DOCSLIB.ORG

Effects of Chiropractic on Equine Gait Kinematics, Heart Rate, And

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Effects of Chiropractic on Equine Gait Kinematics, Heart Rate, And EFFECTS OF CHIROPRACTIC ON EQUINE GAIT KINEMATICS, HEART RATE, AND SERUM CORTISOL by JESSICA GLADNEY (Under the Direction of Kari Turner) ABSTRACT Study used 18 horses to determine how chiropractic affects riding sound horses over 8 weeks. Horses divided into Chiropractic (n=6), Riding (n=6), and Sedentary (n=6) group. Riding and Chiropractic group ridden 4 days per week. Sedentary horses unridden. Kinematics collected on Day 0, Day 2, Day 14, Day 28, Day 30, Day 42, and Day 56. Chiropractic group received chiropractic while Riding and Sedentary received grooming treatment on Day 1 and Day 29. Heart rate monitors placed on all horses during treatment. Serum cortisol collected on all PRE, MID, and POST treatment. At the trot, swing as a percentage of stride decreased in hind limbs on Day 56 compared to Day 28 (p=0.05), Day 14 (p=0.03), and Day 2 (p=0.02) in Chiropractic group. Cortisol is higher on Day 29 PRE vs. Day 1 PRE (P=0.03) in the Chiropractic group. Results show chiropractic minimally effects kinematics and increases PRE cortisol levels. INDEX WORDS: Chiropractic, Gait Kinematics, Heart rate, Serum Cortisol EFFECTS OF CHIROPRACTIC ON EQUINE GAIT KINEMATICS, HEART RATE, AND SERUM CORTISOL IN RIDING HORSES by JESSICA GLADNEY B.S., UGA, 2015 A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE ATHENS, GEORGIA 2017 © 2017 Jessica Gladney All Rights Reserved EFFECTS OF CHIROPRACTIC ON EQUINE GAIT KINEMATICS, HEART RATE, AND SERUM CORTISOL by JESSICA GLADNEY Major Professor: Kari Turner Committee: Kylee Jo Duberstein Franklin West Electronic Version Approved: Suzanne Barbour Dean of the Graduate School The University of Georgia December 2017 ACKNOWLEDGEMENTS Committee: Dr. Kari Turner, Dr. Kylee Jo Duberstein, Dr. Franklin West UGA Livestock Arena Manager: Alexander Abrams Chiropractor: Dr. Dana Peroni DVM, Covered Bridge Equine Lameness Evaluation: Dr. Paige Williams DVM, Covered Bridge Equine Cortisol: Dr. Clay Lents, (who was found via Dr. Michael Azain) Undergraduate Researchers and Riders: Mary Cate Marchert, Marisa Bartholomew, Erin Jarboe, Madisen Gloeggler, Morgan Garrick, Jessica Simons, and Kaitlyn Gilroy This project would not be possible without every single one of the above names. Thank you, Thank you with all my heart. Thank you to every single one of my family and friends who encouraged me, supported me, and helped me along the way. iv TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ........................................................................................................... iv LIST OF TABLES ........................................................................................................................ vii LIST OF FIGURES ..................................................................................................................... viii CHAPTER 1 INTRODUCTION .........................................................................................................1 Literature Cited ........................................................................................................3 2 REVIEW OF LITERATURE ........................................................................................4 Alternative Therapies ...............................................................................................4 Pain and Lameness ...................................................................................................7 Treating Pain and Lameness ....................................................................................9 Stress in Horses ........................................................................................................9 Biomechanics of the Horse ....................................................................................15 Gait and Back Kinematics .....................................................................................16 Literature Cited ......................................................................................................21 3 EFFECTS OF CHIROPRACTIC ON EQUINE GAIT KINEMATICS, HEART RATE, AND SERUM CORTISOL .............................................................................29 Abstract ..................................................................................................................30 Introduction ............................................................................................................32 Materials and Methods ...........................................................................................34 v Results ....................................................................................................................41 Discussion ..............................................................................................................48 Literature Cited ......................................................................................................51 4 CONCLUSIONS..........................................................................................................56 Literature Cited ......................................................................................................58 REFERENCES ..............................................................................................................................59 APPENDICES A Chiropractic Adjustment for Horses ............................................................................70 B Results Tables ..............................................................................................................71 