Kehollistuneet vuorovaikutuskoreografiat. Kinesteettinen lähestymistapa älykkäiden ympäristöjen suunnitteluun

Research investigates interaction design through application of the concept of choreography. Special attention is paid to assess what kind of influences technological designs have on the user’s body and movements. Choreographic approach to interaction design emphasizes the felt experience of movement as content to interaction design and offers methods for conducting multi-level choreographic analysis. The concept of kinesthesia, which refers to the felt sensation of movement, is regarded as the foundational concept for both understanding and realizing the choreographic analysis. Choreographic method is applied in studying a future vision of intelligent information and communication environments. Intelligent environment refers to development where objects in everyday environments become connected and form a communicating-actuating network that possess abilities to collect information on the environment and of its users, and enables processing of this information for serving the user’s needs. The research data consists of two visions on intelligent environments in video format, introduced by Microsoft. Visions are analyzed through choreographic analysis with intention to investigate interactions between the user, the intelligent environment and the computer system. Micro level choreography analysis focuses on how the user experiences choreographies as movement continuums. Also local level choreographies that address the broader interaction context will be analyzed. Task based analysis focuses on two functions, first, sending and fetching digital information and, second, real time re-modelling of data and visualizations. Phenomenological methodology that enabled embodiment of the choreographies through dancing was applied in the analysis. Dancing aimed at internalizing the choreographies and enabled the analysis of felt sensation of movement. Key finding of the study is that choreographic analysis and hermeneutics of the body work well to be utilized in tandem in conducting a case study research on intelligent ICT environments. Dancing is considered as choreographic practice that provides understanding on the unfolding of interactions in space, time and movement. Furthermore, dancing integrates the designer’s explicit technological information to the design context and highlights the kinesthetic dimension of interaction. Presented methods provide relevant support for defining technological systems in intelligent ICT environments that are grounded in the embodied experience of interaction. I suggest that ‘dancing as choreographic practice’ is to be applied in user-centered design of intelligent information and communication environments.Tutkimus tarkastelee vuorovaikutussuunnittelua koreografian käsitteen kautta. Koreografinen lähestymistapa tarkastelee teknologian kokonaisvaltaista ohjausvaikutusta käyttäjän liikkeeseen teknologian käyttötilanteessa. Koreografinen suunnitteluote korostaa liikkeen kokemuksen huomioimisen tärkeyttä vuorovaikutussuunnittelussa ja tarjoaa menetelmiä monitasoisen vuorovaikutusanalyysin toteuttamiseen. Kinestesian käsite, jolla tarkoitetaan liikkeen kokemista kehossa, nousee yhdeksi koreografisen lähestymistavan keskeisistä käsitteistä. Sovellan koreografista menetelmää tulevaisuuden älykästä informaatio- ja kommunikaatioympäristöä kuvaavan vision tutkimiseen. Älykkäällä ympäristöllä viittaan kehityskulkuun, jossa jokapäiväisissä ympäristöissämme läsnä oleva teknologia verkottuu, kykenee keräämään ja jakamaan tietoa ympäristöstä ja käyttäjistä sekä mahdollistaa tiedon jalostuksen käyttäjän tarpeita palvelevalla tavalla. Aineistona on käytetty Microsoftin teknologiavisioita, joissa esitetyt kuvaukset älykkäistä ympäristöistä sekä esimerkit käyttäjän ja teknologian välisistä liikkeellisistä vuorovaikutuksista nousevat analyysin kohteeksi. Analyysissa keskitytään ensinnäkin käyttäjän toteuttamien mikroliikkeiden jatkumon kokemuksen analyysiin. Toiseksi analysoidaan yksilön kokemusta paikallisen tason koreografioissa. Tällä analyysitasolla huomiota kiinnitetään teknologista vuorovaikutusta laajemman vuorovaikutustapahtuman kontekstiin jolloin mm. sosiaaliset tapahtumat ja tilan vaikutus vuorovaikutukseen tulevat huomioiduksi. Analyysi toteutetaan tehtäväperusteisena ja analyysi käsittää kaksi toimintoa: tiedostojen jakaminen ja vastaanottaminen sekä datan ja visualisointien muokkaus. Toteutin tutkimuksen nojaten fenomenologiseen metodologiaan, joka mahdollisti koreografioiden henkilökohtaisen omaksumisen tanssin eli tutkimuksen kohteena olevien vuorovaikutustapojen kehollisen harjoittamisen kautta. Teknologiavisioissa esitetyn liikemateriaalin perusteella jäsentyi koreografia, jonka tanssiminen mahdollisti liiketiedon sisäistämisen ja vuorovaikutusten kehollisesti koettujen ulottuvuuksien arvioinnin. Tutkimus osoitti koreografisen analyysin ja osittain tanssimalla toteutetun ruumiin hermeneuttisen lähestymistavan soveltuvan hyvin sovellettavaksi yhdessä älykästä ympäristöä käsittelevässä tapaustutkimuksessa. Tutkimuksen johtopäätöksenä koreografisen menetelmän ja vuorovaikutusten kehollisen harjoittamisen todetaan auttavan suunnittelijaa tilassa, ajassa ja liikkeessä tapahtuvien vuorovaikutusten jäsentämisessä, ja arvioimaan miten teknologisen järjestelmän suunnitteluratkaisut vaikuttavat käyttäjän kehoon ja liikkeeseen vuorovaikutustapahtumassa. Esitän ’tanssimista koreografisena käytäntönä’ sovellettavaksi älykkäiden ympäristöjen käyttäjäkeskeisen suunnittelun menetelmänä

