Abstract-Designing a good scatterplot can be difficult for non-experts in visualization, because they need to decide on many parameters, such as marker size and opacity, aspect ratio, color, and rendering order. This paper contributes to research exploring the use of perceptual models and quality metrics to set such parameters automatically for enhanced visual quality of a scatterplot. A key consideration in this paper is the construction of a cost function to capture several relevant aspects of the human visual system, examining a scatterplot design for some data analysis task. We show how the cost function can be used in an optimizer to search for the optimal visual design for a user's dataset and task objectives (e.g., "reliable linear correlation estimation is more important than class separation"). The approach is extensible to different analysis tasks. To test its performance in a realistic setting, we pre-calibrated it for correlation estimation, class separation, and outlier detection. The optimizer was able to produce designs that achieved a level of speed and success comparable to that of those using human-designed presets (e.g., in R or MATLAB). Case studies demonstrate that the approach can adapt a design to the data, to reveal patterns without user intervention.

No abstract

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.

Aalto Interface Metrics (AIM) pools several empirically validated models and metrics of user perception and attention into an easy-to-use online service for the evaluation of graphical user interface (GUI) designs. Users input a GUI design via URL, and select from a list of 17 different metrics covering aspects ranging from visual clutter to visual learnability. AIM presents detailed breakdowns, visualizations, and statistical comparisons, enabling designers and practitioners to detect shortcomings and possible improvements. The web service and code repository are available at interfacemetrics.aalto.fi.