the systematic review on gait analysis: trends and

European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 07, Issue 06, 2020

1636

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. Gait

recognition can be performed through involvement and non-involvement of a person to the

recognition system. In human involvement approach, person is directly contacted to the recognition

system with the help of different sensors, accelerometer or monitoring devices.Non-involvement

approach uses camera at a distance to identify a person identity with or without their knowledge based

on its application. Gait analysis is prevalent than other features since thefeatures can be extraction

from low resolution and without the cooperation of any individual.

“On the Gait of Animals” is the first book to report the gait analysis written by Aristotle in 350BC[1].

In 1680, Giovanni Alfonso Borelli made the extensive study on the animal locomotion mechanism. In

1890, Christian Wilhelm Braune and Otto Fischer performed the investigation of gait activity over

human and published a series of papers. They have extended their research on the center of gravity,

moments of inertia of the human body and its segments. In 1880s Eadweard Muybridge and Étienne-

Jules Marey developed the animated photography to capture the several movement phases in a

photographic surface. Muybridge used multiple cameras to record the locomotion and presented

through his public lectures and demonstrations. In 1970s, video camera systems were available to

study the several human pathological conditions. Human individuals were recognized with their gait

style in 1970s. Later gait analysis was impressed by the forensic and animation experts [3-5].

Advancements in technologies of artificial intelligence and computing,Gait recognition has become

the most popular data driven and artificial surveillance tool which can recognize the human at a

distance. This survey aims to highlight the technologies developed for gait analysis. Section II

provides the application of gait recognition in various fields. Section III describes about the human

gait features considered for model based and appearance based recognition. Section IV focuses on the

role of machine learning algorithms in recognizing human. Section V presents the description and

https://en.wikipedia.org/wiki/Christian_Wilhelm_Braune


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comparison of recently implemented deep architecture which gives the high accuracy. Section VI

discusses about future directions in the field of human gait analysis andSection VII concludes with the

challenges and the future work.

2. APPLICATIONS OF GAIT RECOGNITION

2.1. Application-Based Systems

Authentication is necessary to avoid violation in secured protected areas, home, public security,etc,.

Authentication processes[3] are carried out in three methodologies such as knowledge based

(credentials, PIN, MPIN), object or token based (E.g bank card, identity card etc.,) and biometric

based. Knowledge based authentication process faces memorability issues specifically robust

passwords. Security breach arises when the password is reused in many services. Object based

method usually combined with knowledge based method for accessing the services provided with

physical Keys. Biometric authentication is used to identify a person by his/her unique features like

fingerprints, face, and periocular region of eye, retina, palm, sweet, body odor and gait. These features

are unique in all subjects and also remain stable for a specific period. Behavioural features are

difficult to imitate. Gait recognition method includes computer vision techniques, signal analysis,

motion estimation, processing the video data, and ubiquitous applications.

2.2. Pattern Recognition

Biometrics is divided into physical and behavioral features. Physical features are the pattern of height,

structure of face, retina, eye etc. Physical features has universality, unique features for a long term

period. Behavioural features includes the way of walking, speaking, singing, movement etc,.Though

behavioral features varies on emotional factor, difficult to forge or imitate the individuality. A set of

biometric images are recorded for verifying the similarity of captured image. Due to the recognition

accuracy in various capturing modes, gait recognition has the highest popularity among the other

biometric approach.

2.3. Life And Medical Sciences

Gait recognition helps to identify the quality of walking of an individual with several common gait

disease such as parkinson’s disease , multiple sclerosis, cardiopathies, post-stroke Hemiplegic gait,

spastic diplegic, neuropathic, myopathic, choreiform, ataxic (cerebellar) and sensory [9-10]. Study of

the gait sequences helps for early diagnoses of their musculoskeletal and neurological complications

to provide the best treatment[62]. Kinematic and kinetic data are extracted from gait analysis helps

the clinicians to interpret, assess, and evaluate the individual gait disorder. Data collection is

performed by videotaping the individual before the examination, examining physically to analyze the

muscle strength and tone, motion of joint, abnormalities in bone, stubborn muscular contractions,

knee and ankle width, distance between right and left sacroiliac spine, etc.

