scholarly journals An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hajra Masood ◽  
Humera Farooq
Keyword(s):  
South Asian ◽  
Cross Correlation ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Visual Surveillance ◽  
Support Vector ◽  
Person Identification ◽  
Correlation Score ◽  
Gait Features ◽  
Dynamic Gait

Gait recognition-based person identification is an emerging trend in visual surveillance due to its uniqueness and adaptability to low-resolution video. Existing gait feature extraction techniques such as gait silhouette and Gait Energy Image rely on the human body’s shape. The shape of the human body varies according to the subject’s clothing and carrying conditions. The clothing choice changes every day and results in higher intraclass variance and lower interclass variance. Thus, gait verification and gait recognition are required for person identification. Moreover, clothing choices are highly influenced by the subject’s cultural background, and publicly available gait datasets lack the representation of South Asian Native clothing for gait recognition. We propose a Dynamic Gait Features extraction technique that preserves the spatiotemporal gait pattern with motion estimation. The Dynamic Gait Features under different Use Cases of clothing and carrying conditions are adaptable for gait verification and recognition. The Cross-Correlation score of Dynamic Gait Features resolves the problem of Gait verification. The standard deviation of Cross-Correlation Score lies in the range of 0.12 to 0.2 and reflects a strong correlation in Dynamic Gait Features of the same class. We achieved 98.5% accuracy on Support Vector Machine based gait recognition. Additionally, we develop a multiappearance-based gait dataset that captures the effects of South Asian Native Clothing (SACV-Gait dataset). We evaluated our work on CASIA-B, OUISIR-B, TUM-IITKGP, and SACV-Gait datasets and achieved an accuracy of 98%, 100%, 97.1%, and 98.8%, respectively.

Centre-of-mass-based gait recognition for person identification

2019 ◽  
Vol 13 ◽  
Author(s):  
Rajib Ghosh
Keyword(s):  
Body Movement ◽  
Gait Recognition ◽  
Visual Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Centre Of Mass ◽  
Person Identification ◽  
Walking Pattern ◽  
Walking Activity

Background: Gait recognition focuses on identification of persons from their walking activity. This type of system plays an important role in visual surveillance applications. Walking pattern of every person is unique and difficult to replicate by others. Objective: The present article focuses on to develop a person identification system based on gait recognition. Methods: In this article, a novel gait recognition approach is proposed to show how human body Centre-of-mass-based walking characteristics can be used to recognize unauthorized and suspicious persons when they enter in a surveillance area. Walking pattern varies from person to person mainly due to the differences in the footsteps and body movement. Initially, background is modelled from the input video captured through static cameras deployed for security purpose. Foreground moving object in the individual frames are then segmented using the background subtraction algorithm. Centre-of-mass based discriminative features of various walking patterns are then studied using Support Vector Machine(SVM) classifier to identify each unique walking pattern. Results: The proposed system has been evaluated using a self-generated dataset containing side view of various walking video clips. The experimental results demonstrate that the proposed system achieves an encouraging person identification rate. Conclusion: This work can be further extended to provide a general approach in developing an automatic person identification system in unconstrained environment.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

STUDY OF GAIT PATTERN RECOGNITION BASED ON FUSION OF MECHANOMYOGRAPHY AND ATTITUDE ANGLE SIGNAL

2020 ◽  
Vol 20 (02) ◽  
pp. 1950085 ◽  
Author(s):  
JING YU ◽  
YUE ZHANG ◽  
CHUNMING XIA
Keyword(s):  
Vastus Lateralis ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Rectus Femoris ◽  
Gait Pattern ◽  
Support Vector ◽  
Dynamic Transition ◽  
Transition Activities ◽  
Attitude Angle ◽  
Average Recognition Rate

The study of lower limb movements plays an important role in many fields, such as rehabilitation and treatment of disabled patients, detection, and monitoring of daily life, as well as the interaction between people and machine, like the application of intelligent prosthetics. In this paper, the wireless device was used to collect the mechanomyography (MMG) signals of four thigh muscles (rectus femoris, vastus lateralis, vastus medialis, and semitendinosus) and the attitude angle of rectus femoris. High precision was achieved in 11 gait movements, including 3 static activities, 4 dynamic transition activities, and 4 dynamic activities. It has been verified that the hidden Markov model (HMM) could not only be applied to the MMG-based gait recognition with high veracity but also support comparative analysis between support vector machine (SVM) and quadratic discriminant analysis (QDA). In addition, the experiment was conducted from the perspectives of feature selections, channel combinations, and muscle contribution rates. The results show that the average classification accuracy of dynamic motions based on MMG is 98.27%, while based on attitude angle, the average recognition rate of static motions and dynamic transition motions could achieve 98.33% and 100%, respectively. Generally, the average recognition rate of 11 gait motions is 98.91%.

