Typing Tutor: Individualized Tutoring in Text Entry for Older Adults Based
on Input Stumble Detection
Older Adult Support
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Hagiya, Toshiyuki
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Horiuchi, Toshiharu
/
Yazaki, Tomonori
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.733-744
© Copyright 2016 ACM
Summary: Many older adults are interested in smartphones. However most of them
encounter difficulties in self-instruction and need support. Text entry, which
is essential for various applications, is one of the most difficult operations
to master. In this paper, we propose Typing Tutor, an individualized tutoring
system for text entry that detects input stumbles and provides instructions. By
conducting two user studies, we clarify the common difficulties that novice
older adults experience and how skill level is related to input stumbles. Based
on these studies, we develop Typing Tutor to support learning how to enter text
on a smartphone. A two-week evaluation experiment with novice older adults
(65+) showed that Typing Tutor was effective in improving their text entry
proficiency, especially in the initial stage of use.
Probabilistic touchscreen keyboard incorporating gaze point information
Gesture & text-entry
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Hagiya, Toshiyuki
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Kato, Tsuneo
Proceedings of 2014 Conference on Human-Computer Interaction with Mobile
Devices and Services
2014-09-23
p.329-333
© Copyright 2014 ACM
Summary: We propose a novel probabilistic keyboard that takes into account the
distance between a gaze point and a touch position in order to improve typing
efficiency. The proposed keyboard dynamically changes the size of the search
space for predicting candidate words based on a model that estimates the
magnitude of touch position errors according to the distance between the gaze
point and the touch position. This makes it possible for users to type intended
words even when they glance at different areas on the screen. Performance was
evaluated in terms of input accuracy in total error rate (TER) and of typing
speed in words per minute (WPM). The results showed that the proposed keyboard
successfully reduced the TER by 18.2% and increased WPM by 12.7% compared to
the conventional keyboard.
Adaptable probabilistic flick keyboard based on HMMs
Posters
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Hagiya, Toshiyuki
/
Kato, Tsuneo
Proceedings of the 2013 International Conference on Intelligent User
Interfaces
2013-03-19
v.2
p.71-72
© Copyright 2013 ACM
Summary: To provide an accurate and user-adaptable software keyboard for
touchscreens, we propose a probabilistic flick keyboard based on HMMs. This
keyboard can reduce the input error by taking the time series of the actual
touch position into consideration and by user adaptation. We evaluated
performance of the HMM-based flick keyboard and MLLR adaptation. Experimental
results showed that a user-dependent model reduced the error rate by 28.2%. In
a practical setting, MLLR user adaptation with only 10 words reduced the error
rate by 16.5% and increased typing speed by 10.5%.