vi LIST OF TABLES Page Table 1: AAEP Lameness Scale .....................................................................................................8 Table 2: Treatment*Day lsmeans of Cortisol .....................................................................................46 Table 3: Treatment*Day lsmeans of Heart Rate .................................................................................47 Table 4: Treatment*Day lsmeans of the Front Limb Measurements at the Walk ..................................71 Table 5: Treatment*Day lsmeans of the Back Limb Measurements at the Walk ...................................73 Table 6: Treatment*Day lsmeans of the Front Limb Measurements at the Trot ....................................74 Table 7: Treatment*Day lsmeans of the Back Limb Measurements at the Trot ....................................75 Table 8: Treatment*Day lsmeans of Suspension Time at the Trot .......................................................76 Table 9: Treatment*Day lsmeans of Stride Length at the Walk and Trot .............................................77 Table 10: Treatment lsmeans of Cortisol ...........................................................................................77 Table 11: Treatment lsmeans of AVG Heart Rate ..............................................................................78 Table 12: Treatment lsmeans of MAX Heart Rate .............................................................................78 Table 13: Treatment lsmeans of MIN Heart Rate ...............................................................................78 Table 14: Treatment lsmeans of Limb Measurements at the Walk .......................................................78 Table 15: Treatment lsmeans of Limb Measurements at the Trot ........................................................80 Table 16: Serum Cortisol by Horse and Group on Day 1 ....................................................................86 Table 17: Serum Cortisol by Horse and Group on Day 29 ..................................................................87 vii LIST OF FIGURES Page Figure 1: Walk and Trot of the Horse ............................................................................................16 Figure 2: Phases of the Trot ...........................................................................................................19 Figure 3.1: Treatment*Day Lsmeans of the Front Limbs at the Walk ..........................................41 Figure 3.2: Treatment*Day Lsmeans of the Hind Limbs at the Walk ...........................................42 Figure 4.1: Treatment*Day Lsmeans of the Front Limbs at the Trot ............................................43 Figure 4.2: Treatment*Day Lsmeans of the Hind Limbs at the Trot .............................................44 Figure 5: Stride Length of the Walk and Trot................................................................................45 Figure 6.1: Treatment*Day Lsmeans of the Front Limbs at the Trot ............................................82 Figure 6.2: Treatment*Day Lsmeans of the Front Limbs at the Trot ............................................82 Figure 7.1: Treatment*Day Lsmeans of the Hind Limbs at the Trot .............................................83 Figure 7.2: Treatment*Day Lsmeans of the Hind Limbs at the Trot .............................................83 Figure 8.1: Treatment*Day Lsmeans of the Front Limbs at the Walk ..........................................84 Figure 8.2: Treatment*Day Lsmeans of the Front Limbs at the Walk ..........................................84 Figure 9.1: Treatment*Day Lsmeans of the Hind Limbs at the Trot .............................................85 Figure 9.2: Treatment*Day Lsmeans of the Hind Limbs at the Trot .............................................85 viii CHAPTER 1 INTRODUCTION Lameness is the main cause of profit loss within the equine
Load more

Recommended publications
  • Gait Analysis in Prosthetics by James R
    Gait Analysis in Prosthetics by James R. Gage, M.D. Ramona Hicks, R.P.T., M.A. REVIEW lems faced by lower limb amputees. Inman's measurement techniques included motion pic­ Objective measurement systems which quan­ tures of coronal and sagittal views, as well as tify locomotion have been in use for the past transverse rotations from below using a glass century. But not until World War II, when walkway. Using interrupted light photography, thousands of men returned home to the United the Biomechanics Laboratory team studied the States with amputations, was technology really motion of body segments during gait. Force applied to the understanding of prosthetic gait. plates measured the subject's ground reaction Inman and colleagues1 founded the Biome­ forces, and muscle activity was recorded using chanics Laboratory at the University of Cali­ electromyography (EMG), which measures the fornia to establish fundamental principles of electrical signals associated with contraction of human walking, particularly in relation to prob­ a muscle. Prior to Inman's fundamental studies, prostheses were customized for the individual Temporal and kinematic data, which were col­ amputee, without any particular regard to ra­ lected at slow, free, and fast speeds, showed tional structural design. Inman's goal was to that the hydraulic knees improved the symmetry provide fundamental data essential for the de­ between the prosthetic limb and the sound limb, sign of prosthetic limbs. By analyzing normal especially at the fast and free speeds. This human walking, he and his colleagues laid the finding was true for both cadence and the groundwork for biomechanical analysis of am­ amount of knee-flexion at swing phase.
