Gait Identification Based on Parallel Shift Technology

2020 ◽  
pp. 75-89
Author(s):  
Stepan Bilan ◽  
Ruslan Motornyuk ◽  
Sergey Yuzhakov ◽  
Mariia Halushko
Keyword(s):  
Parallel Shift ◽  
Gait Identification

scholarly journals 5G-FOG: Freezing of Gait Identification in Multi-class Softmax Neural Network Exploiting 5G Spectrum

2020 ◽  
pp. 26-36
Author(s):  
Jan Sher Khan ◽  
Ahsen Tahir ◽  
Jawad Ahmad ◽  
Syed Aziz Shah ◽  
Qammer H. Abbasi ◽  
...  
Keyword(s):  
Neural Network ◽  
Freezing Of Gait ◽  
Gait Identification

scholarly journals Kinect-based human gait identifcation under different covariate factors

2018 ◽  
Vol 6 (2) ◽  
pp. 1-11
Author(s):  
Azhin T. Sabir
Keyword(s):  
Human Body ◽  
Gait Recognition ◽  
Video Quality ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Human Beings ◽  
Linear Discriminant ◽  
Gait Biometrics ◽  
Gait Identification ◽  
Microsoft Kinect Sensor

Introduction: Nowadays human gait identification/recognition is available in a variety of applications due to rapid advances in biometrics technology. This makes them easier to use for security and surveillance. Due to the rise in terrorist attacks during the last ten years research has focused on the biometric traits in these applications and they are now capable of recognising human beings from a distance. The main reason for my research interest in Gait biometrics is because it is unobtrusive and requires lower image/video quality compared to other biometric traits. Materials and Methods: In this paper we propose investigating Kinect-based gait recognition using non-standard gait sequences. This study examines different scenarios to highlight the challenges of non-standard gait sequences. Gait signatures are extracted from the 20 joint points of the human body using a Microsoft Kinect sensor. Results and Discussion: This feature is constructed by calculating the distances between each two joint points from the 20 joint points of the human body provided which is known as the Euclidean Distance Feature (EDF). The experiments are based on five scenarios, and a Linear Discriminant Classifier (LDC) is used to test the performance of the proposed method. Conclusions: The results of the experiments indicate that the proposed method outperforms previous work in all scenarios.

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