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Evolution of performance in terms of WPM (a) and KSPC (b) for all combinations of screen size and keyboard.
Source publication
The advent of wearables (e.g., smartwatches, smartglasses, and digital jewelry) anticipates the need for text entry methods on very small devices. We conduct fundamental research on this topic using 3 qwerty-based soft keyboards for 3 different screen sizes, motivated by the extensive training that users have with qwerty keyboards. In addition to Z...
Contexts in source publication
Context 1
... measures were averaged on a per-trials basis, so that there were 5 obser- vations or "data points" for each of the 9 tested conditions. As shown in Figure 6, participants performed consistently better while entering the fifth phrase in all conditions. In addition, we observed that performance improved as screen size increased. ...
Context 2
... short-term learning analysis suggests that the tested key- boards provide reasonable entry speeds on very small screens along with competitive accuracy. Interestingly, in terms of KSPC, both Callout and ZShift keyboards stabilized around 1.5 on all screen sizes, which means that transcription errors were systematically committed from time to time (Figure 6b). Meanwhile, ZoomBoard approached its innate value of 2.0 when entering the fifth phrase on all conditions. ...
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Citations
... For older adult participants, we found that T9 with enhanced key 1 led to 6.0% and 27.5% fastertyping speed over the conventional T9 and T9 with wiggle gesture respectively, but the difference was only significant between the enhanced key 1 and wiggle gesture. In contrast, the speed advantage of T9 with enhanced key 1 was more apparent for young adults since it was significantly faster than both the conventional T9 by 28 [31]. It is important to note that the aforementioned keyboards all employed a QWERTY layout with 26 keys while our study utilized the 9-key layout. ...
... Even the optimized-T9 keyboard proposed by previous researchers resulted in a slower typing speed than the conventional QWERTY keyboard [49]. Furthermore, prior work added novel features (e.g., statistical decoding, error correction, space key omission, and iterative zooming) with the goal of achieving the best typing performance [21,31,32,44,49,59]. In contrast, our goal was not to design the fastest keyboards by rearranging layouts or introducing advanced features but to leverage older adults' familiarity with the T9 keyboard toward a smoother typing experience. ...
... After informing them that page scaling was disabled to simulate the size of a smartwatch, they mentioned that the keys were still too small relative to their fingertips. Thus, future designers should maximize the keyboard space on smartwatches by removing any interface elements that are not vital to the typing task , as well as using zooming or callout techniques to make the keys more visible [31]. Another possible way to alleviate this issue is to use a stylus which has a smaller tip to enter letters, but this requires the use of additional equipment and may be inconvenient for a smartwatch. ...
Older adults increasingly adopt small-screen devices, but limited motor dexterity hinders their ability to type effectively. While a 9-key (T9) keyboard allocates larger space to each key, it is shared by multiple consecutive letters. Consequently, users must interrupt their gestures when typing consecutive letters, leading to inefficiencies and poor user experience. Thus, we proposed a novel keyboard that leverages the currently unused key 1 to duplicate letters from the previous key, allowing the entry of consecutive letters without interruptions. A user study with 12 older adults showed that it significantly outperformed the T9 with wiggle gesture in typing speed, KSPC, insertion errors, and deletes per word while achieving comparable performance as the conventional T9. Repeating the typing tasks with 12 young adults found that the advantages of the novel T9 were consistent or enhanced. We also provide error analysis and design considerations for improving gesture typing on T9 for older adults.
... Apple Watch detects only the absence and presence of extra force. It is relatively common to use larger devices to study interactions with smartwatches due to technological limitations of current smartwatches [10,18,23,26,29]. Relevantly, a prior work reported that text entry performances of a keyboard on an actual smartwatch and a simulated smartwatch on a smartphone were comparable in terms of speed and accuracy [37]. ...
... One limitation of the work is the studies reported here were conducted on simulated smartwatch interfaces on a smartphone. While this is fairly common in the literature [10,18,23,26,29], due to the absence of empirical evidence, it is unclear if the performance recorded on a simulated smartwatch is generalizable to actual smartwatches. In the future, we will explore dierent control-display mapping functions for force input. ...