    [Show full text]
  • Normal and Abnormal Gaits in Dogs
    Pagina 1 di 12 Normal And Abnormal Gait Chapter 91 David M. Nunamaker, Peter D. Blauner z Methods of Gait Analysis z The Normal Gaits of the Dog z Effects of Conformation on Locomotion z Clinical Examination of the Locomotor System z Neurologic Conditions Associated With Abnormal Gait z Gait Abnormalities Associated With Joint Problems z References Methods of Gait Analysis Normal locomotion of the dog involves proper functioning of every organ system in the body, up to 99% of the skeletal muscles, and most of the bony structures.(1-75) Coordination of these functioning parts represents the poorly understood phenomenon referred to as gait. The veterinary literature is interspersed with only a few reports addressing primarily this system. Although gait relates closely to orthopaedics, it is often not included in orthopaedic training programs or orthopaedic textbooks. The current problem of gait analysis in humans and dogs is the inability of the study of gait to relate significantly to clinical situations. Hundreds of papers are included in the literature describing gait in humans, but up to this point there has been little success in organizing the reams of data into a useful diagnostic or therapeutic regime. Studies on human and animal locomotion commonly involve the measurement and analysis of the following: Temporal characteristics Electromyographic signals Kinematics of limb segments Kinetics of the foot-floor and joint resultants The analyses of the latter two types of measurements require the collection and reduction of voluminous amounts of data, but the lack of a rapid method of processing this data in real time has precluded the use of gait analysis as a routine clinical tool, particularly in animals.
    [Show full text]
  • Gait and Posture – Assessment in General Practice
    THEME Musculoskeletal medicine Gait and posture Assessment in general practice BACKGROUND A basic analysis of a patient’s gait and posture provides information about the body and the capability of the musculoskeletal system to adjust to physical stressors. An understanding of normal gait and posture is essential for Kent Sweeting identifying and treating musculoskeletal pain. BHlthSc(Pod)(Hons), MAPodA, OBJECTIVE is a podiatrist, Brisbane, and a This article discusses normal gait and how to assess gait. It also outlines common musculoskeletal conditions and their researcher, Griffith University, Queensland. k.sweeting@ association with abnormal gait and posture. General practitioners can detect faulty postural syndromes and abnormal griffith.edu.au gait by visual scanning and awareness of pain referral patterns. Michael Mock DISCUSSION MBBS, MMed(PhysMed), Awareness of pain that can arise from faulty gait and posture will assist GPs to shift their focus away from structural FRACGP, FACPM, is a general diagnoses and unhelpful radiological investigations. The GP can become an effective facilitator of the prevention and practitioner, Wetherill Park rehabilitation of pain problems where abnormal gait and posture are found to be a main contributing factor. and St Leonards, New South Wales. Gait analysis is comparable to an X-ray or blood test; 40% respectively.5 Figure 1 summarises all events and it is a powerful investigative tool, which together with the timing of just over one complete gait cycle. When the patient history and physical examination, may examining gait, clinicians can fall into the trap of solely be used to assess and diagnose patients suffering focusing on the stance phase and miss vital pieces of musculoskeletal pain, and predict successful treatment information that can be gathered from the patient’s swing of these pathologies.1–3 Understanding the basic phase (eg.
    [Show full text]
  • Grizzly Bear (Ursus Arctos Horribilis) Locomotion: Gaits and Ground Reaction Forces Catherine L
    © 2015. Published by The Company of Biologists Ltd | Journal of Experimental Biology (2015) 218, 3102-3109 doi:10.1242/jeb.121806 RESEARCH ARTICLE Grizzly bear (Ursus arctos horribilis) locomotion: gaits and ground reaction forces Catherine L. Shine1,*, Skylar Penberthy1, Charles T. Robbins2, O. Lynne Nelson3 and Craig P. McGowan1,4 ABSTRACT because of the lack of cursorial specialisations, their limb Locomotion of plantigrade generalists has been relatively little studied movements are less restricted to the sagittal plane (Liem et al., compared with more specialised postures even though plantigrady is 2001). Within mammals, plantigrade species include raccoons, ancestral among quadrupeds. Bears (Ursidae) are a representative badgers, weasels, as well as all rodents and primates. All of these family for plantigrade carnivorans, they have the majority of the animals are small compared with most digitigrade and especially morphological characteristics identified for plantigrade species, and unguligrade species; however, bears also retain the plantigrade they have the full range of generalist behaviours. This study stance. The goal of this study was to determine whether the compared the locomotion of adult grizzly bears (Ursus arctos locomotor mechanics of a stereotypical plantigrade quadruped, the horribilis Linnaeus 1758), including stride parameters, gaits and grizzly bear (Ginsburg, 1961), differ from those of more extensively analysis of three-dimensional ground reaction forces, with that of studied cursorial quadrupeds. previously studied quadrupeds. At slow to moderate speeds, grizzly The selection of gaits used by plantigrade and cursorial species bears use walks, running walks and canters. Vertical ground reaction could represent some of the locomotor differences observed forces demonstrated the typical M-shaped curve for walks; however, between these postures.