Interactive relighting, digital image enhancement and inclusive diagrammatic representations for the analysis of rock art superimposition: The main Pleito cave (CA, USA)

This paper deals with the documentation, and virtual visual analysis of pictographs using interactive relighting, digital image enhancement techniques and diagrammatic representations. It discusses areas of interest for the analysis of low surface detail, large and geometrically complex superimposed pictographs. The synergy of reflectance transformation imaging (RTI) and decorrelation stretch (DS) aimed to improve the study of superimposition via the enhanced visualization of the surface morphology, dominant features, paint characteristics and layering. Additionally, diagrammatic representations of the results of the image-based analysis provided a valuable tool for interpretation and integration of the diverse dataset from the ongoing research in the Pleito Cave in California. This method allows revisiting unresolved hypotheses concerning the site by unpacking chemical and visual data in superimposed sequences

The Effect of Alignment on Peoples Ability to Judge Event Sequence Similarity

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This paper describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic vs. local vs. global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98% vs. 93% correct), with the basic group getting 95% correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches

Learning from Teacher's Eye Movement: Expertise, Subject Matter and Video Modeling

How teachers' eye movements can be used to understand and improve education is the central focus of the present paper. Three empirical studies were carried out to understand the nature of teachers' eye movements in natural settings and how they might be used to promote learning. The studies explored 1) the relationship between teacher expertise and eye movement in the course of teaching, 2) how individual differences and the demands of different subjects affect teachers' eye movement during literacy and mathematics instruction, 3) whether including an expert's eye movement and hand information in instructional videos can promote learning. Each study looked at the nature and use of teacher eye movements from a different angle but collectively converge on contributions to answering the question: what can we learn from teachers' eye movements? The paper also contains an independent methodology chapter dedicated to reviewing and comparing methods of representing eye movements in order to determine a suitable statistical procedure for representing the richness of current and similar eye tracking data. Results show that there are considerable differences between expert and novice teachers' eye movement in a real teaching situation, replicating similar patterns revealed by past studies on expertise and gaze behavior in athletics and other fields. This paper also identified the mix of person-specific and subject-specific eye movement patterns that occur when the same teacher teaches different topics to the same children. The final study reports evidence that eye movement can be useful in teaching; by showing increased learning when learners saw an expert model's eye movement in a video modeling example. The implications of these studies regarding teacher education and instruction are discussed.PHDEducation & PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145853/1/yizhenh_1.pd

Contributions to the cornerstones of interaction in visualization: strengthening the interaction of visualization

Visualization has become an accepted means for data exploration and analysis. Although interaction is an important component of visualization approaches, current visualization research pays less attention to interaction than to aspects of the graphical representation. Therefore, the goal of this work is to strengthen the interaction side of visualization. To this end, we establish a unified view on interaction in visualization. This unified view covers four cornerstones: the data, the tasks, the technology, and the human.Visualisierung hat sich zu einem unverzichtbaren Werkzeug für die Exploration und Analyse von Daten entwickelt. Obwohl Interaktion ein wichtiger Bestandteil solcher Werkzeuge ist, wird der Interaktion in der aktuellen Visualisierungsforschung weniger Aufmerksamkeit gewidmet als Aspekten der graphischen Repräsentation. Daher ist es das Ziel dieser Arbeit, die Interaktion im Bereich der Visualisierung zu stärken. Hierzu wird eine einheitliche Sicht auf Interaktion in der Visualisierung entwickelt

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp

Visualisation of Long in Time Dynamic Networks on Large Touch Displays

Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging fields from travel networks to epidemiology.However, it is very difficult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We first introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To confirm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we find that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also find that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types