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# Enabling Input on Tiny/Headless Systems Using Morse Code

Anna-Maria Gueorguieva  Human-Computer Interaction GroupUniversity of
California, Merced5200 N. Lake RoadCaliforniaMercedUSA
<EMAIL_ADDRESS>,  Gulnar Rakhmetulla  Human-Computer
Interaction GroupUniversity of California, Merced5200 N. Lake
RoadCaliforniaMercedUSA<EMAIL_ADDRESS>and  Ahmed Sabbir Arif
Human-Computer Interaction GroupUniversity of California, Merced5200 N. Lake
RoadCaliforniaMercedUSA<EMAIL_ADDRESS>
(2018)

###### Abstract.

This paper presents results of a pilot study that explored the potential of
Morse code as a method for text entry on mobile devices. In the study,
participants without prior experience with Morse code reached 6.7 wpm with a
Morse code keyboard in three short sessions. Learning was observed both in
terms of text entry speed and accuracy, which suggests that the overall
performance of the keyboard is likely to improve with practice.

Morse code, accessibility, text entry, smartphone, virtual keyboard.

††copyright: rightsretained††conference: Poster at the 2nd Annual Center for
Cellular and Biomolecular Machines Open House; October 22, 2018; University of
California, Merced, USA

##  1\. Introduction

Entering text on devices with tiny or no displays (referred to as headless
devices) remains a challenge. Conventional virtual keyboards are ineffective
on devices with tiny displays, like smartwatches or smart thermostats, since
the keys of these keyboards are too small for precise selection (Arif and
Mazalek, 2016). Entering text on a headless devices, such as a digital
tangible in a tangible-tabletop system, is even more challenging due to the
difficulties in verifying or correcting an input without visual feedback on a
display. This work investigates the potential of Morse code as a method for
text entry on these devices. Morse code encodes characters as standardized
sequences of dots (.) and dashes ($-$) (Fig. 1). Since it was originally
designed for telegraphs, a device without a display (Snodgrass and Camp,
1922), we hypothesize that it can enable users to enter text on both tiny and
headless devices. In this work, however, we focus only on the learning of the
code.

Figure 1. Morse code for the letters and numerals in the English language
(Snodgrass and Camp, 1922).

##  2\. Related Work

Not much work has investigated the potential of Morse code as an input method
on computer systems. The earliest work in this area studied the effectiveness
of Morse code as an input method with a Z-80 based microcomputer (Levine et
al., 1986). In the study, six participants yielded a 7.9 wpm entry speed after
two months of training (2–3 sessions per week). A different work investigated
if Morse code can be learned through passive haptic learning (PHL) using the
bone conduction transducer on a Google Glass (Seim et al., 2016). In their
study, participants reached an 8 wpm entry speed after four hours of exposure
to passive stimuli. A follow up study demonstrated PHL of Morse code on a
smartwatch (Seim et al., 2018). Table 1 summarizes the findings of these
studies.

Several works have also explored the potential of Morse code with people with
physical disabilities (Koester and Arthanat, 2018) and with various channels,
including eye-blink (Mukherjee and Chatterjee, 2015), foot stomping (Pedrosa
and Pimentel, 2014), and tongue gestures (Sapaico and Sato, 2011). Some have
used language models to improve text entry with Morse code (Tanaka-Ishii and
Frank, 2005). These, however, are outside the scope of this work.

Table 1. Text entry speed (wpm) with Morse code reported in the literature. Here, “PHL” refers to passive haptic learning and “$N$” represents sample size. Reference | $N$ | Session | Support | Speed
---|---|---|---|---
Levine et al. (1986) | 6 | 26–40 | Cheat Sheet | 7.9
Seim et al. (2016) | 12 | 4 | PHL | 8.0

##  3\. Morse Code Keyboard

We developed a simple virtual keyboard based on Morse code. It enables users
to enter characters using sequences of dots (.) and dashes ($-$). The keyboard
has dedicated keys for dot, dash, backspace, and space (Fig. 2). To enter the
letter “R”, represented by “$.-.$” in Morse code, the user presses the
respective keys in that exact sequence, followed by the SEND key, which
terminates the input sequence. The user presses the NEXT key to terminate the
current phrase and progress to the next one. The keyboard does not use a
predictive system, thus does not auto-complete words, auto-correct incorrect
words, or suggest the next probable words.

Figure 2. The device and keyboard used in the pilot study.

##  4\. Pilot Study

We conducted a pilot study to investigate the learning of Morse code and the
potential of Morse code as a method for text entry.

###  4.1. Apparatus

We used a Motorola Moto G5 Plus smartphone (150.2$\times$74$\times$7.7 mm, 155
g) at 1080$\times$1920 pixels (Fig. 2). The Morse code keyboard was developed
with the Android Studio 3.1, SDK 27. It automatically calculated all
performance metrics and recorded all interactions with timestamps.