    [Show full text]
  • A Review on Face and Gait Recognition: System, Data and Algorithms 1 Haiping Lu, Jie Wang and Konstantinos N
    Contents 8 A Review on Face and Gait Recognition: System, Data and Algorithms 1 Haiping Lu, Jie Wang and Konstantinos N. Plataniotis 8.1 Introduction 1 8.1.1 Face recognition 2 8.1.2 Gait recognition 3 8.1.3 Organization 3 8.2 Face and Gait Recognition System Overview 4 8.2.1 Model-based face recognition approach 5 8.2.2 Model-based gait recognition approach 6 8.2.3 Appearance-based face recognition approach 7 8.2.4 Appearance-based gait recognition approach 7 8.2.5 Fusion of face and gait for recognition 7 8.3 Face and Gait Data Sets 8 8.3.1 The PIE database 8 8.3.2 The FERET database 9 8.3.3 The USF gait database 10 8.4 Face and Gait Recognition Algorithms 11 8.4.1 Linear subspace learning algorithms 11 8.4.1.1 Principal component analysis 12 8.4.1.2 Bayesian method 12 8.4.1.3 Linear discriminant analysis 13 8.4.2 Nonlinear kernel-based subspace learning algorithms 15 8.4.2.1 Kernel principal component analysis 18 8.4.2.2 Kernel-based discriminant analysis (KDA) 19 D R A F T April 8, 2008, 10:55am D R A F T ii CONTENTS 8.4.3 Multilinear subspace learning algorithms 20 8.4.3.1 Multilinear principal component analysis 22 8.4.3.2 Multilinear discriminant analysis 23 8.5 Concluding Remarks 24 8.5.1 Summary 24 8.5.2 Current state and future directions of face recognition 24 8.5.3 Current state and future directions of gait recognition 25 References 27 D R A F T April 8, 2008, 10:55am D R A F T 8 A Review on Face and Gait Recognition: System, Data and Algorithms Haiping Lu1*, Jie Wang2 and K.
    [Show full text]
  • Biometric System Using Gait Feature Analysis and Comparison
    Published by : International Journal of Engineering Research & Technology (IJERT) http://www.ijert.org ISSN: 2278-0181 Vol. 6 Issue 05, May - 2017 Biometric System using Gait Feature Analysis and Comparison Nishtha Gupta M. TECH (Mobile Pervasive Computing), IGDTUW, Delhi Abstract:- The world today is making rapid progress in its (health, age, size, weight, speed etc.) from its gait pattern. Gait quest to realize the dream of a creating a user friendly, analysis and recognition can form the basis of unobtrusive customer caring ambience. With every new dream comes the technologies for the detection of individuals who represent a nightmare of a security of the system lapse which may allow security threat or behave suspiciously. the misuse of the system. A major success in trying to bridge the advent of a security lapse is the use of biometrics. GAIT VERSUS OTHER BIOMETRIC TRAITS Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity Compared to other biometrics, gait has some unique verification. Biometric gait recognition (i.e. recognizing people characteristics. The most attractive feature of gait as a biometric from the way they walk) is one of the recent attractive topics trait is its unobtrusiveness, i.e., the fact that, unlike other in biometric research. This paper presents biometric user biometrics, it can be captured at a distance and without requiring recognition based on gait. the prior consent of the observed subject. Most other biometrics Most real-life biometric systems are still unimodal. Unimodal such as fingerprints [2], face [3], hand geometry [4], iris [5], voice biometric systems perform person recognition based on a [6], and signature [7] can be captured only by physical contact or single source of biometric information.
