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Visual Analysis of Multidimensional Data for Biomechanics and HCI
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Multidimensional analysis is performed in many scientific fields.Its main tasks involve the identification of correlations between data dimensions,the investigation of data clusters, and the identification of outliers. Visualization techniques often help in getting a better understanding. In this thesis, we present our work on improving visual multidimensional analysis by exploiting the semantics of the data and enhancing the perception of existing visualizations. Firstly, we exploit the semantics of the data by creating new visualizations which present visual encodings specifically tailoredto the analyzed dimensions. We consider the resulting visual analysis to be more intuitive for the user asit provides a more easily understandable idea of the data. In this thesis we concentrate on the visual analysis of multidimensional biomechanical data for Human-Computer Interaction (HCI).To this end, we present new visualizations tackling the specific features of different aspectsof biomechanical data such as movement ergonomics, leading to a more intuitive analysis. Moreover, by integrating drawings or sketches of the physical setup of a case study as new visualizations, we allow for a fast and effective case-specific analysis. The creation of additional visualizations for communicating trends of clusters of movements enables a cluster-specific analysis which improves our understanding of postures and muscular co-activation.Moreover, we create a new visualization which addresses the specificity of the multidimensional data related to permutation-based optimization problems. Each permutation of a given set of n elements represents a point defined in an n-dimensional space. Our method approximates the topologyof the problem-related optimization landscape inferring the minima basins and their properties and visualizing them organized in a quasi-landscape. We show the variability of the solutions in a basin using heat maps generated from permutation matrices.Furthermore, we continue improving our visual multidimensional analysis by enhancing the perceptual encoding of existing well-known multidimensional visualizations. We focus on Parallel Coordinates Plots (PCP) and its derivative Continuous Parallel Coordinates (CPC). The main perceptual issues of PCP are visual clutter and overplotting which hamper the recognition of patterns in large data sets. In this thesis, we present an edge-bundling method for PCP which uses density-based clustering for each dimension. This reduces clutter and provides a faster overview of clusters and trends. Moreover, it allows for a fast rendering of the clustered lines using polygons. Furthermore, we present the first bundling approach for Continuous Parallel Coordinates where classic edge-bundling fails due to the absence of lines. Our method performs a deformation of the visualization space of CPC leading to similar results as those obtained through classic edge-bundling.Our work involved 10 HCI case studies and helped to establisha new research methodology in this field. This led to publications in internationally peer-reviewed journals and conference proceedings.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. , 60 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2016:27
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-193713ISBN: 978-91-7729-154-1 (print)OAI: oai:DiVA.org:kth-193713DiVA: diva2:1033911
Public defence
2016-11-17, VIC Visualization Studio, Lindstedtsvägen 7, floor 4, room 4450., Stockholm, 11:36 (English)
Opponent
Supervisors
Note

QC 20161011

Available from: 2016-10-11 Created: 2016-10-10 Last updated: 2016-10-11Bibliographically approved
List of papers
1. Biomechanical Simulation in the Analysis of Aimed Movements
Open this publication in new window or tab >>Biomechanical Simulation in the Analysis of Aimed Movements
2013 (English)In: Extended Abstracts (Works in Progress) CHI’13, ACM Digital Library, 2013, 1-6 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
ACM Digital Library, 2013
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-184836 (URN)10.1145/2468356.2468406 (DOI)978-1-4503-1952-2 (ISBN)
Conference
Extended Abstracts (Works in Progress) CHI’13
Note

QC 20160408

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2016-10-10Bibliographically approved
2. Is Motion Capture-Based Biomechanical Simulation Valid for HCI Studies?: Study and Implications
Open this publication in new window or tab >>Is Motion Capture-Based Biomechanical Simulation Valid for HCI Studies?: Study and Implications
2014 (English)In: Proc. ACM CHI Conference on Human Factors in Computing Systems, ACM Digital Library, 2014, 3215-3224 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
ACM Digital Library, 2014
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-184823 (URN)10.1145/2556288.2557027 (DOI)2-s2.0-84900425136 (Scopus ID)978-1-4503-2473-1 (ISBN)
Conference
ACM CHI Conference on Human Factors in Computing Systems
Note

QC 20160408

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2016-10-10Bibliographically approved
3. MovExp: A Versatile Visualization Tool for Human-Computer Interaction Studies with 3D Performance and Biomechanical Data
Open this publication in new window or tab >>MovExp: A Versatile Visualization Tool for Human-Computer Interaction Studies with 3D Performance and Biomechanical Data
Show others...
2014 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 20, no 12, 2359-2368 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Keyword
Design study, Human-Computer Interaction, Information visualization
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-184830 (URN)2-s2.0-84910058423 (Scopus ID)
Note

QC 20160425

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2017-11-30Bibliographically approved
4. Informing the Design of Novel Input Methods with Muscle Coactivation Clustering
Open this publication in new window or tab >>Informing the Design of Novel Input Methods with Muscle Coactivation Clustering
2015 (English)In: ACM Transactions on Computer-Human Interaction, ISSN 1073-0516, Vol. 21, no 6, 30Article in journal (Refereed) Published
Abstract [en]

This paper 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 eleven clusters of pointing movements with distinct muscular, spatio-temporal and performance properties. We discuss their use as heuristics when designing for 3D pointing.

