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"fatemeh dehghani" <fatemeh_dehghani2001@yahoo.com> wrote in message <jngkrn$ik8$1@newscl01ah.mathworks.com>...
> "Dilli Babu" <dillibab@yahoo.co.in> wrote in message <ieepjm$bns$1@fred.mathworks.com>...
> > hi dear friends, i'm doing gait based gender classification as my final year project. I dont know much information about matlab. So i need matlab code for gender classification based on gait.
> > Thanks in advance.....
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First, learn as much as you can about MATLAB. (There are several introductory books. Experiment freely; the world will not come to an end if your MATLAB routines generate errors.) Second, if you're doing anything in mathematical biology (your final year project qualifies as such), it will help greatly to be proficient in MATLAB, linear algebra, linear and nonlinear differential equations, and stochastic processes.
Your question intrigued me, so I did a 'PubMed' search on it. I'm not sure generic MATLAB code exists for what you want, especially since that code likely depends on the data you have or the experiment you will design, but from what I read in the abstracts (three of whiich I include below; you'll have to go to your university library to download the full articles), MATLAB certainly has the ability to do this analysis. It's quite possible that the authors of these papers used MATLAB in their research, and given your time constraints, might be willing to give you some suggestions on how best to approach your problem. (If they do, please be sure to acknowledge them in your results.)
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1. Gait Posture. 2012 Feb 1. [Epub ahead of print]
Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics.
Maurer C, Federolf P, von Tscharner V, Stirling L, Nigg BM.
Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
Abstract
Changes in gait kinematics have often been analyzed using pattern recognition methods such as principal component analysis (PCA). It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns. However, while subtle changes in gait pattern (for instance, due to different footwear) may not change main movement patterns, they may affect movements represented by higher principal components. This study was designed to test two hypotheses: (1) speed and gender differences can be observed in the first principal components, and (2) small interventions such as changing footwear change the gait characteristics of higher principal components. Kinematic changes due to different running conditions (speed - 3.1m/s and 4.9m/s, gender, and footwear - control shoe and adidas MicroBounce
shoe) were investigated by applying PCA and support vector machine (SVM) to a full-body reflective marker setup. Differences in speed changed the basic movement pattern, as was reflected by a change in the time-dependent coefficient derived from the first principal. Gender was differentiated by using the time-dependent coefficient derived from intermediate principal components. (Intermediate principal components are characterized by limb rotations of the thigh and shank.) Different shoe conditions were identified in higher principal components. This study showed that different interventions can be analyzed using a full-body kinematic approach. Within the well-defined vector space spanned by the data of all subjects, higher principal components should also be considered because these components show the differences that result from small interventions such as footwear changes.
Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.
PMID: 22304784 [PubMed - as supplied by publisher]
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2. Hum Mov Sci. 2011 Sep 16.
Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis.
Helwig NE, Hong S, Polk JD.
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820-6232, USA; Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820-5710, USA.
Abstract
Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three- or higher-mode data. Using three-mode joint angle waveform data (subjects×time×joints), we demonstrate Parafac's ability to (a) determine interpretable
components revealing the primary interrelationships between lower-limb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).
Copyright © 2011 Elsevier B.V. All rights reserved.
PMID: 21925756 [PubMed - as supplied by publisher]
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3. Clin Biomech (Bristol, Avon). 2008 Dec;23(10):1260-8. Epub 2008 Sep 6.
Gender differences in walking and running on level and inclined surfaces.
Chumanov ES, Wall-Scheffler C, Heiderscheit BC.
Department of Orthopedics and Rehabilitation, Physical Therapy Program, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706-1532, USA.
Abstract
BACKGROUND: Gender differences in kinematics during running have been speculated to be a contributing factor to the lower extremity injury rate disparity between men and women. Specifically, increased non-sagittal motion of the pelvis and hip has been implicated; however it is not known if this difference exists under a variety of locomotion conditions. The purpose of this study was to characterize gender differences in gait kinematics and muscle activities as a function of speed and surface incline and to determine if lower extremity anthropometrics contribute to these differences.
METHODS: Whole body kinematics of 34 healthy volunteers were recorded along with electromyography of muscles on the right lower limb while each subject walked at 1.2, 1.5, and 1.8m/s and ran at 1.8, 2.7, and 3.6m/s with surface inclinations of 0%, 10%, and 15% grade. Joint angles and muscle activities were compared between genders across each speed-incline condition. Pelvis and lower extremity segment lengths were also measured and compared.
FINDINGS: Females displayed greater peak hip internal rotation and adduction, as well as gluteus maximus activity for all conditions. Significant interactions (speed-gender, incline-gender) were present for the gluteus medius and vastus lateralis. Hip adduction during walking was moderately correlated to the ratio of bi-trochanteric width to leg length.
INTERPRETATION: Our findings indicate females display greater non-sagittal motion. Future studies are needed to better define the relationship of these differences to injury risk.
PMID: 18774631 [PubMed - indexed for MEDLINE]
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