Summary:
This paper primarily talks
about Word-Gesture Keyboard, a creative HCI method through keyboard. It is on
average considered more preferred, more fun, and less physically but more
visually demanding. It is designed with following three aims in mind:
(1)
Fast input speed
(2)
Minimal
recognition load on new users
(3)
Improved
efficiency with users getting more familiar with Word-Gesture Keyboard
Word-Gesture Keyboard will
allow user to write each and every word via a word gesture. Here, the “word” is
not limited in lexicon. It can be tokens defined by arbitrary strings of
characters, such as “Gmail”.
Word-Gesture Keyboard Feasibilities:
One big problem of
Word-Gesture Keyboard is most word gestures will run across letters that are
not part of the word intended. Fortunately, it can be solved with statistical
regularities of natural language, which indicates some character sequences are
more likely than others and most simply don’t exist as legitimate words. It
implies that all valid letter combinations will form a finite set that can be
captured in a language model. With this theory breakthrough, all possible words
can be represented geometrically on a given key-board layout as word gestures
and matched against users’ input gestures. Just as mentioned above, “word” here
has a more generalized meaning. It can be rare names, jargons, email addresses,
passwords, etc. We name this kinds of words Out of Vocabularies (OOV). In our
Word-Gesture Keyboard, OOV letter sequences can always be entered by typing the
individual letter keys. If these OOV sequences are frequently used then they
may be added to the system’s list of recognized words, either manually or
automatically. An addition to the solution is N-best suggestions. When “tip
top” conflict occurs, they can be addressed by manual selection from the
alternative N-best suggestions, or automatically according to word context.
Word-Gesture Keyboard Efficiency:
One Continuous Movement: In comparison to tapping-based touchscreen keyboards,
gesture keyboards do not require up and down movements for each letter.
Therefore, it is undoubtedly faster. The speed advantage of a single-stroke
word gesture input can also be understood in motor control modeling terms. Tapping
individual letters in a word can be viewed as a sequence of discrete target
pointing tasks, each can be modeled by Fitts’ law (showed below).
tk,k + 1 is the time duration from tapping the kth letter (key)
to the (k+ 1)th letter in the word; Dk,k + 1 is the
movement distance from the kth letter to the (k+ 1) letter; and Sis the size of
the target key. a and b are two constants of Fitts’ law. ID is called Fitts’
index of difficulty, measured in bits.
Experts’ conclusion is goal-crossing
task is faster than tapping on the same sized targets as long as ID is less
than 4 bits. Here, “goal” means a letter key needed in a word.
Auto word ending and
spacing: each time a user lifts the
finger from the touch surface, a word and a space are entered. Not having to
enter a space character after each word is another efficiency advantage of a
gesture keyboard.
Error-tolerance: Error tolerance allows the user to cut corners, to be
inaccurate but fast.
One finger operation: This is
the only aspect that Word-Gesture Keyword is not as good as two-handed typing.
This is particularly true when the keyboard layout is the conventional QWERTY
on which consecutive letters of a word tend to alternate between the left and
right side of the keyboard. With two handed-typing, when one hand strikes one
letter the other hand can, to some degree, move towards the next letter in
parallel.
Word-Gesture Keyboard Ease of Use:
First, typing on a keyboard
is a familiar text input method to most, if not all computer and smartphone
users.
Second, drawing or doodling
is a fun and easy action that even children enjoy doing.
Third, the user does not have
to have learned any gestures before using a word-gesture keyboard.
Word-Gesture Keyboard Ease VS Efficiency:
The two types of behavior are
two ends of a continuum. Our main behavioral theory of word shorthand gesture
key-boards is that their use automatically shifts from the ease end (visual
tracing) to the efficient end (recall gesturing).
Importantly, we do not expect
the users to gesture every word without looking at the keyboard. Due to the
Zipf’s law effect, a small number of words are used disproportionally
frequently and their stroke patterns are memorized early. Longer and less
common words are typically made of common fragments whose shapes can be quickly
remembered. An important word-gesture key-board property is that it does not
force the user into either “mode”. The user gradually progresses from the easy
end to the more efficient end in use. In this sense, a word-gesture keyboard is
a “progressive user interface.”
Word-Gesture Keyboard Gesture Recognitions:
where P(G|W) is the likelihood
of W’s word gesture matching a user’s input gesture G, and P(W) reflects the
system’s estimate of prior probability that the word W is the user’s intended
word. The denominator P(G) only depends on the user’s gesture and is invariant
during the search.
The search for the user’s
intended word is thus the product of two model estimates. The probability
P(G|W) reflects the gestural model and the probability P(W) reflects the language
model.
In order to estimate P(G|W),
we have used various techniques, such as dynamic time warping and template
matching, to compute gesture keyboarding shape similarities.
Word-Gesture Keyboard Two Novel Functions:
(1)
Command Strokes:
With our systems, the user may issue
commands (such as “Copy” and “Paste”) by tracing out the command names on the
keyboard starting from a designated key (e.g. a Cmd key). The system suggests
the command effect as soon as the command
stroke is unambiguous.
(2)
Case Key:
We introduced a new key on the keyboard,
the Case key (see the lower left corner of Figure 1). This key cycles through
the different word case alternatives for the word just entered or preceding the
text caret. The Case key uses dictionary information to intelligently support
nonstandard casing convention for some words, such as “iPhone”. Since the Case
key modifies the word preceding the current text
caret position (“reverse Polish”) it enables users to
perform case corrections after the word is entered and only when they are
actually needed.
Bibliography:
Zhai, Shumin, and Per Ola Kristensson. "The
Word-gesture Keyboard: Reimagining Keyboard Interaction." Communications of the ACM 55.9 (2012): 91-101. ACM Digital Library. Web. 23 Mar. 2013.
<http://dl.acm.org/citation.cfm?id=2330689>.
The blog content is created by urjnasw xkfjjkn (Xu Yan) on 23rd, March, 2013.