Place, publisher, year, edition, pages
ACM Digital Library, 2015
Keyword
Muscle coactivation clustering, biomechanical simulation, physical ergonomics, user interface design, user performance
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-184822 (URN)10.1145/2687921 (DOI)2-s2.0-84921466880 (Scopus ID)
Note

QC 20160408

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2017-01-11Bibliographically approved
5. Performance and Ergonomics of Touch Surfaces: A Comparative Study Using Biomechanical Simulation
Open this publication in new window or tab >>Performance and Ergonomics of Touch Surfaces: A Comparative Study Using Biomechanical Simulation
Show others...
2015 (English)In: CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing, ACM Digital Library, 2015, 1817-1826 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
ACM Digital Library, 2015
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-184848 (URN)10.1145/2702123.2702607 (DOI)
Conference
ACM CHI Conference on Human Factors in Computing Systems
Note

Received a Best Paper Honorable Mention. QC 20160408

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2016-10-10Bibliographically approved
6. An Edge-Bundling Layout for Interactive Parallel Coordinates
Open this publication in new window or tab >>An Edge-Bundling Layout for Interactive Parallel Coordinates
Show others...
2014 (English)In: Proc. IEEE PacificVis, IEEE , 2014Conference paper, Published paper (Refereed)
Abstract [en]

Parallel Coordinates is an often used visualization method for multidimensional data sets. Its main challenges for large data sets are visual clutter and overplotting 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.

Place, publisher, year, edition, pages
IEEE, 2014
National Category
Computer Science
Research subject
Computer Science; SRA - E-Science (SeRC)
Identifiers
urn:nbn:se:kth:diva-184829 (URN)
Conference
IEEE PacificVis, Yokohama, Japan, March 4 - 7, 2014
Note

QC 20160425

Available from: 2016-04-05 Created: 2016-04-05 Last updated: 2016-10-10Bibliographically approved
7. Space Bundling for Continuous Parallel Coordinates
Open this publication in new window or tab >>Space Bundling for Continuous Parallel Coordinates
2016 (English)In: Eurographics Proceedings, The Eurographics Association , 2016Conference paper, Published paper (Refereed)
Abstract [en]

Continuous Parallel Coordinates (CPC) are a visualization technique used to perform multivariate analysis of different scalar fields defined on thesame domain.While classic Parallel Coordinatesdraws a line for each sample point,a CPC visualization uses a density-based representation.An interesting possibility for the classic methodis to highlight higher-dimensional clustersusing 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 Coordinatesthat 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 interactionswith the bundled clusters.We show several examples of our technique applied to a commonly available data set.

Place, publisher, year, edition, pages
The Eurographics Association, 2016
Keyword
Picture/Image Generation, Bitmap and framebuffer operations, Line and curve generation
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-193728 (URN)
Conference
EuroVis 2016, June 2016, Groningen, the Netherlands
Note

QC 20161011

Available from: 2016-10-10 Created: 2016-10-10 Last updated: 2016-10-11Bibliographically approved
8. Optimization Landscapes: A Topological Approach to Understanding Permutation-based Optimization Problems
Open this publication in new window or tab >>Optimization Landscapes: A Topological Approach to Understanding Permutation-based Optimization Problems
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Permutation-based optimization problemsare a class of NP-hard combinatorial problemsrepresenting many challenges in theory and practice.Their solution spaceconsists of all permutations $n!$of a given set of $n$ elements.Hence, for many real-world problemsthe solution spaceis too largeto even just visit every solution.The quality of a solutionis described by an objective function.A good understandingof the structures and symmetriesin this data is requiredto develop and steer heuristic algorithmsand other approaches to solving the underlying problem.We present a novel topological approachto exploring the objective functionsof permutation-based optimization problems.We infer the minima basinsand their propertiesfrom descending optimization paths.To deal with noise and general oversegmentation,we introduce an approachinspired by topological simplification of scalar fields.Based on this,we construct an edge-weighted graphapproximating the distances between basins,and visualize it using a force-directed layout,which shows the basins of local and global optimaorganized in a quasi-landscape.We show the variabilityof the solutions in a basinusing heat maps generated from permutation matrices.Our method is designed to be interactiveand read its input data as a streamfrom a simultaneously running simulation.We evaluate our method using different optimization problemsfrom both theory and practice.

National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-193729 (URN)
Note

QC 20161011

Available from: 2016-10-10 Created: 2016-10-10 Last updated: 2016-10-11Bibliographically approved

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