Selecting small targets is difficult on tiny displays due to the “fat finger problem”. In this paper, we explore the possibility of using a force-based approach to target selection on smartwatches. First, we identify the most comfortable range of force on smartwatches. We then conduct a 1D Fitts’ law study to compare the performance of tap and force-tap. Results revealed that force-tap is significantly better in selecting smaller targets, while tap outperforms force-tap for bigger targets. We then developed two new force-based keyboards to demonstrate the feasibility of force input in practical scenarios. These single-row alphabetical keyboards enable character-level text entry by performing slides and variating contact force. In a user study, these keyboards yielded about 4 wpm with about 2% error rate, demonstrating the viability of force input on smaller screens.
... We optimized all methods for the simulated smartwatch for a fair comparison between them. It is relatively common to use smartphones or tablets to study interactions with smartwatches due to technological limitations of current smartwatches (e.g., [18,46,57]). ...
... Although it is relatively common to use smartphones or tablets to study interactions with smartwatches because of the technological limitations of current smartwatches (e.g., [18,46,57]), due to the absence of empirical evidence, it is unclear whether the performance recorded on a simulated smartwatch is generalizable to actual smartwatches. Hence, to increase the external validity of the work, we replicated not only the interface but also the holding position and posture of a smartwatch (Fig. 6). ...
Picking numbers is arguably the most frequently performed input task on smartwatches. This paper presents three new methods for picking numbers on smartwatches by performing directional swipes, twisting the wrist, and varying contact force on the screen. Unlike the default number picker, the proposed methods enable users to actively switch between slow-and-steady and fast-and-continuous increments and decrements during the input process. We evaluated these methods in two user studies. The first compared the new methods with the default input stepper method in both stationary and mobile settings. The second compared them for individual numeric values and values embedded in text. In both studies, swipe yielded a significantly faster input rate. Participants also found the method faster, more accurate, and the least mentally and physically demanding compared to the other methods. Accuracy rates were comparable between the methods.
... Many previous works have been done to find a way to touch the out-of-reach region. When it refers to the touchscreen, some methods provide users an imaginary zoom in or zoom out glass, the users can touch the desired positions within the glass [15], which is more applicable from tiny to large screens, since when the users shrink a large screen to a touchable size, the keys could be too small to touch accurately. For other components of smartphones, such as Back-of-Device (BoD) touch panels or other sensors, their reachability and usability have been studied previously [16], [17]. ...
div>Many questions regarding single-hand text entry on modern smartphones (in particular, large-screen smartphones) remain under-explored, such as, (i) will the existing prevailing single-handed keyboards fit for large-screen smartphone users? and (ii) will individual-customization improve single-handed keyboard performance? In this paper we study single-handed typing behaviors on several representative keyboards on large-screen mobile devices. We found that, (i) the user-adaptive-shape curved keyboard performs best among all the studied keyboards; (ii) users' familiarity with the Qwerty layout plays a significant role at the beginning, but after several sessions of training, the user-adaptive curved keyboard can have the best learning curve and performs best; (iii) generally the statistical decoding algorithms via spatial and language models can well handle the input noise from single-handed typing.</div
... Many previous works have been done to find a way to touch the out-of-reach region. When it refers to the touchscreen, some methods provide users an imaginary zoom in or zoom out glass, the users can touch the desired positions within the glass [15], which is more applicable from tiny to large screens, since when the users shrink a large screen to a touchable size, the keys could be too small to touch accurately. For other components of smartphones, such as Back-of-Device (BoD) touch panels or other sensors, their reachability and usability have been studied previously [16], [17]. ...
div>Many questions regarding single-hand text entry on modern smartphones (in particular, large-screen smartphones) remain under-explored, such as, (i) will the existing prevailing single-handed keyboards fit for large-screen smartphone users? and (ii) will individual-customization improve single-handed keyboard performance? In this paper we study single-handed typing behaviors on several representative keyboards on large-screen mobile devices. We found that, (i) the user-adaptive-shape curved keyboard performs best among all the studied keyboards; (ii) users' familiarity with the Qwerty layout plays a significant role at the beginning, but after several sessions of training, the user-adaptive curved keyboard can have the best learning curve and performs best; (iii) generally the statistical decoding algorithms via spatial and language models can well handle the input noise from single-handed typing.</div
... Second, indirect selection can be another option. Zshift [5] uses a callout-based shift-pointing technique, and DriftBoard [6] uses a panning-based technique that utilizes a fixed cursor point with a movable QWERTY layout. Backof-device [19] and tilt-based gesture [20] can be other options if additional input channels are available. ...
