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.
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.
Figure 1. This paper presents performance and ergonomics indices for six typical touchscreen surfaces. Motion capture-based biomechanical simulation was used to understand differences in speed, accuracy, posture, energy expenditure, and muscle group differences. This figure shows the median postures recorded in the study. ABSTRACTAlthough different types of touch surfaces have gained extensive attention in HCI, this is the first work to directly compare them for two critical factors: performance and ergonomics. Our data come from a pointing task (N=40) carried out on five common touch surface types: public display (large, vertical, standing), tabletop (large, horizontal, seated), laptop (medium, adjustably tilted, seated), tablet (seated, in hand), and smartphone (single-and two-handed input). Ergonomics indices were calculated from biomechanical simulations of motion capture data combined with recordings of external forces. We provide an extensive dataset for researchers and report the first analyses of similarities and differences that are attributable to the different postures and movement ranges.
Abstract-We propose a computer-assisted constraint-based methodology for virtual reassembly of Cultural Heritage (CH) artworks. Instead than focusing on automatic, unassisted reassembly, we targeted the scenarios where the reconstruction process is not be based on shape properties only but it is build over the experience and intuition of a CH expert. Our purpose is therefore to design a flexible interactive system, based on the selection of a set of constraints which relates different fragments, according to the understanding and experience of the CH operator. Once the user has defined those constraints, the system searches for a suitable solution, using a global energy minimization strategy that considers simultaneously all the pieces involved in the reconstruction process. Additionally, our framework provides the possibility to work in a hierarchical way, mimicking the traditional physical procedure that archaeologists use to reassemble tangible fractured objects. The frameworks is designed to work even with fragments that could have been severely damaged or eroded. On those datasets, automatic approaches may often fail, since the fractured regions do not contain enough geometric information to infer the correct matches. We present some successful uses of our framework on real application scenarios.
In Human-Computer Interaction (HCI), experts seek to evaluate and compare the performance and ergonomics of user interfaces. Recently, a novel cost-efficient method for estimating physical ergonomics and performance has been introduced to HCI. It is based on optical motion capture and biomechanical simulation. It provides a rich source for analyzing human movements summarized in a multidimensional data set. Existing visualization tools do not sufficiently support the HCI experts in analyzing this data. We identified two shortcomings. First, appropriate visual encodings are missing particularly for the biomechanical aspects of the data. Second, the physical setup of the user interface cannot be incorporated explicitly into existing tools. We present MovExp, a versatile visualization tool that supports the evaluation of user interfaces. In particular, it can be easily adapted by the HCI experts to include the physical setup that is being evaluated, and visualize the data on top of it. Furthermore, it provides a variety of visual encodings to communicate muscular loads, movement directions, and other specifics of HCI studies that employ motion capture and biomechanical simulation. In this design study, we follow a problem-driven research approach. Based on a formalization of the visualization needs and the data structure, we formulate technical requirements for the visualization tool and present novel solutions to the analysis needs of the HCI experts. We show the utility of our tool with four case studies from the daily work of our HCI experts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.