The Effect of Color on Performance in an Instructional Gaming Environment

1991 ◽  
Vol 24 (2) ◽  
pp. 246-253 ◽  
Author(s):  
Lori A. Livingston
2004 ◽  
Author(s):  
David B. Boles ◽  
Jeffrey B. Phillips ◽  
Jason R. Perdelwitz ◽  
Jonathan H. Bursk

1989 ◽  
Vol 17 (3) ◽  
pp. 189-203 ◽  
Author(s):  
Philip Duchastel

Games have a fascination for people which make them ideal vehicles for instruction of an informal nature. Described here is an instructional game (GEO) in which the user learns elements of Canadian geography as she chases a spy around the country. The game utilizes artificial intelligence approaches to represent and put to use various types of knowledge (knowledge of geography, of tutoring, and of the student). Our experience in designing and refining the game is discussed, as well as prospects for extending this approach to other learning situations.


Author(s):  
Sergey Lobov ◽  
Nadia Krilova ◽  
Innokentiy Kastalskiy ◽  
Victor Kazantsev ◽  
Valeri A. Makarov

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by a long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. A short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.


Author(s):  
Sarika Chaudhary ◽  
Shalini Bhaskar Bajaj ◽  
Aman Jatain ◽  
Pooja Nagpal

Game controllers have been planned and improved throughout the years to be as easy to understand as could reasonably be expected. A game controller is a gadget utilized with games or theatre setups to give contribution to a computer game, commonly to control an item or character in the game. Information gadgets that have been named game controllers incorporate consoles, mice, gamepads, joysticks, and so on. A few controllers are intended to be purposely best for one sort of game, for example, guiding wheels for driving games, move cushions for moving games, and light firearms for firing games. The aim here is to create a virtual environment, where the user is appealed by various gesture controls in a gaming application. A Gesture is an action that has to be seen or felt by someone else (here a PC) and has to convey some piece of information. Now obviously, to create a virtual gaming environment, we need to create a real-time gaming application first. We’ll be designing our 2D and 3D gaming applications through Unity 3D video game engine. The data used in this project is primarily from the Ego Hands dataset. After an input has been taken, and the consequent action has been performed, we’ll use this activity for future development of the model by using Tensor-Flow. The input will be taken through the webcam of the PC which will be accessed and combined to the gaming application and hands dataset by WebGL. WebGL is a JavaScript API for rendering interactive 2D and 3D graphics within any compatible web browser without the use of plug-ins.


Author(s):  
Agnieszka Chmurzynska ◽  
Karolina Hejbudzka ◽  
Andrzej Dumalski

During the last years the softwares and applications that can produce 3D models using low-cost methods have become very popular. What is more, they can be successfully competitive with the classical methods. The most wellknown and applied technology used to create 3D models has been laser scanning so far. However it is still expensive because of the price of the device and software. That is why the universality and accessibility of this method is very limited. Hence, the new low cost methods of obtaining the data needed to generate 3D models appeare on the market and creating 3D models have become much easier and accessible to a wider group of people. Because of their advantages they can be competitive with the laser scanning. One of the methods uses digital photos to create 3D models. Available software allows us to create a model and object geometry. Also very popular in the gaming environment device – Kinect Sensor can be successfully used as a different method to create 3D models. This article presents basic issues of 3D modelling and application of various devices, which are commonly used in our life and they can be used to generate a 3D model as well. Their results are compared with the model derived from the laser scanning. The acquired results with graphic presentations and possible ways of applications are also presented in this paper.


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