    [Show full text]
  • Towards Gait-Based Human Identification Using Front View Depth Images
    TOWARDS GAIT-BASED HUMAN IDENTIFICATION USING FRONT VIEW DEPTH IMAGES By HITESH JHURANI A thesis submitted to the Graduate School|New Brunswick Rutgers, The State University of New Jersey in partial fulfillment of the requirements for the degree of Master of Science Graduate Program in Electrical and Computer Engineering written under the direction of Prof. Janne Lindqvist and approved by New Brunswick, New Jersey October, 2016 ABSTRACT OF THE THESIS TOWARDS GAIT-BASED HUMAN IDENTIFICATION USING FRONT VIEW DEPTH IMAGES By HITESH JHURANI Thesis Director: Prof. Janne Lindqvist There are several competing approaches towards identifying humans based on their bio- metrics. In this thesis, we designed, implemented a system towards human front gait identification. We leveraged the depth information obtained from front view depth image of a person. We explain how basic features which can be extracted from the depth image of an individual can be used for identifying a person correctly. Basic features such as depth values of legs, the angle between the legs and height of the person are utilized. We conclude the thesis discussing tradeoffs of the approach and avenues for further research. ii Acknowledgments I would express my deepest gratitude to my advisor, Prof. Janne Lindqvist, for giving me the opportunity to be a part of this project and also for his constant guidance and support. I would like to extend a big thank you to the enthusiastic people in my lab especially Can Liu who helped me in the testing and other aspects of my thesis, without which it would have been difficult to complete my thesis.
    [Show full text]
  • Biometric Recognition by Gait: a Survey of Modalities and Features
    Computer Vision and Image Understanding 167 (2018) 1–27 Contents lists available at ScienceDirect Computer Vision and Image Understanding journal homepage: www.elsevier.com/locate/cviu Biometric recognition by gait: A survey of modalities and features T ⁎ Patrick Connor ,a, Arun Rossb a Stepscan Technologies Inc., 14 MacAleer Drive (Suite 1), Charlottetown, Prince Edward Island C1E 2A1, Canada b Department of Computer Science and Engineering, Michigan State University, 428 South Shaw Lane, 3142 Engineering Building, East Lansing,MI 48824, USA ARTICLE INFO ABSTRACT Keywords: The scientific literature on automated gait analysis for human recognition has grown dramatically over the past Gait biometrics 15 years. A number of sensing modalities including those based on vision, sound, pressure, and accelerometry Gait recognition have been used to capture gait information. For each of these modalities, a number of methods have been Features developed to extract and compare human gait information, resulting in different sets of features. This paper Silhouette provides an extensive overview of the various types of features that have been utilized for each sensing modality Ground reaction force and their relationship to the appearance and biomechanics of gait. The features considered in this work include Covariates (a) static and dynamic (temporal) features; (b) model-based and model-free visual features; (c) ground reaction force-based and finely resolved underfoot pressure features; (d) wearable sensor features; and (e) acoustic features. We also review the factors that impact gait recognition, and discuss recent work on gait spoofing and obfuscation. Finally, we enumerate the challenges and open problems in the field of gait recognition.
    [Show full text]
  • Dynamics of Human Gait Dynamics Of
    DDYNAMICSYNAMICS OFOF HHUMANUMAN GGAITAIT 2nd Edition ChristopherChristopher LL VaughanVaughan BrianBrian LL DavisDavis JeremyJeremy CC OConnorOConnor Dynamics of Human Gait (Second Edition) Christopher L Vaughan, PhD University of Cape Town Brian L Davis, PhD Cleveland Clinic Foundation Jeremy C OConnor, BSc (Eng) University of Cape Town Kiboho Publishers Cape Town, South Africa South African State Library Cataloguing-in-Publication Data Vaughan, Christopher L Dynamics of human gait / Christopher L Vaughan, Brian L Davis, Jeremy C OConnor Includes bibliographical references and index. 1. Gait in humans I Title 612.76 ISBN: 0-620-23558-6 Dynamics of Human Gait (2nd edition) by CL Vaughan, BL Davis and JC OConnor ISBN: 0-620-23560-8 Gait Analysis Laboratory (2nd edition) by CL Vaughan, BL Davis and JC OConnor First published in 1992 Copyright 1999 by Christopher L Vaughan All rights reserved. Except for use in a review, the reproduction or utilisation of this work in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including xerography, photocopying and recording, and in any information storage and retrieval system, is forbidden without the written permission of the publisher. The software is protected by international copyright law and treaty provisions. You are authorised to make only archival copies of the software for the sole purpose of backing up your purchase and protecting it from loss. The terms IBM PC, Windows 95, and Acrobat Reader are trademarks of International Business Machines, Microsoft and Adobe respectively. Editor: Christopher Vaughan CD Replication: Sonopress South Africa Text Layout: Roumen Georgiev and Narima Panday Software Design: Jeremy OConnor, Michelle Kuttel and Mark de Reus Cover Design: Christopher Vaughan and Brian Hedenskog Illustrations: Ron Ervin, Christopher Vaughan and Roumen Georgiev Printer: Mills Litho, Cape Town Printed in South Africa Kiboho Publishers P.O.