###  4.2. Participants

Two participants took part in the pilot study. Both of them were 25 years old.
One of them was female and the other was male. They used both hands to hold
the device and the thumbs to type (Fig. 3). They were proficient in the
English language. Both were experienced smartphone users (9 years’ of
experience) but had no prior experience with Morse code.

Figure 3. A volunteer transcribing text with the Morse code keyboard with the
aid of a cheat sheet.

###  4.3. Design

We used a within-subjects design, where the independent variable was session
and the dependent variables were the commonly used words per minute (wpm) and
error rate (ER) performance metrics (Arif and Stuerzlinger, 2009). In summary,
the design was as follows:

  * 2 participants $\times$

  * 3 sessions (different days) $\times$

  * 10 pangram entries = 60 entries, in total.

###  4.4. Procedure

The study was conducted in a quiet room. On the first day, we explained the
research to the participants, demonstrated Morse code and the custom keyboard,
then enabled them to practice with the keyboard by entering free-form text for
about five minutes. The first session started after that, where participants
were instructed to enter the pangram “the quick brown fox jumps over the lazy
dog” as fast and accurate as possible, then tap the NEXT key to submit the
phrase. This was repeated for ten times. Participants could use a cheat sheet
to look-up the code for a character (Fig. 3). Error correction was disabled
during the study. We instructed the participants to ignore all errors. Logging
started from the first tap on the display and ended when participants pressed
NEXT. The following sessions followed the same procedure, except for the
demonstration and practice. The sessions were scheduled on consecutive days.

##  5\. Results

We only report descriptive statistics due to the small sample size ($N=2$) of
the study.

###  5.1. Entry Speed

Participants yielded an average of 5.9 wpm (SD $=1.1$). The average entry
speed in the three sessions were 5.0 wpm (SD $=0.6$), 5.9 wpm (SD $=1.2$), and
6.7 wpm (SD $=0.4$), respectively (Fig. 4).

Figure 4. Average entry speed (wpm) in the three sessions fitted to a power
trendline.

###  5.2. Error Rate

The average error rate in the study was 1.7% (SD $=2.6$). The average error
rate in the three sessions were 3.4% (SD $=3.0$), 0.8% (SD $=1.2$), and 1.0%
(SD $=2.5$), respectively (Fig. 5).

Figure 5. Average error rate (%) in the three sessions fitted to a power
trendline.

##  6\. Discussion

Participants yielded a competitive entry speed with the Morse code keyboard
(Table 1), reaching an average of 6.7 wpm by the final session. It is
inspiring that learning occurred even in such brief sessions of the study.
Entry speed improved by 16% in the second session and by 12% in the third
session compared to the preceding sessions. The average entry speed over
session correlated well ($R^{2}=0.9965$) with the power law of practice
(Snoddy, 1926). This suggests that users are likely to get much faster with
the method with practice. Participants made more errors in the first session
(3.4%), which reduced by 76% in the second session. The error rate in the
second and the final sessions were comparable (about 1%). Innately, the
average error rate over session moderately correlated ($R^{2}=0.7158$) with
the power law of practice (Snoddy, 1926). This suggests, unlike entry speed,
error rate is unlikely to reduce substantially with practice. These findings
indicate towards the possibility that Morse code could enable text entry on
devices where using conventional input methods is impractical. Although it was
not the focus of this work, Morse code could also enable people with various
motor disabilities to enter text on computer systems.

##  7\. Limitations and Future Work

There are several limitations of the work, which we will address in future
studies. First, we evaluated the method on a smartphone where users could see
their input. This visual feedback most likely improved their performance. It
is unknown whether users will yield a similar performance with devices without
a display. Second, we did not evaluate error correction in the study. Further
investigation is needed to identify an effective method for identifying and
correcting errors on devices without a display. Relevantly, studies showed
that error correction is an integral part of text entry and difficulties in
correcting errors significantly compromises users’ experience with a method
(Arif and Stuerzlinger, 2010). Third, the custom keyboard includes dedicated
keys for dots (.) and dashes ($-$), which may not be possible on all devices.
Hence, investigation is needed to identify the best modes of interaction on
devices that do not have physical or virtual keys. Some possibilities are
using taps and long-taps, taps and hard-taps (de Jong et al., 2010; Heo and
Lee, 2011; Arif et al., 2014; Zeleznik et al., 2001), or tap and flick
(Baglioni et al., 2011) in place of dots and dashes. Finally, the study had a
very small sample size ($N=2$). Replicating the study with a larger and more
diverse sample will improve the generalizability of the findings.

##  8\. Conclusion

We conducted a pilot study to investigate the potential of Morse code as a
method for text entry on mobile devices. For this, we developed a simple Morse
code keyboard. In the study, participants without prior experience with Morse
code reached 6.7 wpm with the keyboard in three short sessions. Learning was
observed both in terms of entry speed and accuracy, suggesting that the
performance of the keyboard is likely to improve with practice.

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