2.4. Animation

Animation of human gait is focused by the fields of biomechanics, signal processing, kinematics,

dynamics and robotics[6-7,11]. Interest in human gait animation is growing rapidly in the Computer

Animation Community despite of the complex techniques. The first computer animation approach for

human was developed in 1970s which depicts the early sequential pictorial representation of series.

Later Humanoid animation(H-Anim) standard was approved in 1990s and greatly influenced by the

research experts of gaming,movies, virtual avatars, graphics, ergonomics and simulations industry.

The challenge in human animation is to simulate the natural motion style into the virtual

characteristics. Animation is performed by the motion sequence extraction of skeleton representation

of human connected hinges points like shoulder, neck , elbow, waist, knee and ankle . Higher quality

is obtained by modelling the skeleton with surfaces. Methods of human gait animation are knowledge

based kinematic animation, dynamic animation[61] and interactive synthetic walking motion data.


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3. Gait Recognition Features

Gait is a synchronized movement of bones, muscles and nerves of hip, knee and ankle joints rotations.

The cyclic nature of the walking style provides the periodic leg movement. Gait of a person is

generally represented as a walk cycle describes in the Figure 1which has two phases: stance phase and

swing phase ; gait cycle has eight stages[2] as (i) initial contact of the foot or toe. (ii) Loading

response as placing the entire foot over the floor with the limb segments reaction. (iii) Mid-stance as

the intermediate support. (iv) Terminal stance is denoted as heel off. V) Pre-swing is the initialization

of knee movement. VI) Initial Swing .Parameters extracted from gait sequence are step length, stride

length, cadence, speed, dynamic base, progression line, foot angle, hip angle and squat performance.

The measurements extracted were analyzed, interpreted to recognize the individual through the

techniques like temporal or spatial, kinematics, Marker less gait capture, pressure analyzer, kinetics

and dynamic electromyography.

Fig. 1.Asingle Gait cycle is also termed as stride. Stance phase begins with the heel strike and toe off

the same foot. Average seconds of stance phase is 0.64s. Swing phasebegins with another foot heel

strike and toe off. Average seconds of swing phase is 0.40s.A stride can also be represented with 8

stages: Initial contact, loading response, mid stance, terminal stance, pre-swing, initial swing, mid

swing, terminal swing.

Human gait recognition system depends on the video has two major categories as model free gait

recognition and model based gait recognition. Model based approaches extract the structural pattern

of model from human body using various methodologies such as pendulum, leg inclination, stride,

trajectories, joint angles, 2D and 3D temporal models [19-22] with their length, width and position

dimensions. It helps to track the individual with set of mathematical parameters and its quantifiable

relationships. Model based approach used in handling self-occlusions. Issues faced in Model based

approach were viewpoint invariant, physiological changes such as age, weight, height , walking

speed,etc., and external factors like clothes , foot wear, load , etc., and environmental factors.In

model based approach kinematics track joints of an individual are head, shoulder, spine, foot, knee,

ankle, thumb, neck, hand,etc shown in the Figure 2a.It also defines the dynamics or kinematics of the

body movements in the terms of gait cycle time, stride length, Fourier magnitude, footstep , ground

reaction force, pressure mat features, etc, Model based gait recognition utilizes spatio temporal

volume, linear regression analysis, trignometric-polynomial interpolant functions, biped locomotion

feature , Principal component Analysis, canonical analysis, 1NN, KNN, Hidden markov Models.

Accuracy of the model based approach depends on the silhouette quality.


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Fig. 2.Gait measurements andtypes of wearable sensors

a) Measurements of human body used for feature extractionb) wearable sensors used to extract gait

features

The other categories of gait recognition approaches were image and video based approach, wearable

sensors, floor sensor and radar sensor[26]. Image and video based approach which involves remote

capturing of human movement. Image and video processing capture the gait sequence of an individual

with the help of several optic sensors like digital camera ,RGB, Low RGB-D Microsoft Kinect, laser

range scanners, infrared sensors and time to flight cameras. Table 1 presents the description about

various gait datasets collected using camera and wearable sensors. Wearable sensors which are

attached to the human body measures movement time, calculate joint or inclination angle, walking

speed, step or stride length, segment position , acceleration ,etc. Wearble sensors used for human

recognition are accelerometer, gyroscope, force sensors, strain gauges, inclinometers, goniometer,

Magnetoresistive sensor, electromagnetic tracking system, fabric sensor, and Electromyography

sensor (Figure 2b). Sensors can be combined with the respect to application of gait analysis. Floor

sensor based approach measures pressure , step length ,etc. with the help of pressure under floor mat

and floor accelerometer .In gait recognition, various factors can modify the gait pattern permanently

or transiently. External factors are terrain, footwear, clothing and cargo; Internal factors are sex,

weight, height, age, physique, etc.

