Time Removed Repeated Trials to Test the Quality of a Human Gait Recognition System

2020 ◽  
pp. 15-24
Author(s):  
Marcin Derlatka
Keyword(s):  
Gait Recognition ◽  
Recognition System ◽  
Human Gait

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

Human Gait Recognition System

2018 ◽  
Author(s):  
Bilal Jawed ◽  
Othman O. Khalifa ◽  
Sharif Shah Newaj Bhuiyan
Keyword(s):  
Gait Recognition ◽  
Recognition System ◽  
Human Gait

scholarly journals A Novel Human Gait Recognition System

2010 ◽  
pp. 1043-1047 ◽  
Author(s):  
Seyyed Meysam Hosseini ◽  
Abbas Nasrabadi ◽  
Peyman Nouri ◽  
Hasan Farsi
Keyword(s):  
Gait Recognition ◽  
Recognition System ◽  
Human Gait

scholarly journals Principal component analysis for human gait recognition system

2019 ◽  
Vol 8 (2) ◽  
pp. 569-576
Author(s):  
Othman O. Khalifa ◽  
Bilal Jawed ◽  
Sharif Shah Newaj Bhuiyn
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Gait Recognition ◽  
Principal Component ◽  
Initial Step ◽  
Component Analysis ◽  
Recognition System ◽  
Human Gait ◽  
The One ◽  
Main Components

This paper represents a method for Human Recognition system using Principal Component Analysis. Human Gait recognition works on the gait of walking subjects to identify people without them knowing or without their permission. The initial step in this kind of system is to generate silhouette frames of walking human. A number of features couldb be exytacted from these frames such as centriod ratio, heifht, width and orientation. The Principal Component Analysis (PCA) is used for the extracted features to condense the information and produces the main components that can represent the gait sequences for each waiking human. In the testing phase, the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance matched with the one that already exist in the database used to identify walking subject.

scholarly journals Examination of the Effect of Psychophysical Factors on the Quality of Human Gait Recognition

2014 ◽  
Vol 8 (4) ◽  
pp. 194-198 ◽  
Author(s):  
Marcin Derlatka
Keyword(s):  
Confusion Matrix ◽  
Gait Recognition ◽  
Body Height ◽  
Human Gait ◽  
Biometric Systems ◽  
The Matrix ◽  
Reaction Forces ◽  
Similar Weight ◽  
Emotional Condition

Abstract The paper presents an analysis concerning the influence of selected psychophysical parameters on the quality of human gait recognition. The following factors have been taken into account: body height (BH), body weight (BW), the emotional condition of the respondent, the physical condition of the respondent, previous injuries or dysfunctions of the locomotive system. The study was based on data measuring the ground reaction forces (GRF) among 179 participants (3 315 gait cycles). Based on the classification, some kind of confusion matrix were established. On the basis of the data included in the matrix, it was concluded that the wrong classification was most affected by the similar weight of two confused people. It was also noted, that people of the same gender and similar BH were confused most often. On the other hand, previous body injuries and dysfunctions of the motor system were the factors facilitating the recognition of people. The results obtained will allow for the design of more accurate biometric systems in the future.

scholarly journals Gait Recognition Based on Invariant Leg Classification Using a Neuro-Fuzzy Algorithm as the Fusion Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hadi Sadoghi Yazdi ◽  
Hessam Jahani Fariman ◽  
Jaber Roohi
Keyword(s):  
High Precision ◽  
Gait Recognition ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Human Gait ◽  
Fusion Method ◽  
Data Set ◽  
Fuzzy Algorithm ◽  
Neuro Fuzzy ◽  
Gesture Classification

This paper presents a human gait recognition algorithm based on a leg gesture separation. Main innovation in this paper is gait recognition using leg gesture classification which is invariant to covariate conditions during walking sequence and just focuses on underbody motions and a neuro-fuzzy combiner classifier (NFCC) which derives a high precision recognition system. At the end, performance of the proposed algorithm has been validated by using the HumanID Gait Challenge data set (HGCD), the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition, and time. And it has been compared to recent algorithm of gait recognition.

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