gram probabilities plus backoff weights) and a compressed disk size of 9 MB. We tested the predictive power of the model by using a set of messages written on Blackberry mobile devices [12]. We measured language-model performance in terms of average per-letter perplexity. The perplexity indicates the average number of choices the model thinks are possible next, given the previous context. The perplexity of the Mobile sentences in our model was Despite its small size, the model performed well even when compared to an unpruned 10-gram model with 340M parameters. This large model only reduced the test set's perplexity to STEP 5: TRAINING AND EVALUATION Empirical evaluation is preferable to model-based predictions in the case of novel layouts, because predictions have turned out to be higher than the empirically achieved rates (e.g., compare the prediction in [32] to empirical rates reported in [33]). Overestimation may arise from the fact that Fitts -law-based models disregard factors that affect typing

6 performance. However, for validation of a novel keyboard design, a compromise between sample size and the length of training must be sought. We preferred securing sufficient time for learning new motor programs over a large N. Training Program and Performance Assessment To minimize the training time and to maximize eventual performance, we developed a systematic training program. Our training program builds on existing work: teaching key locations [29], practicing frequent bigrams and distributing practice over time [33], and rewarding high performance monetarily [4]. Cold turkey evaluation, wherein users type randomly selected phrases with no special practice, may not allow time for performance to approach a model s predicted performance. The program consists of one-hour sessions structured in the manner Table 2 describes. The baseline performance level is assessed first with QWERTY, without practice and instruction in typing. The first training sessions with KALQ focus on learning the grip, spacebar use, and the hover-over technique. We provide instructions in each and monitor performance. Key locations are practiced by typing the alphabet without seeing the key labels. After learning these basics, the trainees enter sessions with the most common English bigrams and words. In the subsequent full-sentence practice, they type randomly chosen sentences of increasing length from the Mobile corpus [12,29]. From that point on, we set performance goals and give feedback on typing rate. We also include a special session that focuses on bigrams or words that had been slower than average for the user. After performance starts to stabilize, toward the 12th session, we introduce error-correction exercises, asking users to speed up and ignore errors. Participants Six right-handed students (3 M, 3 F) were recruited from Saarland university (M 25 years, SD 3.52). They reported having almost no experience with large touchscreen devices such as tablets, and only one was a touch-typist on physical QWERTY keyboards. The participants were compensated at 10 /hour, and the two best were given a bonus of 100. Session Contents Test Goal 0 QWERTY typing test I Baseline measurement 1 Grip, idle thumb tech- nique, spacebar policy Introduce KALQ, confirm understanding of the basics 1-3 The alphabet and frequent words Type the alphabet without seeing the key labels 3-8 Frequent bigrams and words II, III Learn motor techniques for frequent text, speed up 9-13 Full sentences, frequent IV Speed up gradually bigrams and words As above but extra prac- tice with error correction Speed up while keeping error rate under 5% Final Final evaluation Va, Vb Personal best with KALQ Table 2. Our program for training and assessing typing performance with KALQ. wpm QWERTY KALQ Test day Figure 9. Development of text entry speed throug the training program. The vertical bars denote 95% CIs. Measurement of Typing Performance Six tests were carried out throughout the program (see Table 2): the first for QWERTY and the rest to track improvement in KALQ. Our QWERTY setup was a full-width threerows-plus-spacebar keyboard identical to the built-in keyboard of the tablet (button sizes larger than in KALQ). Because using the same phrase sets repeatedly overestimates entry rates [33], we used separate phrases sets for training vs. testing. Our phrase set is a subset of the Mobile corpus with verified memorable sentences: 200 phrases, 1,073 words, and 5,253 characters [12]. The training phrase sets had 1,147 unique sentences after the removal of these sentences. Presentation order was randomized. All tests included a 15-minute warm-up session. Users final performance was tested with and without error correction (tests Va and Vb in Table 2). Apparatus and Materials We used a Samsung Galaxy Tab 7.7", which has a slightly larger and more responsive touch sensor than the tablet of our previous studies. Feedback on error rate and wpm was given after each phrase. In exercises but not in testing, a REDO button appeared if the error rate exceeded 5%. During typing, a black asterisk * was presented for correct taps and a red one * for incorrect taps. With error correction, coloring was turned off to improve the latency of feedback. Results We calculate wpm with words of five characters. Error rate is character error rate (CER), calculated by using the Damerau-Levenhstein distance. Typing Performance The users baseline performance with the full-width QWERTY layout was 27.7 wpm (9.0% CER). Figure 9 shows the development of typing performance over the course of the program. In the final test, after, on average, 16.8 hours of training (min. 13 h, max. 19 h), the users reached 37.1 wpm (5.2% CER). The difference to QWERTY was significant (see Figure 9). We believe that in a task involving text generation, their performance would be even better. We noticed that in the transcription task, long sentences caused problems, because users often had to glance at the stimulus phrase and they lost the position in the text because only asterisks were pro-

