Application of serious games to sport, health and exercise

Use of interactive entertainment has been exponentially expanded since the last decade. Throughout this 10+ year evolution there has been a concern about turning entertainment properties into serious applications, a.k.a "Serious Games". In this article we present two set of Serious Game applications, an Environment Visualising game which focuses solely on applying serious games to elite Olympic sport and another set of serious games that incorporate an in house developed proprietary input system that can detect most of the human movements which focuses on applying serious games to health and exercise

Towards a consistent SLAM algorithm using B-splines to represent environments

This paper presents a statistically consistent SLAM algorithm where the environment is represented using a collection of B-Splines. The use of B-Splines allow environment to be represented without having to extract specific geometric features such as lines or points. Our previous work proposed a new observation model that enables raw measurements taken from a laser range finder to be transferred into relative position information between the control points of a B-Spline and the robot pose where the observation is made. One of the unresolved issues in the work was the estimation of the observation covariance, which is addressed through an analytical approach in this paper. As the uncertainty associated with the observation model is accurately defined and an optimization approach is used in the estimation process, the proposed SLAM algorithm can produce consistent estimates. Both simulation and experimental data are used for evaluation of the results. ©2010 IEEE

How analysts think: sense-making strategies in the analysis of temporal evolution and criminal network structures and activities

Analysis of criminal activity based on offenders’ social networks is an established procedure in intelligence analysis. The complexity of the data poses an obstacle for analysts to gauge network developments, e.g. detect emerging problems. Visualization is a powerful tool to achieve this, but it is essential to know how the analysts’ sense-making strategies can be supported most efficiently. Based on a think aloud study we identified ten cognitive strategies on a general level to be useful for designers. We also provide some examples how these strategies can be supported through appropriate visualizations

Sense-making strategies in explorative intelligence analysis of network evolutions

Visualising how social networks evolve is important in intelligence analysis in order to detect and monitor issues, such as emerging crime patterns or rapidly growing groups of offenders. It remains an open research question how this type of information should be presented for visual exploration. To get a sense of how users work with different types of visualisations, we evaluate a matrix and a node-link diagram in a controlled thinking aloud study. We describe the sense-making strategies that users adopted during explorative and realistic tasks. Thereby, we focus on the user behaviour in switching between the two visualisations and propose a set of nine strategies. Based on a qualitative and quantitative content analysis we show which visualisation supports which strategy better. We find that the two visualisations clearly support intelligence tasks and that for some tasks the combined use is more advantageous than the use of an individual visualisation

Studying How Health Literacy Influences Attention during Online Information Seeking

Health literacy affects how people understand health information and, therefore, should be considered by search engines in health searches. In this work, we analyze how the level of health literacy is related to the eye movements of users searching the web for health information. We performed a user study with 30 participants that were asked to search online in the context of three work task situations defined by the authors. Their eye interactions with the Search Results Page and the Result Pages were logged using an eye-tracker and later analyzed. When searching online for health information, people with adequate health literacy spend more time and have more fixations on Search Result Pages. In this type of page, they also pay more attention to the results' hyperlink and snippet and click in more results too. In Result Pages, adequate health literacy users spend more time analyzing textual content than people with lower health literacy. We found statistical differences in terms of clicks, fixations, and time spent that could be used as a starting point for further research. That we know of, this is the first work to use an eye-tracker to explore how users with different health literacy search online for health-related information. As traditional instruments are too intrusive to be used by search engines, an automatic prediction of health literacy would be very useful for this type of system

Concern level assessment: building domain knowledge into a visual system to support network-security situation awareness

Information officers and network administrators require tools to help them achieve situation awareness about potential network threats. We describe a response to mini-challenge 1 of the 2012 IEEE VAST challenge in which we developed a visual analytic solution to a network security situation awareness problem. To support conceptual design, we conducted a series of knowledge elicitation sessions with domain experts. These provided an understanding of the information they needed to make situation awareness judgements as well as a characterisation of those judgements in the form of production rules which define a parameter we called the ‘Concern Level Assessment’ (CLA). The CLA was used to provide heuristic guidance within a visual analytic system called MSIEVE. An analysis of VAST challenge assessment sessions using M-SIEVE provides some evidence that intelligent heuristics like this can provide useful guidance without unduly dominating interaction and understanding

