Two-dimensional PCA-based human gait identification

2012 ◽  
Author(s):  
Jinyan Chen ◽  
Rongteng Wu
Keyword(s):  
Human Gait ◽  
Two Dimensional ◽  
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.

Gait analysis

2013 ◽  
Author(s):  
Sophie Roberts ◽  
Sharon Dixon
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Visual Analysis ◽  
Three Dimensional ◽  
Human Gait ◽  
Two Dimensional ◽  
3D Motion ◽  
Walking Gait ◽  
3D Motion Analysis ◽  
Technological Analysis

Gait analysis describes the process of systematically quantifying mechanical aspects of walking or running to aid in the examination of a patient/client. In the publication Gait Analysis: An Introduction, Whittle (2002) identifies the eye as being the first tool in this assessment, with technology being available to supplement this visual analysis. Technological analysis tools include two-dimensional (2D) video, three-dimensional (3D) motion analysis, pressure plates, and pressure insoles. The application of technology has increased our understanding of human gait substantially. This chapter introduces the basic tools of gait analysis and highlights specific considerations when selecting appropriate tools for the assessment of walking gait. Details of running gait are provided in Chapter 1.8....

scholarly journals Open source hardware based sensor platform suitable for human gait identification

2017 ◽  
Vol 38 ◽  
pp. 154-165 ◽  
Author(s):  
César Llamas ◽  
Manuel A. González ◽  
Carmen Hernández ◽  
Jesús Vegas
Keyword(s):  
Open Source ◽  
Human Gait ◽  
Sensor Platform ◽  
Gait Identification ◽  
Open Source Hardware

scholarly journals Gait-based Biometric Identification System using Triangulated Skeletal Models (TSM)

2021 ◽  
Vol 10 (3) ◽  
pp. 202-208
Author(s):  
Azhin Tahir Sabir
Keyword(s):  
Nearest Neighbor ◽  
Individual Recognition ◽  
Gait Recognition ◽  
User Interaction ◽  
Human Gait ◽  
Identification System ◽  
Kinect Sensor ◽  
Human Beings ◽  
Gait Identification ◽  
Leave One Out

Human gait identification is a behavioral biometric technology which can be used to monitor human beings without user interaction. Recent researches are more focused on investigating gait as one of the biometric traits.  Further, gait recognition aims to analyze and identify human behavioral activities and may be implemented in different scenarios including access control and criminal analysis. However, using various techniques in relation to image processing and obtaining better accuracy are remaining challenges. In last decade, Microsoft has introduced the Kinect sensor as an innovative sensor to provide image characteristics, precisely. Therefore, this article uses a Kinect sensor to extract gait characteristics to be used in individual recognition. A set of Triangulated shape are generated as new feature vector and called Triangulated Skeletal Model (TSM). Nearest Neighbor technique is utilized to do the recognition issue based on leave-one-out strategy. The experimental outcomes indicated that the recommended technique provides significant results and outperforms other comparative similar techniques with accuracy of 93.46%.  

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2020 ◽  
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Gold Standard ◽  
Three Dimensional ◽  
Human Gait ◽  
Large Set ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

ABSTRACTWalking is the primary mode of human locomotion. Accordingly, people have been interested in studying human gait since at least the fourth century BC. Human gait analysis is now common in many fields of clinical and basic research, but gold standard approaches – e.g., three-dimensional motion capture, instrumented mats or footwear, and wearables – are often expensive, immobile, data-limited, and/or require specialized equipment or expertise for operation. Recent advances in video-based pose estimation have suggested exciting potential for analyzing human gait using only two-dimensional video inputs collected from readily accessible devices (e.g., smartphones, tablets). However, we currently lack: 1) data about the accuracy of video-based pose estimation approaches for human gait analysis relative to gold standard measurement techniques and 2) an available workflow for performing human gait analysis via video-based pose estimation. In this study, we compared a large set of spatiotemporal and sagittal kinematic gait parameters as measured by OpenPose (a freely available algorithm for video-based human pose estimation) and three-dimensional motion capture from trials where healthy adults walked overground. We found that OpenPose performed well in estimating many gait parameters (e.g., step time, step length, sagittal hip and knee angles) while some (e.g., double support time, sagittal ankle angles) were less accurate. We observed that mean values for individual participants – as are often of primary interest in clinical settings – were more accurate than individual step-by-step measurements. We also provide a workflow for users to perform their own gait analyses and offer suggestions and considerations for future approaches.

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