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).