Gregorio Palmas

An Edge-Bundling Layout for Interactive Parallel Coordinates

Parallel Coordinates is an often used visualization method for multidimensional data sets. Its main challenges for large data sets are visual clutter and over plotting which hamper the recognition of patterns in the data. We present an edge-bundling method using density-based clustering for each dimension. This reduces clutter and provides a faster overview of clusters and trends. Moreover, it allows rendering the clustered lines using polygons, decreasing rendering time remarkably. In addition, we design interactions to support multidimensional clustering with this method. A user study shows improvements over the classic parallel coordinates plot in two user tasks: correlation estimation and subset tracing.

Performance and Ergonomics of Touch Surfaces: A Comparative Study using Biomechanical Simulation

Although 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.

Is motion capture-based biomechanical simulation valid for HCI studies?: study and implications

Motion-capture-based biomechanical simulation is a non-invasive 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.

Informing the Design of Novel Input Methods with Muscle Coactivation Clustering

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.

MovExp: A Versatile Visualization Tool for Human-Computer Interaction Studies with 3D Performance and Biomechanical Data

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.

Towards Perceptual Optimization of the Visual Design of Scatterplots

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.

Biomechanical simulation in the analysis of aimed movements

For efficient design of gestural user interfaces both performance and fatigue characteristics of movements must be understood. We are developing a novel method that allows for biomechanical analysis in conjunction with performance analysis. We capture motion data using optical tracking from which we can compute performance measures such as speed and accuracy. The measured motion data also serves as input for a biomechanical simulation using inverse dynamics and static optimization on a full-body skeletal model. The simulation augments the data by biomechanical quantities from which we derive an index of fatigue. We are working on an interactive analysis tool that allows practitioners to identify and compare movements with desirable performance and fatigue properties. We show the applicability of our methodology using a case study of rapid aimed movements to targets covering the 3D movement space uniformly.

Space bundling for continuous parallel coordinates

Continuous Parallel Coordinates (CPC) are a visualization technique used to perform multivariate analysis of different scalar fields defined on the same domain. While classic Parallel Coordinates draws a line for each sample point, a CPC visualization uses a density-based representation. An interesting possibility for the classic method is to highlight higher-dimensional clusters using edge bundling, where each line becomes a spline bent towards the centroid of the cluster. This often leads to expressive, illustrative visualizations. Unfortunately, bundling lines is not possible for CPC, as they are not involved in this method. In this paper, we propose a deformation of the visualization space for Continuous Parallel Coordinates that leads to similar results as those obtained through classic edge bundling. We achieve this by performing a curved-profile transformation in image space. The approach lends itself to a computationally lightweight GPU implementation. Furthermore, we provide intuitive interactions with the bundled clusters. We show several examples of our technique applied to a commonly available data set.

Aalto Interface Metrics (AIM): A Service and Codebase for Computational GUI Evaluation

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.

Gestikulieren mit Stil

ZusammenfassungUm Überanstrengungen und Ermüdungen vorzubeugen, müssen sich die Designer von neuen Nutzerschnittstellen auch über ergonomische Aspekte Gedanken machen. Hierzu kann eine Methode eingesetzt werden, bei der optisches Motion Capture und anschließende biomechanische Simulation verwendet werden. Wir erklären die Methode und beschreiben, wie wir sie im Bereich der Mensch-Computer-Interaktion einsetzen.