Augmented Reality System Design and Scenario Study for Location-Based Adaptive Mobile Learning

Author(s):  
William Chang ◽  
Qing Tan
Author(s):  
Mohammad Faizal Amir ◽  
Novia Ariyanti ◽  
Najih Anwar ◽  
Erik Valentino ◽  
Dian Septi Nur Afifah

Augmented Reality research in mobile learning systems so far have not especially to improve Preservice Students Teachers' (PSTs) geometry understanding in mathematical development. Studies conducted in this study basically use a case study. This research aims to develop a mobile augmented reality system for PSTs learning so that it can be used to improve PSTs understanding of mathematics development includes doing, image-making, an image having, property noticing, formalizing, observing, structuring and inventing. In this development, PSTs can understand the understanding of the geometry transformation is a translation, reflection, rotation, and dilatation.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3061
Author(s):  
Alice Lo Valvo ◽  
Daniele Croce ◽  
Domenico Garlisi ◽  
Fabrizio Giuliano ◽  
Laura Giarré ◽  
...  

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.


2013 ◽  
Vol 60 (9) ◽  
pp. 2636-2644 ◽  
Author(s):  
Hussam Al-Deen Ashab ◽  
Victoria A. Lessoway ◽  
Siavash Khallaghi ◽  
Alexis Cheng ◽  
Robert Rohling ◽  
...  

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
Jurjen Caarls ◽  
Pieter Jonker ◽  
Yolande Kolstee ◽  
Joachim Rotteveel ◽  
Wim van Eck

2009 ◽  
Vol 5 (4) ◽  
pp. 415-422 ◽  
Author(s):  
Ramesh Thoranaghatte ◽  
Jaime Garcia ◽  
Marco Caversaccio ◽  
Daniel Widmer ◽  
Miguel A. Gonzalez Ballester ◽  
...  

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