7 vided as resumption cues. Therefore, we examined the distribution of MT and divided the phrases into two bins by whether they contained taps longer than 900 ms. The latter, in our experience, correlates well with the glancing behavior. The average typing speed for non-glance phrases was indeed slightly higher: 40.2 wpm (4.0% CER). This result is considered tentative, because an eye-tracker was not used. Effect of Error Correction The users entry rate with error correction was about the same as without it: 36.7 wpm (6.4% CER). Disappointingly, the error rate was slightly higher with the online version of our error-correction technique than without it. We hypothesized that this is probably due to the chickenand-egg problem of no suitable training data from true KALQ expert users being available before the experiment was conducted. Therefore, once experimental data had been collected, we performed two offline experiments with the typing data. We used a single user typing 26 phrases as training data, a dataset disjoint from the testing data on which we ran the offline experiment. The typing data from this user were then used to re-estimate the error-correction model s touch-point parameters. We found that allowing the error-correction algorithm to learn touch-point regularities from even a single expert KALQ user was enough to result in a percentage point s reduction in CER, both for test data originally collected with online error correction and for data originally collected without it. We also investigated the impact of allowing the error-correction algorithm to leverage prior recognition context (the sets of likelihoods and priors for all previously typed keys) instead of having to rely on a character string as the sole prior context. In other words, the error-correction algorithm performs a search through all possible letter combinations over the entire prior context when it tries to identify the most likely text in view of the user s input, instead of just performing a point estimate for the last inputted key. Since a search over all letter sequences is infeasible, we used a pruning beam to speed up the search. We found that using prior recognition context further reduced the error rate, with a 1.3 percentage point reduction in CER, both for test data collected with online error correction and data collected without it. DISCUSSION This work has contributed to understanding how to design usable and effective keyboards for two-thumb text entry on mobile devices using touchscreens. We have presented a series of studies with the goal of improving text entry rates. With all design choices in play, trained users achieved an entry rate of 37 wpm (5% CER) an improvement of 34% over their naïve baseline performance with a standard touch-qwerty system. This rate represents an improvement of 19% over the best rate, of 31 wpm, reported in the previous literature [8]. However, the entry rates are not directly comparable due to differences in the samples and the training procedures. Nevertheless, given that our users were non-native speakers, we consider the result promising. More interestingly, the results can help future efforts by providing estimates of the gains attributable to different design and ergonomic choices: Grip: Grasping the tablet with its corner in the valley created by the thenar and hypothenar eminence yields ~4% faster tapping performance than does a random grip. Moreover, the associated keyboard layout (Figure 3) occludes the display the least. Hover-over technique: By analyzing an existing model of two-thumb typing, we proposed a thumb coordination technique wherein the idle thumb is approaching its next target and hovers over it to minimize travel distance. Based on Figure 5, we estimate that this typing strategy saves about 10 20% on MT in alternating taps. Optimization of letter assignment: KALQ was optimized computationally from a model of best-performance twothumb typing. As a result, it maximizes alternating taps and minimizes same-side travel distances. Our model predicted a benefit of only 4% over a comparable quasi- QWERTY layout. However, this prediction was made assuming the same typing technique and grip. Error correction: We developed an error-correction technique that adapts well-known techniques to the unique motor and linguistic aspects of two-thumb typing. Although our users error rates were not improved by the online version of our corrector, offline analyses showed that with better parameters, the error rate can be decreased by 1.3 percentage points. The design of KALQ is readily usable. The layout has space to accommodate more buttons without breaking the grip. Backspace, shift, punctuation, and special characters can be placed in the empty slots on KALQ s right-hand side. To tune the keyboard to the hand dimensions of the user, it could be scaled, with calibration asking the user to perform the sweep gesture shown in Figure 3, and left-handed users could select a version wherein the left and right keygrids are swapped. However, because the associated improvement due to the optimization layout is small, not many users may want to learn KALQ. Our results suggest that tangible improvements can be achieved also for QWERTY simply by changing the grip and learning the hover-over technique. However, we hypothesize that, because of the smaller travel distances, KALQ is more ergonomic when used intensively. We foresee several opportunities to reach even higher typing rates. To improve letter-to-key assignment, other factors affecting two-thumb typing should be incorporated, such as the angle of approach, occlusion by the thumb s tip, and the absolute screen location of keys. To improve the design for goals other than performance, especially learning time, multi-objective optimization could be used [7]. Error correction can be improved through training of the movement model with real-world user data. We have studied only one grip, which is probably contingent on properties unique to our sample. Future research should address other grips and find designs that work with the distribution of grips that

8 users normally exhibit. Finally, our sampling has been limited to right-handed male students and a 7" tablet. Future research needs to examine handedness and the different hand sizes and form factors. Larger form factors are likely to exhibit phenomena similar to those reported here, but smaller form factors will face novel issues, such as that the thumbs active regions will overlap [5]. ACKNOWLEDGEMENTS The code for optimization, predictive models, the keyboard, and empirical data are released on our project homepage. This work was supported by the Max Planck Center for Visual Computing and Communication (MPC-VCC), EPSRC (grant number EP/H027408/1), and the Scottish Informatics and Computer Science Alliance. REFERENCES 1. Altman, E.M., and Trafton, J.G. Memory for goals: An activation-based model. Cognitive Science 26, 1 (2002), Bao, J. Pierce, S. Whittaker, and S. Zhai. Smart phone use by non-mobile business users. Proc. MobileHCI 11, ACM Press (2011). 3. Castellucci, S.J., and MacKenzie, I.S. Gathering text entry metrics on Android devices. Ext. Abst. CHI 11, ACM Press (2011), pp Clarkson, E., Clawson, J., Lyons, K., and Starner, T. An empirical study of typing rates on mini-qwerty keyboards. Ext. Abstr. CHI 05, ACM Press (2005), Clarkson, E., Lyons, K., Clawson, J., and Starner, T. Revisiting and validating a model of two-thumb text entry. Proc. CHI 07, ACM Press (2007), Clawson, J., Lyons, K., Rudnick, A., Iannucci Jr., R.A., and Starner, T. Automatic whiteout++: correcting mini-qwerty typing errors using keypress timing. Proc. CHI 08, ACM Press (2008), pp Dunlop, M., and Levine, J. Multidimensional pareto optimization of touchscreen keyboards for speed, familiarity and improved spell checking. Proc. CHI 12, ACM Press (2012), Goel, M., Findlater, L., and Wobbrock, J. Walktype: Using accelerometer data to accommodate situational impairments in mobile touch screen text entry. Proc. CHI 12, ACM Press (2012). 9. 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