Reporting and Visualizing Fitts's Law: Dataset, Tools and Methodologies
Late-Breaking Works: Novel Interactions
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Jude, Alvin
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Guinness, Darren
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Poor, G. Michael
Extended Abstracts of the ACM CHI'16 Conference on Human Factors in
Computing Systems
2016-05-07
v.2
p.2519-2525
© Copyright 2016 ACM
Summary: In this paper we compare methods of reporting and visualizing Fitts
regressions. We show that reporting this metric using mean movement time per
user over accuracy-adjusted Index of Difficulty (IDe) produces more descriptive
visualization. This method displays variance, which is more useful in
understanding the interfaces, than an aggregated means-of-means approach using
Index of Difficulty. We demonstrate that there is little difference in slope
and intercept between the two methods, but has the potential to uncover wider
goodness-of-fit coefficients which could allow for better comparison across
experiments. We propose the use of quantile regression to report central
tendencies as a trend, rather than box plots. The tools released with this
paper can be used with any pointing device evaluation done with the FittsStudy
program. The dataset released with this paper contains almost 25,000 samples,
which can be used in future research for reporting or visualizing Fitts
regressions.
An evaluation of touchless hand gestural interaction for pointing tasks with
preferred and non-preferred hands
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Jude, Alvin
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Poor, G. Michael
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Guinness, Darren
Proceedings of the 8th Nordic Conference on Human-Computer Interaction
2014-10-26
p.668-676
© Copyright 2014 ACM
Summary: Performance evaluations of touchless gestural interaction are generally done
by benchmarking pointing performance against existing interactive devices,
requiring the use of user's preferred hand. However, as there is no reason for
this interaction to be limited to only one hand, evaluation should rightfully
consider both hands. In this paper we evaluate the performance of touchless
gestural interaction for pointer manipulation with both the preferred and
non-preferred hands. This interaction is benchmarked against the mouse and the
touchpad with a multidirectional task. We compared the performance between all
devices, improvement in performance between 2 rounds, and the degradation of
performance between hands. The results show the mouse has no performance
increase between rounds but high degradation across hands, the touchpad has
medium performance increase and medium degradation, and gestural interaction
has the highest performance increase and the lowest degradation between hands.
Gestures with speech for hand-impaired persons
Poster abstracts
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Guinness, Darren
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Poor, G. Michael
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Jude, Alvin
Sixteenth International ACM SIGACCESS Conference on Computers and
Accessibility
2014-10-20
p.259-260
© Copyright 2014 ACM
Summary: Mid-air hand-gestural interaction generally causes a fatigue due to
implementations that require the user to hold their arm out during this
interaction. Recent research has discovered a new approach to reduce fatigue
related to gestural interaction, by allowing users to rest their elbow on a
surface, and calibrate their interaction space from this rested position[1].
Additionally, this approach reduced stress on the hand and wrist compared to
the mouse, by shifting much of the load to the forearm and shoulder muscles. In
this paper we evaluated gesture and speech multimodal interaction as a form of
assistive interaction for those with hand impairments. Two participants with
hand impairments were recruited to perform the evaluation. We collected
qualitative and quantitative data, which showed promising results in using this
method for assistive interaction.
Personal space: user defined gesture space for GUI interaction
Works-in-progress
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Jude, Alvin
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Poor, G. Michael
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Guinness, Darren
Proceedings of ACM CHI 2014 Conference on Human Factors in Computing Systems
2014-04-26
v.2
p.1615-1620
© Copyright 2014 ACM
Summary: Reality-Based Interaction (RBI) [14] theorizes that realistic user
interactions (UIs) are effective because they exploit users' pre-existing
knowledge about their bodies and objects in the world. Gesture based
interaction allows users to relay information to a computer through body
movement without physical contact with additional hardware such as a mouse or
trackball. However, this interaction style requires the users to interact in a
manner that is tailored for the system to recognize with very strict rules for
bodily interaction, not toward a gesture space that is more natural for the
user. In this paper we propose a natural method of gestural input through a
user-defined 3-dimensional space. We conducted two pilot studies to assess the
performance and usability of these augmented gestural pointing methods for
cursor manipulation as compared to a standard mouse interaction as well as the
current standard approach used in gestural input.
