“Those
Look Similar!” Issues in Automating Gesture Design Advice
This
paper primarily talks about the concept of advising interface designers unsolicitedly
on how to make their new input gestures less similar with existing ones if they
are perceived to be similar by machines. The authors developed a tool, called Quill,
to realize the concept. With the help of quill, it will be easier for interface
designers to generate excellent gesture sets and incorporate gesture
recognition into the interface they want.
In
the second section, the paper introduces the satisfactory experimental outcome
for Quill. It turns out, when inputting a pair of non-similar gestures, Quill
will perceive them to be non-similar with an accuracy of 99.8%. Although
sometimes a pair of similar gestures may be perceived as non-similar (about
22.4%), the overall accuracy of 87.7% is still acceptable.
In
the third section, the paper introduces 10 to 15 gesture examples of each gesture
class are needed to train Quill gesture recognizer. The gesture classes will be
organized into gesture groups. Quill uses similarity metrics to predict whether
people will perceive two gestures to be similar.
Figure 1.Training:10-15 gesture examples for each
gesture class
Figure 2.The new drawn gesture is perceived to be
similar to the copy class
The
last section of the paper is about three advice-related UI challenges,
implementation challenges and a similar metric challenge.
- Advice-related challenges:
---Advice Time:
The
drawbacks of early advice time: Distract users; Advice may become stale as user
works.
Quill
gives advice when designer begins to test a gesture. Testing is a sign that designer
has already completed entering a new class.
---How much advice:
Quill
shows a concise message initially. It is a hyperlink and can be opened for
detailed information.
---What advice:
English
prose supplemented with drawings.
- Implementation challenges:
---Background Analysis:
For
user-initiated analyses, Quill disables all user actions that would change any
state during advice computation.
For
system-initiated analyses, Quill allows any action, but if a change happens
that affects analysis, analysis will be canceled. After that, canceled analyses
will be automatically restarted.
---Advice for hierarchies:
In
quill, all notices(i.e., pieces of advice) that apply to an object are stored in
a list property of the object.
- Similarity metric challenges:
---The
models Quill uses to predict human-perceived similarity are not perfect, and
participants rightly disagreed with it at times. The model seemed especially
prone to overestimate similarity.
Bibliography:
Long A C, Landay J A, Rowe L A. “Those Look Similar!” Issues in Automating Gesture Design Advice[D]. Orlando:Carnegie Mellon University,University of California at Berkeley, 2001.
