Uploaded by 徐国辉

探索用于3D选择的手臂到手指控制的改变肌肉映射

advertisement
An Exploration of Altered Muscle Mappings of Arm to Finger
Control for 3D Selection
Elliot O. Hunt
Amy Banic
University of Wyoming
Laramie, Wyoming
ehunt4@uwyo.edu
University of Wyoming, Idaho National Lab
Laramie, Wyoming
abanic@cs.uwyo.edu
ABSTRACT
In this poster, we present a novel 3-dimensional (3D) interaction
technique, Altered Muscle Mapping (AMM), to re-map muscle movements of hands/arms to �ngers/wrists. We implemented an initial design of AMM as a 3-Dimensional (3D) selection technique,
where �nger movements translate a virtual cursor (in 3-degrees-offreedom) for selection. Direct Manipulation performance bene�ts
may be preserved yet reduce physical fatigue. We designed an initial
set of mapping variations. Our results from an initial pilot study provide initial performance insights of mapping con�gurations. AMM
has potential for direct hand interaction in virtual and augmented
reality and for users with a limited range of motion.
CCS CONCEPTS
• Human-centered computing → Gestural input; User centered
design; Interaction design theory, concepts and paradigms;
KEYWORDS
3D UI, Spatial Interaction, Bi-manual, Uni-manual, Finger Gestures,
Selection Technique, Muscle Mapping, Direct Manipulation
ACM Reference Format:
Elliot O. Hunt and Amy Banic. 2018. An Exploration of Altered Muscle
Mappings of Arm to Finger Control for 3D Selection. In Symposium on
Spatial User Interaction (SUI ’18), October 13–14, 2018, Berlin, Germany. ACM,
New York, NY, USA, 1 page. https://doi.org/10.1145/3267782.3275241
1
INTRODUCTION AND MOTIVATION
Mid-air interaction techniques can produce fatigue [3]. Previous
techniques reduce little fatigue and rely on the rotator-cu� [5].
Smaller movements [6] and resting hands on a surface while interacting reduce fatigue [1]. To achieve similar results, we look to
previous research that suggests high acceptance rates of altered
anatomy. Humans are able to adapt to use a tail e�ectively for selection tasks by re-mapping its control to lower hip muscles [4].
Humans are able to control a third limb from their chest by mapping to movements of a participant’s arms [5]. In this poster, we
present Altered Muscle Mappings (AMM) of �nger to arm control.
We present our four designs of AMM (smallest muscles to largest)
for 3D selection: XYThumb, YThumb, 3-Fingers, and Fist.
Permission to make digital or hard copies of part or all of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for pro�t or commercial advantage and that copies bear this notice and the full citation
on the �rst page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
SUI ’18, October 13–14, 2018, Berlin, Germany
© 2018 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-5708-1/18/10.
https://doi.org/10.1145/3267782.3275241
Figure 1: Finger mappings of four AMM designs.
2
AMM TECHNIQUE AND PILOT RESULTS
We designed four variations of AMMs (Figure 1) and collected data
in a pilot study. For selection, �nger movements translate a cursor in
3D. Unlike other work where each �nger movement aligns to x, y, or
z axis [2], AMM �nger movements may not exactly map along each
axis. XYThumb: X and Y positions of cursor controlled by a thumb’s
horizontal and vertical movements (Thumb muscles). The cursor’s
Z position is controlled by curling and uncurling the bottom three
�ngers. YThumb: X position is controlled by a thumb’s horizontal
movement. Y is controlled by a palm’s position in space (Moved
with wrist muscles). Z is controlled by curling and uncurling the
bottom three �ngers. 3-Fingers: X and Y positions are determined
by a palm position. Z is controlled by curling and uncurling the
bottom three �ngers. Fist: A Palm’s position from wrist, elbow, and
rotator cu� motion determines X, Y, and Z positions of cursor. Four
adult participants were recruited for a pilot study. Participants used
a HTC VIVE and LEAP Motion controller to use AMM techniques
to select a series of presented targets. Fist AMM selection resulted
in highest accuracy and preference, and least physically fatiguing.
Y-Thumb AMM selection was least, and XYThumb AMM selection
was most physically fatiguing. These initial trends will help to
redesign the mappings and conduct a more extensive study.
ACKNOWLEDGMENTS
The authors thank Rajiv Khadka for design and poster feedback.
REFERENCES
[1] H. Benko and S. Feiner. 2007. Balloon Selection: A Multi-Finger Technique for
Accurate Low-Fatigue 3D Selection. IEEE Symposium on 3D User Interfaces (2007).
[2] G. Casiez, P. Plenacoste, C. Chaillou, and B. Semail. 2003. The DigiHaptic, a new
three degrees of freedom multi-�nger haptic device. In Proc. of IEEE VR (2003).
[3] J.D. Hincapie-Ramos, X. Guo, P. Moghadasian, and P. Irani. 2014. Consumed
endurance: a metric to quantify arm fatigue of mid-air interactions. CHI ’14 Proc.
of the SIGCHI conf. on Human Factors in Computer Systems (Apr 2014), 1063–1072.
[4] A. Steed W. Steptoe and M. Slater. 2013. Human tails: ownership and control of
extended humanoid avatars. IEEE Trans on Visual Computer Graphics 19, 4 (2013).
[5] A.S. Won, J.N. Bailenson, and J. Lanier. 2015. Homuncular Flexibility: The Human
Ability to Inhabit Nonhuman Avatars. Emerging Trends in the Social and Behavioral
Sciences 1, 16 (2015).
[6] H. Yang, G. Kim, and S. Han. 2002. Body-based interfaces. Fourth IEEE International
Conference on Multimodal Interfaces (2002).
Download