3.1. Model Based Features

Model based approach model[19]generate the structure for human body and the arbitrary 2D views

from the 3D model using the techniques like pendulum, cadence , stride, 2d Stick , kinematic features,

angular trajectories etc. 3D model uses more than two calibrated cameras to capture the gait

sequences and to avoid the problems of variations in surfaces, self-occlusion and fixed viewpoint.

Structural method uses geometrical patterns of time variations and motion variations of gait sequence.

This method has overcome the problem of variation in light, background, view and segmentation. The

features extracted in model based approaches were classified using K- Nearest Neighbor, Back

Propagation Neural network, Multilayer Perceptron, Principal Component Analysis, Support Vector

machine and Linear Discriminant Analysis.

3.2. Model Free Features

Model free approach extract the gait features for classification and analysis from the captured video or

image without the person direct involvement with the system. The general process involves person


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detection, background subtraction, silhouette extraction, feature extraction and classification. Model

free approach is preferred for its low computational cost and better performance in low quality video.

Background subtraction is a process of classifying the pixel as foreground and background. Basic

background techniques are frame differencing and filtering which uses the concept of min, max,

mean, median and histogram filter. Gait image descriptors are body length and height, GEI, gradient

histogram energy image , Fourier descriptors, normal vectors, Principal Component Analysis, k-

means, HMM, standard deviation and frequency domain features , Gabor filters Multi-linear

Discriminant Analysis, Chrono-Gait Image, Linear Discriminant Analysis , Microsoft Kinect distance

method.

TABLE 1Datasets available for gait biometric analysis

METHOD DATASET DEVICE SUBJECTS DATA FORMAT

WEARABLE

SENSOR

OU-ISIR Inertial

Sensor

tri-axial

accelerometer

and gyroscope,

smartphone

744

389 – Males

355 – females,

Age- 2 to 87

ZJU-gaitacc Wii Remote -

5

175 Subjects

22-session 0

153- session 1

153- session 2

BWRMultiDevice

3 mobile

devices with

accelerometer

25 subjects

(session 1& 2)

CASIA D – FOOT

PRINT

Foot pressure

measurement

plate + foot

scanner

88 subjects

VISION

BASED

OU-ISIR

Biometric

Database

camera 62 528,

Single view

OU-ISIR

Multiview

Database

7 cameras 10,307,

Multi view

CASIA A –

STANDARD

DATASET

camera

20 person ;

each with 12

sequence in

three

directions


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CASIA B –

Multiview Gait

dataset

Captured in 11

views 124 subjects

CASIA C –

INFRARED

Infrared

thermal

camera

153 subjects

Tum Gaid Dataset

RGB Video ,

audio and

depth

305 subjects

DBMHI-DB2 Camera (front

and back) 100 subjects

Pathological

Dataset

Gait in

Neurodegenerative

Disease Database

force-sensitive

resistors

64 Subjects

(Parkinson

Disease -15 ,

Huntington

Disease – 20,

amyotrophic

lateral

sclerosis- 13,

Healthy – 16)

Daphnet Freezing

of Gait Data Set

Accelerator

Sensor

10 Parkinson

disease

Patient(3

measurements-

ankle, knee

and hip)


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4. MACHINE LEARNING IN GAIT RECOGNITION

Machine learning concepts are required to build the intelligent system to learn, recognize, analyze the

gait characteristics with large number of image sequence collected to create the gait dataset. Figure

3gives the general process of gait analysis using machine learning algorithms. Artificial intelligence

techniques like data mining, machine learning, deep learning and automation are considered for the

innovation in gait analysis fields. In machine learning paradigm, learning models are like supervised

learning, unsupervised learning, reinforcement learning used in various design perspective to capture

the gait sequence, finding the gait pattern from the characteristic features extracted with respect to the

implementing field.

Machine learning approach follows the sequential procedure of data collection, feature extraction,

learning algorithm and the evaluation methodology. Feature selection is a process used to identify the

unique characteristics and eliminate the unnecessary information to reduce the problem of over fitting

and training time thereby increasing the accuracy of the model. Feature selection techniques which are

commonly used are univariate selection, feature importance, correlation matrix with heatmap and

filter methods. Filter and wrapper feature selection method is specially used for gait sequence images.

Wrapper methods is quite expensive since the learning algorithm repeats the random resampling

technique usage for obtaining the feature subset accurately. Filter method use heuristic search

procedure to obtain the undesirable feature subset. Without the help of learning algorithm, undesirable

features were removed in terms of correlation between the examined data. Popular filter method is

recursive feature elimination algorithm which removes the low weighted features. Feature subset were

fed into evaluation procedure which determines the quality by comparing with the previous best value

given by predefined stopping criterion and validated [22].

Model selection is the technique of selecting the appropriate model from the set of models which has

high classification or prediction accuracy. Model is selected with the help of statistical technique,

exploratory data analysis, scientific techniques and specification of a model. It is recommended that

learning approach should be selected along with model selection [23, 24]. Generally, dataset is divide

into three portions namely training data, cross-validation data and testing data. Training data is

utilized to train the model with the input and known outcome. Cross validation data helps for frequent

evaluation to improve the effectiveness of the model. Testing data is used for validating the specific

model. Selected model is estimated to perform well with the training data and computed with similar

learning model with various hyper parameters, complexities. Validation dataset is opted using the

methods: holdout method, k-fold cross validation, leave-one cross validation, random subsampling

cross validation and bootstrapping.


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Fig. 3. Basic machine learning approaches used for Gait classification, prediction and analysis

4.1. Classification Network

Classification Network has statistical and derivative phase. Statistical phase extracts the statistical

measurements from the non-linear transformed input. Derivative phase improves the accuracy with

mathematical model. Classification networks are classified into shallow network and deep neural

network. Shallow neural network usessolitary hidden layer whereas deep neural network uses more

than one hidden layer. Shallow networks used for gait recognition are K-Neighbor, Hidden Markov

Model, Bayesian Network, Support Vector machine and BPNN. Deep neural network increases the

accuracy rate when compared to shallow network and has become more interesting approaches in

extracting features in recent years. Wang et al[33] used nearest neighbour approach to generate the

silhouette signal to identify the person and achieved 77.4% classification rate with CASIA B dataset.

Jeevan et al [34] recognizes the gait of an individual with Gait Pal and Pal Entrophy using Principal

Component Analysis with Support Vector machines. Implementing with treadmill dataset and CASIA

A, B and C datasets, achieved 92.3% accuracy. Xiao et al[35] recognizes the individual by their gait

sequence using Zernike moments and Back Propagation Neural Network (BPNN)

4.2. Deep Learning Approaches

In the family of machine learning algorithms, deep learning has become the most important methods

due to its wide applications. Deep architectures represent the high level abstractions using multiple


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stacked learning neural network layers and separate network segment is used to learn separate data

representation. The energetic factors of deep neural network are feature learning, hierarchical and

distributed representations, computational speed with large dataset varies in the automation process

and analysis of depth. Multiple neural network layers are convolutional layer, sampling pooling

layers, activation layer and fully connected layer. The multiple arrangement of these layer and

dimensions varies corresponding to the application and architecture.

5. DEEP ARCHITECTURES

In deep learning approaches, there is no need to create a separate feature set since the discriminative

features were found while training the modelDeep architectures which are used widely for gait

recognition is convolutional neural network, multiple stacked auto encoder and deep Boltzmann

machine and Restricted Boltzmann Machine.

5.1. Deep Boltzmann Machine

A Deep Boltzmann Machine uses hidden random variables for multiple layers of symmetrically

couple stochastic Markov Random Field[44]. It has visible unit set {0,1}D and hidden layer series

units h(1) {0,1}F1, h(2) {0,1}F2 ,…… h(L) {0,1}FL . Units in adjacent layers have connection between

them. For instance, consider three hidden layers for DBM as shown in Figure 4. The probability of a

visible vector assigned by Deep Boltzmann Machine is given as (1).

𝑃(𝑣; 𝜃) = 1

(𝜃∑ 𝑒𝑥𝑝

ℎ

(∑ 𝑊𝑖𝑗(1)

𝑢𝑖

𝑖𝑗

ℎ𝑗(1)

+ ∑ 𝑊𝑗𝑙(2)

𝑗𝑙

ℎ𝑗(1)

ℎ𝑙(2)

+ ∑ 𝑊𝑙𝑚(3)

ℎ𝑙(2)

𝑙𝑚

ℎ𝑚(3)

)

(1)

In equation (1) h is a set of hidden units represented as {h(1) , h(2) , h(3)} and the model parameter =

{W(1) , W(2) , W(3)}. This represents symmetric interactions between the visible units and hidden units.

Restricted Boltzmann Machine model is recovered by assigning W(2) and W(3) as 0. Initially the

weights in hidden units are asymmetric as the weights are twice as the top layer. Later to maintain the

symmetry, weights are modified.

Fig. 4.a) Deep Boltzmann machine has undirected connection. b) DBM is pretrained with RBM stack.

The weights of first and last layers of RBM is modified as W(1) and W(3) to maintain the symmetric

pattern [44].

Yu et al[43] proposed the Convolutional Restricted Boltzmann Machine to extract the gait features to

identify the individuals. CRBM was experimented on CASIA B gait dataset with 11 different angles

and 3 variations in outfit obtained the accuracy of 96.5%.


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5.2. Deep Stacked Autoencoder

Fig. 5. a) single autoencoder with same dimensionality of input layer (x1, x2, , x6) and output layer ,

lower dimensionality in feature extraction layer. b) Stacked autoencoder enables the supervised

classification in the final output layer [45].

Deep Stacked Autoencoder has more layers to extract the discriminative and representative features. It

is formed by stacking the autoencoder network one above the other and implemented multilayer

perceptron. A single autoencoder network has encoding layer for encoding the input and decoder layer

for reconstructing the input. In decoding layer, number of neurons and dimension of input are

considered to be in the same count. It recovers the input x with high accuracy using the two stage

approximation model. The code h used for recovering is

𝑓𝑑𝑒𝑐 (𝑓𝑒𝑛𝑐(𝑥)) = 𝑓𝑑𝑒𝑐(ℎ) = 𝑥 ≈ 𝑥

Where fenc is encoding function and fdec is the decoding function computed by their respective coding

layer. The main function of stacked autoencoder to train the unsupervised input layer by layer. Figure

5 shows how the single autoencoder turns to stacked autoencoder to perform the classification. The

final output layer produces the supervised classification by fine tuning the pre-trained network with

Back Propagation[45]. Sun et al [46] proposed the deep network by combining the convolutinal neural

network and stacked autoencoder to recognize the human with the gait subtle features. Micro-motion

dataset is a radar echo dataset collected using the Radio Frequency device is used to implement the

proposed network and achieved 96% as highest average precision.

5.3. Recurrent Neural Network

Recurrent Neural Network (RNN) is designed to solve classification problems. RNN deals with

sequential data with more interdependencies especially well-suited for clinical data.In RNN, input

sequence is termed as X1, X2,…., Xnand output sequence is termed as Y1, Y2,…., Yn. Output Vector Yi

depends on the past values of X and it is given by

𝑌𝑖 = 𝑓𝑖(𝑋1, … . . 𝑋𝑛)

Hidden states are utilized to learn parameters from the sequential data given in the previous and

current recurrence. The hidden state, hi and recurrent layer output, yi is calculated as

ℎ𝑖 = tanh(𝑊ℎℎℎ𝑖−1 + 𝑊𝑥ℎ𝑥𝑖 + 𝑏ℎ )


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Where W denote weight, b denote biases and i denote the frame index.To solve the vanishing

gradient problem, Long Short Term Memory (LSTM) and Gated Recurrent units(GRU) is proposed.

These networks maintain the long term inter relations and nonlinear dynamics effectively.To control

the number of parameters in the network, same weight is determined for all layers.

Hidden state is updated with forget gate value fi, input gate value gi and output gate value oi..

Learning variables are given as follows:

𝑓𝑖 = 𝜎(𝑊𝑥𝑓𝑥𝑖 + 𝑊ℎ𝑓ℎ𝑖−1 + 𝑏𝑓)

𝑔𝑖 = 𝜎(𝑊𝑥𝑖𝑥𝑖 + 𝑊ℎ𝑖ℎ𝑖−1 + 𝑏𝑖)

𝐶𝑖 = 𝑓𝑡 ⃘ 𝐶𝑖−1 + 𝑖𝑡 ⃘ tanh(𝑊𝑥𝐶𝑥𝑖 + 𝑊ℎ𝐶ℎ𝑖−1 + 𝑏𝐶)

𝑜𝑖 = 𝜎(𝑊𝑥𝑖𝑥𝑖 + 𝑊ℎ𝑖ℎ𝑖−1 + 𝑏𝑖)

ℎ𝑖 = 𝑖𝑡 ⃘ tanh(𝐶𝑖)

Where W denote weights and b denote biases. Prado et al [65] used RNN to classify the cerebral

palsy disease in adults and children. Data were collected from 28 adults and 7children with cerebral

palsy using the pressure inertial foot sensor. They have proposed RNN with instrumented foot sensor

which does not require any preporocessing technique, thereby processing time is reduced to less than

1 second.

5.4. Convoultional Neural Network

Convolutional neural network (CNN) is multilayer perceptron in a regularized version effectively

implemented on Graphics Processing Unit (GPU) or General Purpose computing on Graphics

Processing Unit (GPGPU). GPU which is mainly developed for video game application is used to

increase the speed of deep Neural Network processing. Convolutional Neural network has

convolution layer, Pooling Layer, Activation function, Loss function, regularization, optimization

process. Figure6 demonstrates the general outline of convolutional neural network.First parameter of

the CNN is the input data in the form of image or signal. Second parameter is the filter. Fully

connected layer gives the output in the form of vector from which feature map is obtained. In the form

of multidimensional array, each input and filter are saved independently in the network. Local

information of the image is identified by convolutional layer.

Fig. 6. Schematic diagram of convolutional Neural network

Convolution layer is categorized into titled convolution, transposed convolution, dilated convolution,

network in network and inception module. Pooling layer is categorized into average pooling, max

pooling, Lp pooling , mixed pooling , stochastic pooling , spectral pooling , spatial pyramid pooling

and multi-scale order less pooling. Activation function used are Rectified Linear Unit (ReLu), Leaky

ReLU, parametric ReLU, Randomized ReLU, Exponential Linear Unit, Maxout and Probout.

Methods used for convolution neural network are as follows : LeNet 5 is a 8 Layered architecture


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used for image recognition. AlexNet uses GPU to train the high resolution large dataset with 1000

different classes. Alexnet does faster processing compared to LeNet5 due to its non saturating

neurons. Overfitting problem is reducing by using dropout method. Zeiler and Fergus proposed ZFNet

as multi-layered deconvolutional neural network with 8 layers. ZfNet uses GPU to classify large

image dataset and dropout to reduce the parameters.Network – In-Network ( NIN) is proposed to

implement per-pixel nonlinearity and to reduce the number of parameters. NIN uses 1 x 1

convolutional network with ReLU activation. Global average pooling is used to produce logit

vectors.Region based Convolutional Neural Network (R-CNN) detects the object by extracting the

features with region proposals. Forward computions is required to choose the thousand number of

region proposals from a single image increase the processing time. To overcome this, Fast RCNN is

proposed to perform the forward computation as a whole. Entire image is given as an image for

processing each region proposals at a time unlike RCNN where region proposals are given as a input.

Faster R-CNN uses region proposal network, thereby reducing the number of generated region

proposal and maintaining the accuracy of object detection. Mask R-CNN uses fully convolutional

layer for locating the objects at pixel level. Comparatively Mask RCNN enhanced object detection

accuracy rather than Faster RCNN. GoogLeNet uses 22 convolutional layer for transfer learning.

Optimization is increased when image classification is done via GoogLeNet CNN model. Visual

Geometry Group (VGG) is widely used in gait recognition when compared to other CNN model since

it provides high accuracy. VGG performs training faster since it has reduced number of parameters.

Table 3 provides the overview of recent work in gait recognition using deep architectures.

TABLE 3Review of recent gait recognition using deep architectures.

YEA

R

REFERENC

E

APPROA

CH

GAIT

FEATUR

ES

DATASET CLASSIFIE

R PURPOSE

ACCURA

CY

2006 [40] Yu at al Model

free

GEI CASIA B Multilayer

autoencoder

Identificatio

n

-

2015 [28] Chao Yan

et al

Model

free

GEI CASIA B CNN Identificatio

n

96%

2015 [29] Perlin et al - Cloth and

gender

H3D

dataset,

ViPer

dataset,

HATdb

CNN Pattern for

clothes and

gender

-

2016 [48] Hannink et

al

Model

Based

acclerator

,

gyroscope

parameter

s

GAITRite CNN with

modifications

Identificatio

n

-

2017 [37] Alotaibi et

al

Model

free

CASIA B CNN, keras

,python

Identificatio

n

Mis-

classificatio

n rate -

<0.15%

2017 [41] Sokolova

and Konushin

Model

free

Optical

Flow

TUM-

GAID

dataset

CNN - VGG Identificatio

n

16 hours ,

97%

2017 [42] Dehzangi

et al

Model

Based

Gait

measurem

ents

Sensor

collected

data

Deep CNN Identificatio

n

97.60%

2017 [30] Alotaibi et Model GEI CASIA B Deep CNN Identificatio 98.30%


1648

al free n

2018 [31] Gadaleta et

al

Model

Based

acclerator

,

gyroscope

parameter

s

GaitDataset

McGill CS

CNN Identificatio

n

94%

2018 [32] batchulunn

et al

Model

free

GEI DBMHI-

DB1

CNN - VGG Authenticati

on

Identificatio

n

100%

2018 [47] Liu et al Model

free

GEI CASIA B CNN - VGG Identificatio

n

89.62%

2018 [39] Wu et al Model

free

GEI CASIA B,

OU-ISIR

Deep CNN

with location

weight

descriptor

Identificatio

n

86%

2018 [53] Yuan et al Model

Based

acclerator

,

gyroscope

parameter

s

GITHUB CNN Authenticati

on

Identificatio

n

87%

2018 [54] Batchuluun

et al

Model

free

camera

image

Sensor

collected

data

DBMHI-

DB1,DBM

HI-DB2

LSTM CNN Authenticati

on

Identificatio

n

97%

2018 [55] Sun et al Model

Based

spectogra

m

Self

collected

dataset

from radar

and

microwave

anechoic

chamber

CNN, Stacked

Auto encoder

Authenticati

on

Identificatio

n

96.36%

2018 [43] Yu et al Model

free

GEI CASIA B CNN-

BOLTZMAN

N MACHINE

Identificatio

n

96.50%

2018 [57] Liu et al Model

free

GEI OU-ISIR CNN,

SIAMESE

NEURAL

NETWORK

Identificatio

n

88

2018 [49] Tong et al Model

free

GEI CASIA-B,

OU-ISIR,

and CMU

MoBo

SPATIAL

TEMPORAL

DEEP NN

Identificatio

n

95.67%

2018 [58] Takemura

et al

Model

free

GEI OU-ISIR CNN Identificatio

n

95%

2018 [59] Yao et al Model

free

Skeleton

Gait

image

CASIA B CNN Identificatio

n

92.09%

2018 [64] Verlekar et

al

Model

free

GEI DAI , DAI2

INIT

CNN – LSTM Clinical gait

disease

95%


1649

classificatio

n

2018 [68] Wang et al Model

based

Threadnill

sensor

Recorded

with Body

weight

support

threadmill

CNN-

AlexNet

Gesture

recognition

99.98%

2019 [27] Maryam et

al

Model

free

GEI OULP and

Casia-B

CNN Identificatio

n

-


free

GEI CASIA B ,

OU-ISIR

CNN- 2

BRANCH

Authenticati

on

94%

2019 [52] Hawas et al Model

free

GEI CASIA B CNN Identificatio

n

95%


free

GEI CASIA A,

B, OU-

ISIR

CNN Identificatio

n

95%

2020 [63] Shao et al Model

free

GEI CASIA B CNN , LeNet5 Identificatio

n with

Covariates

98.3%

(normal)

89.2 % (

Backpack)

95.8% (

Wearing

coat)

Human Gait Recognition:

Hannink et al [48] used convolutional neural network with ReLU activation function to extract the

gait feature from wearable sensor dataset. Tong et al [49] used spatial-temporal convolutional neural

network to recognize the gait in multi-view angle with 92.54% accuracy. Liu et al[47] combined the

convolutional neural network and support vector machine to predict the gender of an individual using

the CASIA B dataset with 87.94%. The convolution model VGGNet-16 is used to train the network

with GEI input while SVM classify the descriptors provided by optimizing the weight parameters.

