Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
Traditional museums and most digital museums use window display to exhibit their collections. However, the agricultural tools\nare distinctive for their use value and wisdom contained. Therefore, this paper first proposes a method of virtual interactive\ndisplay for agricultural tools based on virtual reality technology, which combines static display and dynamic use of agricultural\ntools vividly showing the agricultural tools. To address the problems of rigid interaction and terrible experience in the process\nof human-computer interaction, four human-computer interaction technologies are proposed to design and construct a more\nhumanized system including intelligent scenes switching technology, multichannel introduction technology, interactive virtual\nroaming technology, and task-based interactive technology. The evaluation results demonstrate that the system proposed achieves\ngood performance in fluency, instructiveness, amusement, and practicability. This human-computer interaction system can not\nonly show the wisdom of Chinese traditional agricultural tools to the experiencer all over the world but also put forward a new\nmethod of digital museum design....
A human gesture prediction system can be used to estimate human gestures in advance of\nthe actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for\nhumanâ??computer interaction. Therefore, the gesture prediction system must be able to capture hand\nmovements that are both complex and quick. We have already reported a method that allows strain\nsensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite\nsheets (PGSs). The wearable electronics could detect various types of human gestures with high\nsensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction\nby artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs.\nOur experiments entailed measuring the hand gestures of subjects for learning purposes and we\nused these data to create four-layered ANNs, which enabled the proposed system to successfully\npredict hand gestures in real time. A comparison of the proposed method with other algorithms using\ntemporal data analysis suggested that the hand gesture prediction system using ANNs would be\nable to forecast various types of hand gestures using resistance data obtained from wearable devices\nbased on PGSs....
The practice of regular physical exercise is a protective factor against noncommunicable diseases and premature mortality. In spite\nof that, large part of the population does not meet physical activity guidelines and many individuals live a sedentary life. Recent\ntechnological progresses and the widespread adoption of mobile technology, such as smartphone and wearables, have opened the\nway to the development of digital behaviour change interventions targeting physical activity promotion. Such interventions would\ngreatly benefit from the inclusion of computational models framed on behaviour change theories and model-based reasoning.\nHowever, research on these topics is still at its infancy.The current paper presents a smartphone application and wearable device\nsystem called Muoviti! that targets physical activity promotion among adults not meeting the recommended physical activity\nguidelines. Specifically, we propose a computational model of behaviour change, grounded on the social cognitive theory of selfefficacy.\nThe purpose of the computational model is to dynamically integrate information referring to individualsâ?? self-efficacy\nbeliefs and physical activity behaviour in order to define exercising goals that adapt to individualsâ?? changes over time.The paper\npresents (i) the theoretical constructs that informed the development of the computational model, (ii) an overview of Muoviti!\ndescribing the system dynamics, the graphical user interface, the adopted measures and the intervention design, and (iii) the\ncomputational model based on Dynamic Decision Network.We conclude by presenting early results from an experimental study....
Given the broad range of applications from video surveillance to humanâ??computer\ninteraction, human action learning and recognition analysis based on 3D skeleton data are currently\na popular area of research. In this paper, we propose a method for action recognition using depth\nsensors and representing the skeleton time series sequences as higher-order sparse structure tensors\nto exploit the dependencies among skeleton joints and to overcome the limitations of methods that\nuse joint coordinates as input signals. To this end, we estimate their decompositions based on\nrandomized subspace iteration that enables the computation of singular values and vectors of large\nsparse matrices with high accuracy. Specifically, we attempt to extract different feature representations\ncontaining spatio-temporal complementary information and extracting the mode-n singular values\nwith regards to the correlations of skeleton joints. Then, the extracted features are combined using\ndiscriminant correlation analysis, and a neural network is used to recognize the action patterns.\nThe experimental results presented use three widely used action datasets and confirm the great\npotential of the proposed action learning and recognition method....
The research on augmented reality applications in education is still in an early stage, and there is a lack of research on the effects\nand implications of augmented reality in the field of education.The purpose of this research was to measure and understand the\nimpact of an augmented reality mobile application on the learning motivation of undergraduate health science students at the\nUniversity of Cape Town. We extend previous research that looked specifically at the impact of augmented reality technology\non student learning motivation. The intrinsic motivation theory was used to explain motivation in the context of learning. The\nattention, relevance, confidence, and satisfaction (ARCS) model guided the understanding of the impact of augmented reality on\nstudent motivation, and the Instructional Materials Motivation Survey was used to design the research instrument. The research\nexamined the differences in student learning motivation before and after using the augmented reality mobile application. A total\nof 78 participants used the augmented reality mobile application and completed the preusage and postusage questionnaires. The\nresults showed that using an augmented reality mobile application increased the learning motivation of students. The attention,\nsatisfaction, and confidence factors of motivation were increased, and these results were found to be significant. Although the\nrelevance factor showed a decrease it proved to be insignificant....
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