Dance-the-music : an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

In this article, a computational platform is presented, entitled “Dance-the-Music”, that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers’ models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method can determine the quality of a student’s performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures

Tools of the Trade: A Survey of Various Agent Based Modeling Platforms

Agent Based Modeling (ABM) toolkits are as diverse as the community of people who use them. With so many toolkits available, the choice of which one is best suited for a project is left to word of mouth, past experiences in using particular toolkits and toolkit publicity. This is especially troublesome for projects that require specialization. Rather than using toolkits that are the most publicized but are designed for general projects, using this paper, one will be able to choose a toolkit that already exists and that may be built especially for one's particular domain and specialized needs. In this paper, we examine the entire continuum of agent based toolkits. We characterize each based on 5 important characteristics users consider when choosing a toolkit, and then we categorize the characteristics into user-friendly taxonomies that aid in rapid indexing and easy reference.Agent Based Modeling, Individual Based Model, Multi Agent Systems

Evaluation of a puzzle-based virtual platform for improving spatial visualization skills in engineering freshmen

Being able to spatially visualize and mentally rotate is a key skill necessary to succeed in graphics and subsequent engineering courses. Recent research has focused on methods to develop Spatial Visualization (SV) skills in engineering students, as it is a key skill to succeed in most of the STEM fields. However, in most of the engineering schools, the instructors find it very difficult to develop keen SV skills in students. The major factors contributing to this challenge include, but not limited to the huge class sizes, limited time to teach the material, lack of effective demonstrations and the unavailability of feasible hands-on activities. With the funding from the National Science Foundation, the authors are developing a puzzle-based active learning platform called Student Assistant for Visualization in Engineering (SAVE) for developing SV skills in engineering freshman. In the preliminary version of this learning platform, the students are asked to complete a quiz with tasks requiring SV skills. For any incorrect answer, they are provided with automated hints about their mistakes. These hints are expected to help them in solving the following tasks. If they commit three mistakes, the quiz locks itself and creates a report on their performance thus far. The students are able to go back and restart the quiz. The student\u27s target is to complete the quiz with a minimum number of attempts. In the study reported here, the effectiveness of this game platform in conveying essential concepts of engineering graphics is investigated. Firstly, SAVE is implemented in a smaller classroom and the student feedback is collected. Then, it is implemented in a freshmen graphics class in a large public university in the west coast. The performance of the participating students in a follow-up exam is compared against that of a control group. The results show that the use of SAVE improves students\u27 conceptual understanding compared to a control group, as measured by the scores in the follow-up exam

Bridging the Geospatial Education-Workforce Divide: A Case Study on How Higher Education Can Address the Emerging Geospatial Drivers and Trends of the Intelligent Web Mapping Era

The purpose of this exploratory collective case study is to discover how geospatial education can meet the geospatial workforce needs of the Commonwealth of Virginia, in the emerging intelligent web mapping era. Geospatial education uses geographic information systems (GIS) to enable student learning by increasing in-depth spatial analysis and meaning using geotechnology tools (Baker & White, 2003). Bandura’s (1977) self-efficacy theory and geography concept of spatial thinking form an integrated theoretical framework of spatial cognition for this study. Data collection included in-depth interviews of twelve geospatial stakeholders, documentation collection, and supporting Q methodology to determine the viewpoints of a total of 41 geospatial stakeholders. Q methodology is a type of data collection that when used as a qualitative method utilizes sorting by the participant to determine their preferences. Data analysis strategies included cross-case synthesis, direct interpretation, generalizations, and a correlation matrix to show similarities in participants\u27 preferences. The results revealed four collaborative perceptions of the stakeholders, forming four themes of social education, technology early adoption, data collaboration, and urban fundamentals. Four strategies were identified for higher education to prepare students for the emerging geospatial workforce trends. These strategies are to teach fundamentals, develop agile faculty and curriculum, use an interdisciplinary approach, and collaborate. These strategies reflect the perceptions of stakeholders in this study on how higher education can meet the emerging drivers and trends of the geospatial workforce

A review of tertiary BIM education for advanced engineering communication with visualization

SPECT with Tc-99m-labeled agents is better able to detect viability after nitrate administration. Nitrates induce vasoclilation and may increase blood flow to severely hypoperfused but viable myocardium, thereby enhancing tracer delivery and improving the detection of viability. Quantitative data on the changes in blood flow are lacking in SPECT but can be provided by PET. The aim of the present study was to use PET to evaluate whether nitrate administration increases blood flow to chronically dysfunctional but viable myocardium. Methods: N-13-Ammonia PET was used to quantitatively assess blood flow, and F-18-FDG PET was used as the gold standard to detect viable myocardium. Twenty-five patients with chronic ischemic left ventricular dysfunction underwent N-13-ammonia PET at rest and after nitrate administration. Results: A significant increase in nitrate-enhanced blood flow was observed in viable segments (from 0.55 +/- 0.15 to 0.68 +/- 0.24 mL/min/g, P <0.05). No statistically significant change in blood flow was observed in nonviable segments (0.60 +/- 0.20 vs. 0.55 +/- 0.18 mL/min/g). A ratio of at least 1.1 for nitrate-enhanced flow to resting flow allowed optimal detection of viable myocardium, yielding a sensitivity of 82% with a specificity of 100%. Conclusion: N-13-Ammonia PET showed a significant increase in nitrate-enhanced blood flow in viable myocardium, whereas blood flow remained unchanged after nitrate administration in nonviable myocardium. Nitrate use during myocardial perfusion imaging will lead to improved assessment of myocardial viability

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion