Performance Evaluation of SVM based Abnormal Gait Analysis with Normalization

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 5, 2010

Performance Evaluation of SVM based
Abnormal Gait Analysis with Normalization
M. Pushpa Rani1 G.Arumugam2
1
Associate Professor in Computer Science, 2
Professor & Head, Dept. of Computer Science,
Mother Teresa Women’s University, Madurai Kamaraj University,
Kodaikanal, Tamil Nadu, India Madurai, Tamil Nadu, India

Abstract—Support Vector Machine classifiers are powerful tools gait data to describe the human motion; walking speed, joint
that are specifically designed to solve large-scale classification angles, forces, and moments etc., Data like joint kinetics, joint
problems. In 1990s, Vapnik along with a group of other moments and joint powers have also been used for gait
mathematicians and scientists developed a new statistical
recognition. Also this technology is very useful for checking
approach that is more efficient particularly in dealing with large
the walking pattern of children as Children under the age of 13
classification problems which they called as Support Vector
Machines (SVM). An SVM method is being broadly used in gait have more chances to have different style of walking. To trace
analysis because of its remarkable learning ability. In this paper, out any abnormality in children’s walk, our proposed method
a two stage SVM algorithm is proposed for children abnormal takes a key role; and with this one could diagnose any existing
gait analysis. The algorithm uses T-Test based preprocessing fault features of walking in early ages of childhood itself. This
methods for feature selection, normalization and combines SVM will surely of great help for earlier treatment of gait
for Classification. Only samples that have weak relationships abnormality in children.
with all the clusters are involved in SVM. Experimental results An SVM method has been broadly used in gait analysis
reveal that this algorithm based on T-Test-SVM combination
because of its remarkable learning ability, accuracy and
achieves a remarkable recognition performance for children
efficiency. In this paper the SVM technique is performed by
abnormal gait analysis with reduced Computational cost.
having a training set and test samples. The training set is
Keywords Abnormal Gait Analysis, Support Vector Machine categorized into different sets of conditions, which can be
(SVM), Gait Data Classification, T-Test grouped into two classes i.e. normality and abnormality. For
this, the collection of data is very important and several
observations are needed. The information regarding leg length,
I. INTRODUCTION
height, cadence, stride length and age are some of the features
GAIT analysis is very significant for early diagnosis of gait which are of great help in this gait analysis. The SVM
diseases and treatment assessment. Doctors in earlier days constructs a hyper plane or a set of hyper planes in a high or
used to diagnose gait diseases manually with the help of infinite dimensional space, which can be used for
certain graphs generated by the gait analysis system, with classification or clustering. In simple words, given a set of
which only vague clues which may or may not reflect the training examples, each with a label of belonging to one of
reality were obtained. In most cases, doctors had different these categories, an SVM training algorithm builds a model
views of opinion by interpreting these curves. As machine that predicts in which category a new example falls. The
based learning technology has developed, it has gained much proposed method uses t-test-SVM for classification.
interest in gait analysis, which is of great support to doctors Interestingly, a good separation is achieved by the hyper plane
for more reliable and accurate diagnosis of a disease. A Gait that has the largest distance to the nearest training data points
analysis is a systematic study of human motion. i.e., walking, of any class. The t-test method is used to normalize data prior
running, skipping and the like, which mainly concentrates on to classification. In its simplest form t-test provides a
the physical activities. Gait analysis is very much useful to statistical analysis of means of several groups and therefore
check out the human conditions; whether normal or abnormal can generalize the Student's two-sample t-test to more than
using the eye and brain of the observers, augmented by two groups.
instrumentation for measuring body movements, body The following section of this paper is projected as follows:
mechanics and the activity of the muscles. In other words, gait Section 2 discusses some of the related works done earlier in
analysis is used to assess, to plan and to treat the individuals gait based classification. The proposed t-test SVM gait
with conditions affecting their ability to walk. There exist a lot classification method is described in Section 3. Section 4
of methods to check out these normality and abnormality on illustrates the performance Analysis and Section 5 concludes
human gait, but SVM is found to be more suitable in terms of the paper with directions for future work.
its efficiency [2]. Motion analysis provides large volume of

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II. RELATED WORK J.H. Yoo et al., [18] had described an automated gait
The study of human gait has created much interest in many recognition system using back propagation neural network
application areas including biomechanics, clinical analysis, algorithm. Gait is the most common human motion, and each
computer animation and biometrics. As a result many person appears to have his or her own characteristic gait
researches emerged in recent years and of which, a few studies pattern. To identify the human gait, a total of 27 parameters
related to Gait classification are noted herewith. are considered as gait features. By calculating a class
C. Bauckhage et al., [13] well thought-out about automatic separability of the given feature, only 10 important features for
gait analysis as a means to deduce if an observed walking classifying the gait are selected from these feature sets. Then,
pattern appears to be normal or not. When compared to most the enhanced back-propagation neural network algorithm is
contributions to visual gait analysis, the problem dealt with the applied to the SOTON database, and recognition rate of 90%
paper requires a representation that abstracts from individual for 30 subjects is accomplished. The results achieved give
gait characteristics but allows for the classification of gait promising performance and higher recognition rates than those
across individuals. Addressing this requirement, the author of an earlier gait recognition approach.
presented a homeomorphism between 2D lattices and shapes Ju Han et al., [19] proposed a new spatio-temporal gait
that enables a robust vector space embedding of silhouettes. representation, called the Gait Energy Image (GEI), for
Sampling apt lattice points allows to roughly track the individual recognition by gait. Different from other gait
movement of limbs without requiring any limb recognition representations which consider gait as a sequence of templates
strategy. Combining shape representations obtained from (poses), GEI represents human motion sequence in a single
several frames into lager feature vectors provides temporal image while preserving temporal information. To overcome
context for the classification task. Experimental results expose the limitation of training templates, a simple model is
a complete knowledge that gait classification using support proposed for simulating distortion in synthetic templates and a
vector machines yields excellent accuracy. Temporal filtering statistical gait feature fusion approach for human recognition
of the results of classification in further improvements of the by gait. Experimental results show that a) GEI is an effective
reliability of the presented framework, because it lessens the and efficient gait representation and b) the proposed
effect of sporadic misclassifications. recognition approach achieves highly competitive
A.H. Khandokerl et al., [3] demonstrated the effectiveness performance with respect to the published major gait
of wavelet based multi scale correlation exponents of MFC as recognition approaches. This paper presents a methodical and
features for automated screening an individual subject of comprehensive gait recognition approach, which can work just
proper balance control as being within healthy ranges, or as fine as other complex published techniques in terms of
having high enough risk to be categorized as a falls risk or a effectiveness of performance while providing all the
faller by using SVM. Findings of that study were based on a advantages associated with the computational efficiency for
small sample of impaired subjects, compared to a relatively real-world applications.
small sample of healthy peers. Therefore, further validation of Shakhnarovich et al. combined the face and MV-based gait.
the relative risk estimation task is suggested in a larger, more The front face was captured by one camera and the side-view
diverse sample of healthy and balance impaired falls risk in of the person was captured by another camera. Face-alone,
elderly adults, which may subsequently lead us to make more gait-alone and combined face and gait recognition rates were
robust automated diagnostic model of falls risk estimation. 80%, 87%, and 91%, respectively. Zhou et al. [21] used a
The significance of this study is that it provides an early single camera to capture both face and gait. Recognition rates
estimation of relative falls risk in the elderly that holds great for face and gait separately were 64.3% and 85.7%, a single
potential for indicating balance improving interventions to respectively. Conversely, when they were combined, the
reduce their relative risk of falls. recognition rate increased up to 100% [23]. In [22], WS-based
Jian Ni et al., [17] had a look about gait recognition, which gait recognition was combined with speaker verification.
is simulated in the small and medium-scale gait database. Performance proved to be appreciably better in a noisy
Higher recognition rate and faster recognition speed of the environment, compared to when speaker verification was used
algorithm are verified. The reason that this algorithm obtains alone. The EER was in the range of 2%-12%, less than half of
superior test results is: The paper adopts support vector the EER of individual modalities. In this group, gait is
machine based on hybrid kernel function. This method makes captured using a video-camera from distance. Video and
that the SVM model has better generalization ability. In the image processing techniques are employed to extract gait
method of parameter selection, the text uses the objective features for recognition purposes.
function and combines OPS algorithm to select the best kernel BenAbdelkader et al. [25] used stride and cadence for
parameter. The way combines the advantages of objective person identification and verification. Johnson and Bobick
function and PSO algorithm to optimize SVM parameters. It [26] extracted static body parameters such as the height, the
significantly improves the optimization speed, at the same distance between head and pelvis, the maximum distance
time obtains a good optimization effect. between pelvis and feet, and the distance between feet, and

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used them for recognition. Most of the MV related gait
recognition algorithms are based on the human silhouette [27,
28]. That is the image background is detached and the
silhouette of the person is extracted and analyzed for
recognition, For example, Liu and Sarkar [27] computed the
average silhouettes over a gait cycle, and used the Euclidean
distance between them to compute similarity.

III. METHODOLOGY
The block diagram of the proposed system is shown in
Figure1. After suitable preprocessing, the Salient Gait
Features are extracted from the possible Gait Signatures.
These Gait features are then subjected to t-test normalization There are k classes.
and subsequently to SVM Classifier.

Preprocessing Deriving Gait Gait Feature
Gait Images Signature Extraction
is the maximum of all k. Ck refers to class k that includes
, samples is the expression value of feature i in sample j
is the mean expression value in class k for feature i. where
SVM Classifier Feature Statistical n is the total number of samples. xi is the general mean value
Normal/ Normalization based Feature for feature i. Si is pooled within-class standard deviation for
Abnormal Selection feature i. In fact, the TS used at this point is a t-statistic
between the centroid of a specific class and the overall
Figure1: Proposed Architecture centroid of all the classes. Another possible model for TS
A. Statistical Methods for Gait Feature Selection & could be a t-statistic between the centroid of a specific class
Normalization and the centroid of all the other classes.
Two samples are given as input to the T-Test. The paired t-
The main objective of feature selection is to discover a subset
test determines whether input features differ from each other
of features, satisfying certain criteria. In pattern recognition,
in a significant way under the assumptions that the paired
recognition metric will be the classification accuracy or
differences are independent and identically normally
inversely the classification error. But direct minimization of
distributed. This gives a clear view for Abnormal Gait
the classification error cannot be analytically performed, so a
Analysis
wide range of alternative statistics that are easier to evaluate
are performed. The typical measure used in the gait feature
2. PCA (Principal Component Analysis)
selection is introduced as follows:
Principal Component Analysis (PCA) involves a
1. T-Test
mathematical process that transforms a number of possibly
The t-test finds whether the means of two groups are
correlated variables into a smaller number of uncorrelated
statistically dissimilar from each other. This analysis is
variables called principal components. The fallouts of a PCA
appropriate to compare the means of two groups, and
are usually discussed in terms of component scores and
especially appropriate for the analysis of the two-group
loadings.
randomized experimental design.
PCA is the simplest of the proper eigenvector-based
multivariate analyses. Often, its process can be thought of as
The t-score (TS) [31] of feature i is defined as follows:
revealing the internal structure of the data in a way which best
explains the variance in the data. If a dataset (multivariate) is
visualized as a set of coordinates in a high-dimensional data
space (1 axis per variable), PCA supplies the user with a
Where, lower-dimensional picture, a "shadow" of this object when
viewed from its (in some sense) most informative viewpoint.
For a data matrix, with zero empirical mean (the empirical
mean of the distribution has been subtracted from the data set),
where each row represents a different repetition of the

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experiment, and each column gives the results from a ⎧n
particular probe, the PCA transformation is given by: ( )
sgn ⎨∑ ai∗ y ∗ k x, xi∗ + b}
j
⎩ i =1

=V C SVM kernel functions
This work is the first attempt to test the classification ability of
feature combinations in gait applications. We have used three
where the m-by-n diagonal matrix Σ is an nonnegative real
main kernel functions for our study here. The Partial kernel
numbers on the diagonal and W Σ VT is the Singular Value
Decomposition (SVD) which is factorization of a rectangular function, which only influence to data near the test points and
real or complex matrix of X. the kernel function more applied here is the Radial Basis
Function:

B Support Vector Machine {
k (x, x k ) = exp − x − x k
2
}
2δ 2 ,
where δ is the width of the gaussian kernel. The overall
2
The support vector machine is used as a classifier in the paper.
SVM is the one of the best linear classification method and kernel function which allows that, the data away from the test
kernel mixed applications. The SVM transforms the samples points will also have impact to kernel function. It is the
to high-dimension space by the kernel mapping, and then get polynomial kernel to be more suitable in this case:
the best linear classification surface of samples in this new
space. This Non-linear transformation is achieved by k(x, xk)=[(x, xk)+1]d,,
appropriate inner product function. The best linear
classification surface function of characteristics space can be where d is degree of polynomial.
described by the formula:
IV. EXPERIMENTAL RESULTS
g ( x ) = Σ a j y i k ( x, xi ) + b
n

j =i This study mainly deals with the performance analysis of
the T-Test based SVM classification method for gait normality
Where (xi, yi) are the two types of sample collection divided and abnormality. In this section, several experiments are
in the sample space, b is the classification threshold, and k(x, carried out to test the validity of T-Test based SVM. A
xi) is being the nonlinear kernel function that replaces comparative analysis is also done for the proposed T-Test with
characteristics space and meets Mercer conditions. Ascertain PCA (Principal Component Analysis). The experimental data
the best linear classification surface function is got by striking used in this study are obtained from the gait database of
the best resolve ai where i = 1, 2,…,n of the following function Virginia University [11]. There are totally 158 gait samples
Q(a). present in the database and all these samples are used for this
experiment. These samples belong to 68 children with normal
g (x ) = Σ a j y i k
n

j =i gait and 88 children with abnormal gait affected with cerebral
palsy (CP). The ages of these children range from 2 years to
13 years. Four features of gait samples are selected for
− 0 .5 ∑ ∑ a i a j y i y j k (x i , x j )
n n n
max Q (a ) = ∑a i classification and they are stride length, cadence, leg length
a
i=0 i=0 j =0 and age.
n
In this study, the t-test is applied to normalize the gait
∑yai =1
i i = 0, i = 1,2,..., n samples. Figure 2 shows the distribution of samples before and
after normalization. As shown in Figure 2, the overlap of two
0 ≤ ai sample sets is effectively reduced after normalization, which
helps to improve the classification accuracy. Three kernel
The above equation is solving of quadratic function extreme functions are used to build SVM classifiers in this study. By
value on condition that inequality, Q (a) is convex comparing the classification results of three classifiers, the
function. Because its local optimal solution is global optimal most suitable kernel function may be decided for t-test-SVM.
solution, the solution is unique. Thus the best classification They are Radial Basis Function (RBF), linear and the
function of SVM is: polynomial. The RBF has best accuracy rate when compared
⎧n to the other kernels such as the linear and the polynomial. In
f ( x ) = sgn ( g ( x )) = sgn ⎨∑ a j y j k (x, xi ) + b} general, the RBF kernel is a reasonable first choice.
⎩ j =1

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(a) (b)

Figure 2: (a) Distribution of Raw Data Before Normalization. (b) Distribution of Raw Data After Normalization.

This kernel nonlinearly maps samples of interest in a higher So, the linear kernel order d is set to be 1 in the following
dimensional space so that, unlike the linear kernel, it can experiments. When d is 1, the polynomial kernel function
handle the case when the relation between class labels and actually is a linear kernel function with accuracy of 90.12%.
attributes is nonlinear. Furthermore, the linear kernel is a But in case of the RBF kernel with σ = 4, c = 100,the absolute
special case of the RBF since the linear kernel with a penalty accuracy rate is of 98.15%, which leads the other two kernels.
parameter C has the same performance as that of RBF kernel When radial basis function (RBF) is applied, the kernel
with some parameters. In addition, the sigmoid kernel behaves parameter σ in the RBF and the regularization parameter C
similar to the RBF for certain parameters. The next reason is
the number of hyper parameters that influences the complexity may impact the classification accuracy of T-Test-SVM. Figure
of model selection. The polynomial kernel has additional 4 elaborately shows the relationship between classification
hyper parameters than that of the RBF kernel. accuracy and parameters combination (d, C).
Finally, the RBF kernel has fewer numerical difficulties.
One key point should lie between 0 and 1, in contrast to
TABLE 1: BEST ACCURACY ACHIEVED FOR KERNEL
polynomial kernels and linear kernel of which kernel values FUNCTIONS
may go to infinity or zero, while the degree is large. Moreover,
that the sigmoid kernel is not valid product of two vectors,
under some parameters and conditions.
There are a few situations where the RBF kernel is not Kernel Function Parameters Accuracy (%)
suitable. In meticulous, when the number of features is very Linear d=1 90.12
large, one may just use the linear kernel. Polynomial d=1 85.69
RBF σ=4, C=100 98.15
By applying this kernel function, the accuracy of t-test -SVM
and PCA SVM are compared at the end of this section. The classification accuracy of the RBF kernel function rates
Polynomial order d is an important parameter when high of order 98%, this is shown in figure 4 for all the three
polynomial kernel function is applied in t-test-SVM. The cases of C=1, C=10, C=100.
classification accuracy of gait samples by using different The Figure reflects that, the generalization capability of the
polynomial order is shown in Figure 3, and d is chosen from 1 SVM enhances along with the increase of C. This is because
to 10. As shown in Figure 3, the classification accuracy the regularization parameter C may adjust the ratio of
declines along with the increase of polynomial order. This is confidence interval and empirical risk.
since the aspect of the feature space is high under a large
polynomial order and it leads a declining generalization
capability of SVM.

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These analysis reveals that T-Test have accuracy rate of
98.15%, while that of the PCA is comparably low.

Figure 3, Classification Accuracy and Polynomial Order

Figure 4, Classification Accuracy for Various Parameter Combinations
TABLE 2: AVERAGE ACCURACY FOR VARIOUS σ & C VALUES

σ C=1 C=10 C=100 V. CONCLUSION
0 20.12 54.21 92.07 In this paper Abnormal Gait Analysis is done using the SVM
1 35.08 63.75 94.29 with T-Test Combination. The results are compared with the
2 54.82 78.64 94.38 other existing methods based on their Classification accuracy.
3 65.54 85.11 95.93 An automated abnormal gait classification system is described
using machine learning techniques. To achieve this, the gait
4 75.12 90.39 96.17
signature has been extracted by combining a statistical
5 90.43 92.42 97.59 approach and machine learning based analysis is further done
6 92.54 92.78 97.26 using the anatomical knowledge. For the derived gait
7 93.72 96.64 98.07 signatures, the motion parameters were calculated, and the gait
8 92.05 96.89 98.15 features based on the motion parameters were extracted. The
T-Test based SVM classifier is used to analyze the
9 92.34 96.92 98.15
discriminatory ability of the extracted features. The result of
10 92.16 96.11 98.05 the proposed method has produced very good classification
rate which exceeds 98%. As such, the automated abnormal
In this case, the SVM has almost no change of the empirical classification system not only accords with quantitative
risk and generalization capability. Table I shows the analysis in results, but also confirms distinctiveness as normal
classification accuracy of the three classifiers. and abnormal gait. Hence this gait classification for medical
diagnosis would be a real boon as its convenience will surely
TABLE 3: BEST ACCURACY ACHIEVED FOR VARIOUS METHODS benefit the children and also the elderly. The drastic
development of computer vision techniques also ensures that
Algorithm Accuracy (%) the clinical gait analysis put into practical realization may
Std SVM 87.68 gradually be achieved.
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Intelligence, Vol. 28, No. 6, June 2006.
[15] L. Lee, G. Dalley, and K. Tieu, “Learning Pedestrian Models for Ms.M.Pushpa Rani is an Associate Professor in Computer Science at
Silhouette Refinement,” Proc. Int’l Conf. Computer Vision, pp. 663- Mother Teresa Women’s University, Tamil
670, 2003. Nadu, India. She received her Master’s
[16] A. Veeraraghavan, A.R. Chowdhury, and R. Chellappa, “Matching degree in Computer Applications from
Shape Sequences in Video with Applications in Human Movement Bharathiar University, Coimbatore, India,
Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. and is currently pursuing doctoral Research
27, no. 12, pp. 1896-1909, Dec. 2005. in Madurai Kamaraj University, Madurai,
India. She has published many articles in
[17] Jian Ni, Libo Liang, " Gait Recognition Method Based on Hybrid Kernel
International Journals and more than 30
and Optimized Parameter SVM", IEEE International Conference on
papers in National and International
Computer Science and Information Technology, 2009. ICCSIT 2009.
Conferences. Her current research areas
[18] Jang-Hee Yoo, Doosung Hwang," Automated Human Recognition by include Image Processing, Biometrics and
Gait using Neural Network", IEEE Conference on Image Processing Adaptive Learning System.
Theory, Tools & Applications, 2008.
[19] Ju Han, Bir Bhanu, "Individual Recognition Using Gait Energy Image",
Dr G Arumugam is a senior Professor in the Department of Computer
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.
Science, Madurai Kamaraj University, Madurai, He did his Masters
28, No. 2, February 2006.
degree in Applied Mathematics specializing in Computer Science in the
[20] G. Shakhnarovich, L. Lee, and T. Darrell. Integrated face and gait PSG College of Technology, Coimbatore in the year 1980. He started
recognition from multiple views. In Proceedings of the IEEE Computer his research carrier from the Department of Mathematics, IIT Kanpur in
Society Conference on Computer Vision and Pattern Recognition., 2001. 1981.He got his Doctorate degree from the University of Pierre and
[21] Xiaoli Zhou, Bir Bhanu, and Ju Han. Human recognition at a distance in Marie Curie , France in 1987.He was a Post-Doctoral Fellow in the
video by integrating face profile and gait. In 5th International University of JYVASKYLA, Finland for a period of three months.
Conference on Audio- and Video-Based Biometric Person He worked in the Hexaware Info Systems,
Authentication, pages 533–543, July 2005. Chennai as a Project Manager and as a
[22] Elena Vildjiounaite, Satu-Marja Makela, Mikko Lindholm, Reima Consultant in the Polaris Software, Chennai
Riihimaki, Vesa Kyllonen, Jani Mantyjarvi, and Heikki Ailisto. to gain industrial experience.. He was in
Unobtrusive multimodal biometrics for ensuring privacy and Singapore for a period of 2 ½ years as
information security with personal devices. In Pervasive, pages 187– Visiting Professor in the Ngee Ann
201, May 2006. Springer LNCS. Polytechnic, Singapore from July 2000 to
[23] G. Shakhnarovich, L. Lee, and T. Darrell. Integrated face and gait Nov.2002.He has published several papers
recognition from multiple views. In Proceedings of the IEEE Computer in the international and national journals and
Society Conference on Computer Vision and Pattern Recognition. 2001. in the conference proceedings. His area of
research is design and analysis of
[24] Davrondzhon Gafurov, "A Survey of Biometric Gait Recognition: algorithms, data mining, and cryptography
Approaches, Security and Challenges", NIK-2007 conference. and network security.

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ISSN 1947-5500