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This article presents a novel summarization of biomechanical and performance data for user interface designers. Previously, there was no simple way for designers to predict how the location, direction, velocity, precision, or amplitude of users' movement affects performance and fatigue. We cluster muscle coactivation data from a 3D pointing task covering the whole reachable space of the arm. We identify 11 clusters of pointing movements with distinct muscular, spatio-temporal, and performance properties. We discuss their use as heuristics when designing for 3D pointing. ACM Reference Format:Myroslav Bachynskyi, Gregorio Palmas, Antti Oulasvirta, and Tino Weinkauf. 2015. Informing the design of novel input methods with muscle coactivation clustering.

Motion-capture-based biomechanical simulation is a noninvasive analysis method that yields a rich description of posture, joint, and muscle activity in human movement. The method is presently gaining ground in sports, medicine, and industrial ergonomics, but it also bears great potential for studies in HCI where the physical ergonomics of a design is important. To make the method more broadly accessible, we study its predictive validity for movements and users typical to studies in HCI. We discuss the sources of error in biomechanical simulation and present results from two validation studies conducted with a state-of-the-art system. Study I tested aimed movements ranging from multitouch gestures to dancing, finding out that the critical limiting factor is the size of movement. Study II compared muscle activation predictions to surface-EMG recordings in a 3D pointing task. The data shows medium-to-high validity that is, however, constrained by some characteristics of the movement and the user. We draw concrete recommendations to practitioners and discuss challenges to developing the method further.