TOCHI2106-30 ACM-TRANSACTION December 1, 2014 13:32 30 Informing the Design of Novel Input Methods with Muscle 1 Coactivation Clustering 2 MYROSLAV BACHYNSKYI, Max Planck Institute for Informatics and Saarland University 3 GREGORIO PALMAS, Max Planck Institute for Informatics 4 ANTTI OULASVIRTA, Max Planck Institute for Informatics and Saarland University 5 TINO WEINKAUF, Max Planck Institute for Informatics 6 This article presents a novel summarization of biomechanical and performance data for user interface 7 designers. Previously, there was no simple way for designers to predict how the location, direction, velocity, 8 precision, or amplitude of users’ movement affects performance and fatigue. We cluster muscle coactivation 9 data from a 3D pointing task covering the whole reachable space of the arm. We identify 11 clusters of 10 pointing movements with distinct muscular, spatio-temporal, and performance properties. We discuss their 11 use as heuristics when designing for 3D pointing. 12 Categories and Subject Descriptors: H.5.2 [Information Interfaces and Presentation (e.g., HCI)]: User 13 Interfaces—Ergonomics 14 General Terms: Human factors, Performance, Design 15 Additional Key Words and Phrases: Muscle coactivation clustering, user interface design, physical 16 ergonomics, user performance, biomechanical simulation 17 ACM Reference Format: 18 Myroslav Bachynskyi, Gregorio Palmas, Antti Oulasvirta, and Tino Weinkauf. 2014. Informing the design 19 of novel input methods with muscle coactivation clustering. ACM Trans. Comput.-Hum. Interact. 21, 6, 20 Article 30 (December 2014), 25 pages. 21 DOI: http://dx.doi.org/10.1145/2687921 22 1. INTRODUCTION 23 This article investigates a novel data-driven approach to biomechanical simulation to 24 inform User Interface (UI) design. Motion capture-based biomechanical simulation is 25 an inverse approach to observed motion of the human body. It bears great potential 26 for Human-Computer Interaction (HCI) because it yields a very rich description of a 27 user’s movement—including velocities and angles of limb segments, forces and mo- 28 ments at joints, and, most importantly, muscle activations [Thelen et al. 2003]. Muscle 29 activations could be particularly useful in interface design as indices of users’ fatigue 30 and energy usage. Moreover, as a method, it has advantages over direct measurements 31 like electromyography (EMG): It is nonintrusive; can estimate activation of all mus- 32 cles, not only those close to surface; and it does not suffer from cross-talk, variable 33 skin-conductivity, or muscle movement noise. Moreover, open simulation software is 34 emerging [Delp et al. 2007], and optical motion tracking equipment is becoming more 35 This research was funded by the Cluster of Excellence for Multimodal Computing and Interaction at Saarland University, as well as the Max Planck Center for Visual Computing and Communication and the Interna- tional Max Planck Research School for Computer Science at the Max Planck Institute for Informatics. Author’s addresses: Q1 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 profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. c  2014 ACM 1073-0516/2014/12-ART30 $15.00 DOI: http://dx.doi.org/10.1145/2687921 ACM Transactions on Computer-Human Interaction, Vol. 21, No. 6, Article 30, Publication date: December 2014.