Is it too small?: Investigating the performances and preferences of users when typing on tiny QWERTY keyboards. Issue 106 (October 2017) - Yi, Xin; Yu, Chun; Shi, Weinan; Shi, Yuanchun
Highlights: The simple Bayesian algorithm works well for text entry on tiny keyboards. Users can type on a keyboard as small as 2 cm at 26.8 words per minute. The 3.5 cm keyboard yields the highest speed of 33.6 words per minute. The most acceptable size of keyboards on smart watches is between 3 and 4 cm. Keyboard size affects typing performance, finger posture and user preference. Abstract: Typing on tiny QWERTY keyboards on smartwatches is considered challenging or even impractical due to the limited screen space. In this paper, we describe three user studies undertaken to investigate users' typing abilities and preferences on tiny QWERTY keyboards. The first two studies, using a smartphone as a substitute for a smartwatch, tested five different keyboard sizes (2, 2.5, 3, 3.5 and 4 cm). Study 1 collected typing data from participants using keyboards and given asterisk feedback. We analyzed both the distribution of touch points (e.g., the systematic offset and shape of the distribution) and the effect of keyboard size. Study 2 adopted a Bayesian algorithm based on a touch model derived from Study 1 and a unigram word language model to perform input prediction. We found that on the smart keyboard, participants could type between 26.8 and 33.6 words per minute (WPM) across the five keyboard sizes with an uncorrected character error rate ranging from 0.4% to 1.9%. Participants' subjective feedback indicated that they felt most comfortable with keyboards larger than 2.5 cm. Study 3 replicated the 3.0 and 3.5 cm keyboard tests on a real smartwatch and verified that in terms of text entry speed, error rate and user preference, there was no significant difference between the results measured on a smartphone and that on a smartwatch with same sized keys. This study result indicated that the results of Study 1 and 2 are applicable to smartwatch devices. Finally, we conducted a simulation to investigate the performance of different touch/language models based on our collected data. The results showed that using either a bigram language model or a detailed touch model can effectively correct imprecision in users' input. Our results suggest that achieving satisfactory levels of text input on tiny QWERTY keyboards is possible.