Shao et al [63] proposed improved LeNet5 network combined with Residual Network to accelerate

the convergence speed and to improve the recognition rate. CASIA B dataset is trained and tested

with the improved LeNet model of CNN. They focused to work on the covariates like Viewing angle,

backpack, jacket and speed. The accuracy of gait recognition with backpack, wearing coat and normal

are 89.2%, 95.8% and 98.3% respectively. Maryam et al [27] used FCN (Fully Convolutional Neural

Network) to reconstruct the incomplete Gait Energy Image(GEI) into a complete GEI. Experiments

were carried out in OU-ISIR Biometric – OULP database and CASIA B database to obtain the

complete GEI from 0.1 part of a gait cycle. This method helps in matching view-variant incomplete

gait image.

Clinical Gait Analysis: Verlekar[64] proposed a fine tuned VGG-19 Deep architecture to classify gait disease type. The

pathological dataset with diplegia, Hemiplegia, neuropathy, parkinson and normal individual gait

dataset is used to experiment the proposed model. The classification of normal and impaired gait type

is performed with high accuracy. Wang et al [64] used Alexnet CNN model to perform the gesture

recognition system with 99.98% accuracy. Five different gestures such as moving forward, moving

backward, moving left, moving right and stop are recognized with Body Weight Support Treadmill

(BWST) Platform. This recognition system helps in lower limb rehabilitation. This system is

controlled by a trainer to monitor the patient walking sequence in BWST platform.


1650

6. DISCUSSION

A comprehensive survey of gait recognition and analysis methods based on wearable sensors and

vision based methods, various features and deep architecture techniques were presented. A review of

gait databases with different techniques used to capture gait features were given. Figure 7 depicts the

recurrently used gait features in various fields and obtained the best accuracy with the recent

techniques of deep architectures.

(a) (b)

(c)

Fig. 7. Statistical figures based on the referred papers a) gait features and usage b) Gait analysis

applied in the various fields c) Different combinations of Deep architectures used in gait recognition.

Future Research Directions.

The scope of future research provides an insight into remote health monitoring, surveillance and

robotics. Immense opportunities are open to enhance and innovate in the current technological


1651

environment in the following fields. Application of Multivariate statistical analysis and deep learning

techniques is becoming increasing popular in the area of clinical gait analysis, thereby providing new

insights into human gait control. According to Global Burden of Disease (GDB), musculoskeletal

disorder is estimated to witness the second highest growth rate across the globe. In recent years,

number of people with abnormal walking posture is increasing. Research were carried out to

differentiate the healthy walking posture and abnormal walking posture. Sports professionals utilized

gait analysis to monitor the movement of leg in terms of angle, position etc. The measurement and

assessment of the players improved their performance as well as decreased the risk of injury. Model

free method is preferred quickly to interpret about the movement of the players. Trajectory

movements of model based human gait helps to design the walking nature of robots.Human gait

recognition system is becoming widely spread due to its nature of identifying individual without their

intervention with the system. Still there exist some limitations such as impact of environmental

changes, user location and orientation, sensing multi-user activity and security considerations. To

perform the longitudinal study over the wide range of covariates, there is a need for a large multi-

modality, multi-covariate standard dataset. Our future work is to carry out the experiments with the

recent network architectures and novel approaches with the huge dataset of different view angles,

clothing and walking style promoting the better automatic gait recognition and gait analysis system.

7. CONCLUSION

The most interesting research in biometrics is automatic gait recognition when compared to other

human unique features. Though there are many approaches to overcome the variations in gait

recognition, still there are challenges to recognize a person. Challenges are distinctive gait datasets,

degree of stability in identifying, sensing modality, covariates and spoofing effects, and exploring

new algorithms. From the developmental perspective, human gait recognition has gained maturity in

adopting recent methodologies to provide the high accuracy and the same was analyzed and studied in

the view of vision based system. In this paper, the basic knowledge about the human gait is identified

and explored. The comparative analysis with the existing techniques of gait recognition were

discussed. In this work, the survey of recent deep architecture model on human gait identification,

authentication and clinical applications were discussed. Future research directions and challenges

were identified.

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