    [Show full text]
  • Gait Recognition Using Image Self-Similarity
    EURASIP Journal on Applied Signal Processing 2004:4, 572–585 c 2004 Hindawi Publishing Corporation Gait Recognition Using Image Self-Similarity Chiraz BenAbdelkader Identix Corporation, One Exchange Place, Jersey City, NJ 07302, USA Email: [email protected] Ross G. Cutler Microsoft Research, One Microsoft Way, Redmond, WA 98052-6399, USA Email: [email protected] Larry S. Davis Department of Computer Science, University of Maryland, College Park, MD 20742, USA Email: [email protected] Received 30 October 2002; Revised 18 May 2003 Gait is one of the few biometrics that can be measured at a distance, and is hence useful for passive surveillance as well as biometric applications. Gait recognition research is still at its infancy, however, and we have yet to solve the fundamental issue of finding gait features which at once have sufficient discrimination power and can be extracted robustly and accurately from low-resolution video. This paper describes a novel gait recognition technique based on the image self-similarity of a walking person. We contend that the similarity plot encodes a projection of gait dynamics. It is also correspondence-free, robust to segmentation noise, and works well with low-resolution video. The method is tested on multiple data sets of varying sizes and degrees of difficulty. Perfor- mance is best for fronto-parallel viewpoints, whereby a recognition rate of 98% is achieved for a data set of 6 people, and 70% for adatasetof54people. Keywords and phrases: gait recognition, human identification at a distance, human movement analysis, behavioral biometrics, pattern recognition. 1. INTRODUCTION (a well-advanced multidisciplinary field that spans kinesi- ology, physiotherapy, orthopedic surgery, ergonomics, etc.) 1.1.
    [Show full text]
  • The Systematic Review on Gait Analysis: Trends and Developments
    European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 07, Issue 06, 2020 The Systematic Review On Gait Analysis: Trends And Developments Nithyakani P1, M Ferni Ukrit2 Assistant Professor, School of Computing, SRMIST. [email protected] [email protected] ABSTRACT -The research on gait analysis has a drastic growth in recent years for various applications like authentication, video surveillance, health monitoring system and animation. Gait recognition is an emerging intelligent system because of its recognition at a distance and it works well with low resolution videos. This paper provide the various gait features extracted in model based approach and model free approach. This paper describes the survey of gait recognition techniques which include the four major steps such as data acquisition, preprocessing, feature extraction and classification. Descriptions about wearable sensor, Vision base and pathological database is summarized. In recent years, performance of human recognition has been impressively enhanced by deep architectures. This paper offers an up to- date survey of deep architectures on gait recognition, mainly focusing on the performance of convolutional neural network combined with other architectures. Furthermore, general challenges faced in gait recognition and directions for future research are discussed. 1. INTRODUCTION Human analysis and identification has been an attractive technology in many fields.In the digital imaging, a human can be analyzed by his/her unique feature of face, iris, sweat, hair, periocular region of eyes,Gait, smell, finger and palm[18]. Gait defines the style of walking. Human Gait analysis is a study of human movement to analyze the style of walking and its related features which are applied in the field of biometrics, surveillance, diagnosis of gait disease, therapy and rehabilitation, etc.
    [Show full text]
  • Based Techniques for Gait Recognition
    A COMPREHENSIVE REVIEW OF PAST AND PRESENT VISION- BASED TECHNIQUES FOR GAIT RECOGNITION Tracey K. M. Lee, e-mail: [email protected] School of EEE, Singapore Polytechnic Mohammed Belkhatir, e-mail: [email protected], phone: +33472692194 Faculty of Computer Science, University of Lyon, France Saeid Sanei, e-mail: [email protected], phone: +441483684615 Department of Computing, University of Surrey Full reference: TKM. Lee, M. Belkhatir, S. Sanei: A comprehensive review of past and present vision-based techniques for gait recognition. Multimedia Tools Appl. 72(3): 2833-2869 (2014) Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement.
    [Show full text]