A 3D Deep Learning Approach for Classification of Gait Abnormalities Using Microsoft Kinect V2 Sensor

Author(s):  
Milad Shoryabi ◽  
Ali Foroutannia ◽  
Alireza Rowhanimanesh
2021 ◽  
Vol 128 ◽  
pp. 103785
Author(s):  
Yongqing Jiang ◽  
Dandan Pang ◽  
Chengdong Li

Author(s):  
Alessio P. Buccino ◽  
Torbjorn V. Ness ◽  
Gaute T. Einevoll ◽  
Gert Cauwenberghs ◽  
Philipp D. Hafliger

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 41770-41781 ◽  
Author(s):  
Catherine Sandoval ◽  
Elena Pirogova ◽  
Margaret Lech

Author(s):  
Fernando Merchan ◽  
Martin Poveda ◽  
Danilo E. Cáceres-Hernández ◽  
Javier E. Sanchez-Galan

This chapter focuses on the contributions made in the development of assistive technologies for the navigation of blind and visually impaired (BVI) individuals. A special interest is placed on vision-based systems that make use of image (RGB) and depth (D) information to assist their indoor navigation. Many commercial RGB-D cameras exist on the market, but for many years the Microsoft Kinect has been used as a tool for research in this field. Therefore, first-hand experience and advances on the use of Kinect for the development of an indoor navigation aid system for BVI individuals is presented. Limitations that can be encountered in building such a system are addressed at length. Finally, an overview of novel avenues of research in indoor navigation for BVI individuals such as integration of computer vision algorithms, deep learning for the classification of objects, and recent developments with stereo depth vision are discussed.


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