Touchless Hand Interaction and Machine Learning Agents to Support Kinesthetic Learning in Augmented Reality

Author(s):  
Muhammad Zahid Iqbal ◽  
Abraham G. Campbell
Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 831
Author(s):  
Vaneet Aggarwal

Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]


2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.


2020 ◽  
Vol 5 (19) ◽  
pp. 104-122
Author(s):  
Azzan Amin ◽  
Haslina Arshad ◽  
Ummul Hanan Mohamad

Data visualization is viewed as a significant element in data analysis and communication. As the data engagement becomes more and more complex, visual presentation of data does help users understand the data. So far, two-dimensional (2D) data visuals are often used for the data visualization process, but the lack of depth dimension leads to inefficient and limited understanding of the data. Therefore, the effectiveness of augmented reality (AR) in data visualization was studied through the development of an AR Data Visualization application using E-commerce data. Machine learning models are also involved in the development of this AR application for the provision of data using predictive analysis functions. To provide quality E-commerce data and an optimal machine learning model, the data science process is carried out using the python programming language. The E-commerce data selected for this study is open data taken through the Kaggle Website. This database has 9994 data numbers and 21 attributes. This AR data visualization application will make it easier for users to understand the E-commerce data in-depth through the use of AR technology and be able to visualize the forecasts for sales profit based on the algorithm model "Auto-Regressive Integrated Moving Average" (ARIMA).


2021 ◽  
Vol 4 ◽  
pp. 98-100
Author(s):  
Semen Gorokhovskyi ◽  
Yelyzaveta Pyrohova

With the rapid development of applications for mobile platforms, developers from around the world already understand the need to impress with new technologies and the creation of such applications, with which the consumer will plunge into the world of virtual or augmented reality. Some of the world’s most popular mobile operating systems, Android and iOS, already have some well-known tools to make it easier to work with the machine learning industry and augmented reality technology. However, it cannot be said that their use has already reached its peak, as these technologies are at the stage of active study and development. Every year the demand for mobile application developers increases, and therefore more questions arise as to how and from which side it is better to approach immersion in augmented reality and machine learning. From a tourist point of view, there are already many applications that, with the help of these technologies, will provide more information simply by pointing the camera at a specific object.Augmented Reality (AR) is a technology that allows you to see the real environment right in front of us with a digital complement superimposed on it. Thanks to Ivan Sutherland’s first display, created in 1968 under the name «Sword of Damocles», paved the way for the development of AR, which is still used today.Augmented reality can be divided into two forms: based on location and based on vision. Location-based reality provides a digital picture to the user when moving through a physical area thanks to a GPS-enabled device. With a story or information, you can learn more details about a particular location. If you use AR based on vision, certain user actions will only be performed when the camera is aimed at the target object.Thanks to advances in technology that are happening every day, easy access to smart devices can be seen as the main engine of AR technology. As the smartphone market continues to grow, consumers have the opportunity to use their devices to interact with all types of digital information. The experience of using a smartphone to combine the real and digital world is becoming more common. The success of AR applications in the last decade has been due to the proliferation and use of smartphones that have the capabilities needed to work with the application itself. If companies want to remain competitive in their field, it is advisable to consider work that will be related to AR.However, analyzing the market, one can see that there are no such applications for future entrants to higher education institutions. This means that anyone can bring a camera to the university building and learn important information. The UniApp application based on the existing Swift and Watson Studio technologies was developed to simplify obtaining information on higher education institutions.


2015 ◽  
Vol 41 ◽  
pp. 55-60 ◽  
Author(s):  
Olivier Pauly ◽  
Benoit Diotte ◽  
Pascal Fallavollita ◽  
Simon Weidert ◽  
Ekkehard Euler ◽  
...  

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