Typing on a smartwatch is challenging because of the fat-finger problem. Rising to the challenge, we present a soft keyboard for ultrasmall touch screen devices with efficient visual feedback integrated with autocorrection and prediction techniques. After exploring the design space to support efficient typing on smartwatches, we designed a novel and space-saving text entry interface based on an in situ decoder and prediction function that can run in real-time on a smartwatch such as LG Watch Style. We outlined the details implemented through performance optimization techniques and released interface code, APIs, and libraries as open source. We examined the design decisions with the simulations and studied the visual feedback methods in terms of performance and user preferences. The experiment showed that users could type more accurately and quickly on the target device with our best-performing visual feedback design and implementation. The simulation result showed that the single word suggestion could yield a sufficiently high hit ratio using the optimized word suggestion algorithm.
... Four papers were excluded as they did not measure any typing performance (e.g., stress levels measured by a pressure-sensitive keyboard). The following 21 papers are included in the meta-analysis (ordered by year): 2009 [24], 2010 [1,20], 2011 [13], 2012 [8,12,14], 2013 [33], 2014 [17,30,43], 2015 [25,35,38], 2016 [11,15], 2017 [32,46,47], 2018 [22,48]. ...
While designing an HCI experiment, planning the sample size with a priori power analysis is often skipped due to the lack of reference effect sizes. On the one hand, it can lead to a false-negative result, missing the effect that is present in the population. On the other hand, it poses a risk of spending more resources if the number of participants is too high. In this work, I present the reference for small, medium, and large effect sizes for typing experiments based on a meta-analysis of well-cited papers from CHI conference. This effect size ruler can be used to conduct a priori power analysis or assess the magnitude of the found effect. This work also includes comparisons to other fields and conclude with a discussion of the existing issues with reporting practices and data availability. This paper and all data and materials are freely available at https://osf.io/nqzpr.
... Much of existing research focuses on addressing the problem of small input space. Several techniques propose a two-step selection process, where users frst select a subset of characters and then the exact character [12,23,27,39]. Some techniques use other mechanics to select the exact key from a subset, e.g., based on fnger detection [21], force [24], or swipe gestures [54]. ...
There are many situations where using personal devices is not socially acceptable, or where nearby people present a privacy risk. For these situations, we explore the concept of hidden interaction techniques through two prototype applications. HiddenHaptics allows users to receive information through vibrotactile cues on a smartphone, and HideWrite allows users to write text messages by drawing on a dimmed smartwatch screen. We conducted three user studies to investigate whether, and how, these techniques can be used without being exposed. Our primary findings are (1) users can effectively hide their interactions while attending to a social situation, (2) users seek to interact when another person is speaking , and they also tend to hide the interaction using their body or furniture, and (3) users can sufficiently focus on the social situation despite their interaction, whereas non-users feel that observing the user hinders their ability to focus on the social activity.
... Shift, as an input technique, was designed such that content directly under the fingertip moves, or shifts away for better legibility [53]. Shift is highly effective in a number of settings, including on smartwatches [28,46], but also in comparison to techniques such as ThumbSpace [23], TapTap or MagStick [43]. ...
... Thus, 2-step techniques do not afford continuous graph exploration, a highlight of our technique. 2) Shift was specifically designed for finger occlusion, and has shown promise on smartwatches [28,46]. ...
... The idea of using the key combination to enter a single character, presented in this study, can also be used for very small devices, such as smartwatches. The topic of small devices keyboards is the subject of many studies (e.g., [35][36][37][38]) and this topic still very current. ...
The development of technology brings the computerization of everyday life. The most common device used to communicate with a computer is the keyboard. Many papers have been devoted to the study of computer keyboards and virtual keyboards. The authors of this study propose a new concept of a single line keyboard named One Row Keyboard (ORK) which can be applied for both computer and virtual keyboards. Applying the proposed concept enables the number of keys on the keyboards to be reduced significantly. This allows for the development of keyboards even on really small mobile devices. The article also presents a new method of assigning characters to keys for the ORK and shows the use of this method to create exemplary ORK layouts. One Row Keyboard is also the first concept that makes it possible to create a fully alternating keyboard. Noteworthy is that the criterion of maximizing typing alternation is the most popular criterion for keyboard optimization used in the studies devoted to computer keyboards as it leads to an increase in typing speed and decrease in the number of errors.