M-Sieve: a visualisation tool for supporting network security analysts

The Middlesex Spatial Interactive Visualisation Environment (M-Sieve) is a spatiotemporal visual analytics tool for exploring computer network activity. M-Sieve allows the user to filter and visualize data through facets to explore and find patterns. To help guide exploration, we developed a set of rules which are used to derive a variable we call the ‘Concern Level Assessment’ (CLA). The CLA is based on attributes of nodes on the network. The rules were developed by eliciting inferences from network security domain experts. The combination of M-Sieve and the CLA allowed us to address the problem presented by the VAST 2012 Competition - Mini Challenge 1

An abductive process of developing interactive data visualization: a case study of market attractiveness analysis

Part 4: Business Intelligence and AnalyticsInternational audienceData visualization has been widely utilized in various scenarios in data analytics for business purposes, especially helping novice readers make sense of complex dataset with interactive functions. However, due to an insufficient theoretical understanding of the process of developing interactive functions and visual presentations, interactive data visualization tools often display all available automatic graphing functions in front of users, instead of guiding them to choose a visualization based on their demands. Thus, this paper is intended to construct a process of developing interactive visualization with a specific focus on enabling the interoperation between design and interpretation. Stemmed from organizational semiotics, an abductive process will be portrayed in this paper to interpret the process of developing interactive data visualization. Especially the interactive functions will be employed in an iterative process, where producers can be aware of and respond to readers’ information demands on semantic, pragmatic and social levels

On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone array

Sound source localization is an important topic in expert systems involving microphone arrays, such as automatic camera steering systems, human-machine interaction, video gaming or audio surveillance. The Steered Response Power with Phase Transform (SRP-PHAT) algorithm is a well-known approach for sound source localization due to its robust performance in noisy and reverberant environments. This algorithm analyzes the sound power captured by an acoustic beamformer on a defined spatial grid, estimating the source location as the point that maximizes the output power. Since localization accuracy can be improved by using high-resolution spatial grids and a high number of microphones, accurate acoustic localization systems require high computational power. Graphics Processing Units (GPUs) are highly parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. Emerging GPUs offer multiple parallelism levels; however, properly managing their computational resources becomes a very challenging task. In fact, management issues become even more difficult when multiple GPUs are involved, adding one more level of parallelism. In this paper, the performance of an acoustic source localization system using distributed microphones is analyzed over a massive multichannel processing framework in a multi-GPU system. The paper evaluates and points out the influence that the number of microphones and the available computational resources have in the overall system performance. Several acoustic environments are considered to show the impact that noise and reverberation have in the localization accuracy and how the use of massive microphone systems combined with parallelized GPU algorithms can help to mitigate substantially adverse acoustic effects. In this context, the proposed implementation is able to work in real time with high-resolution spatial grids and using up to 48 microphones. These results confirm the advantages of suitable GPU architectures in the development of real-time massive acoustic signal processing systems.This work has been partially funded by the Spanish Ministerio de Economia y Competitividad (TEC2009-13741, TEC2012-38142-C04-01, and TEC2012-37945-C02-02), Generalitat Valenciana PROMETEO 2009/2013, and Universitat Politecnica de Valencia through Programa de Apoyo a la Investigacion y Desarrollo (PAID-05-11 and PAID-05-12).Belloch Rodríguez, JA.; Gonzalez, A.; Vidal Maciá, AM.; Cobos Serrano, M. (2015). On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone array. Expert Systems with Applications. 42(13):5607-5620. https://doi.org/10.1016/j.eswa.2015.02.056S56075620421

Qualitative Adaptation: Informing Design for Risk-based Decision Making

Research on decision making during risk and uncertainty facilitates risk-based decision making by understanding techniques decision makers use to arrive at informed decisions. Approaches to the research usually involve a mix of cognitive techniques for information discovery and sense-making; these were methodologically not intended to inform design. We detail our experience in applying qualitative techniques to elicit persona characteristics from risk-based decision making data