scholarly journals Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals

2018 ◽  
Vol 8 (9) ◽  
pp. 1462 ◽  
Author(s):  
Xianfu Zhang ◽  
Shouqian Sun ◽  
Chao Li ◽  
Zhichuan Tang
Keyword(s):  
Control System ◽  
Lower Limb ◽  
Gait Recognition ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Real Time Control ◽  
Lower Limb Exoskeleton ◽  
Load Variation ◽  
Significant Difference

As lower-limb exoskeleton and prostheses are developed to become smarter and to deploy man–machine collaboration, accurate gait recognition is crucial, as it contributes to the realization of real-time control. Many researchers choose surface electromyogram (sEMG) signals to recognize the gait and control the lower-limb exoskeleton (or prostheses). However, several factors still affect its applicability, of which variation in the loads is an essential one. This study aims to (1) investigate the effect of load variation on gait recognition; and to (2) discuss whether a lower-limb exoskeleton control system trained by sEMG from different loads works well in multi-load applications. In our experiment, 10 male college students were selected to walk on a treadmill at three different speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h) with four different loads (L0 = 0, L20 = 20%, L30 = 30%, L40 = 40% of body weight, respectively), and 50 gait cycles were performed. Back propagation neural networks (BPNNs) were used for gait recognition, and a support vector machine (SVM) and k-nearest neighbor (k-NN) were used for comparison. The result showed that (1) load variation has significant effects on the accuracy of gait recognition (p < 0.05) under the three speeds when the loads range in L0, L20, L30, or L40, but no significant impact is found when the loads range in L0, L20, or L30. The least significant difference (LSD) post hoc, which can explore all possible pair-wise comparisons of means that comprise a factor using the equivalent of multiple t-tests, reveals that there is a significant difference between the L40 load and the other three loads (L0, L20, L30), but no significant difference was found among the L0, L20, and L30 loads. The total mean accuracy of gait recognition of the intra-loads and inter-loads was 91.81%, and 69.42%, respectively. (2) When the training data was taken from more types of loads, a higher accuracy in gait recognition was obtained at each speed, and the statistical analysis shows that there was a substantial influence for the kinds of loads in the training set on the gait recognition accuracy (p < 0.001). It can be concluded that an exoskeleton (or prosthesis) control system that is trained in a single load or the parts of loads is insufficient in the face of multi-load applications.

Hybrid Feature Vector-Assisted Action Representation for Human Action Recognition Using Support Vector Machines

2021 ◽  
pp. 1-22
Author(s):  
L. Nirmala Devi ◽  
A.Nageswar Rao
Keyword(s):  
Action Recognition ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Gabor Filter ◽  
Principal Component ◽  
Human Action Recognition ◽  
Human Action ◽  
Visual Surveillance ◽  
Support Vector ◽  
Significant Research

Human action recognition (HAR) is one of most significant research topics, and it has attracted the concentration of many researchers. Automatic HAR system is applied in several fields like visual surveillance, data retrieval, healthcare, etc. Based on this inspiration, in this chapter, the authors propose a new HAR model that considers an image as input and analyses and exposes the action present in it. Under the analysis phase, they implement two different feature extraction methods with the help of rotation invariant Gabor filter and edge adaptive wavelet filter. For every action image, a new vector called as composite feature vector is formulated and then subjected to dimensionality reduction through principal component analysis (PCA). Finally, the authors employ the most popular supervised machine learning algorithm (i.e., support vector machine [SVM]) for classification. Simulation is done over two standard datasets; they are KTH and Weizmann, and the performance is measured through an accuracy metric.

scholarly journals EOG feature relevance determination for microsleep detection

2017 ◽  
Vol 3 (2) ◽  
pp. 815-818
Author(s):  
Martin Golz ◽  
Sebastian Wollner ◽  
David Sommer ◽  
Sebastian Schnieder
Keyword(s):  
Cross Correlation ◽  
Feature Reduction ◽  
Support Vector ◽  
Spectral Bands ◽  
Automatic Relevance Determination ◽  
Signal Features ◽  
Power Spectral ◽  
Vector Machines ◽  
Power Spectral Densities ◽  
Feature Relevance

AbstractAutomatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 - 4.9 % and 1.9 - 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 - 0.006 % and 0.002 - 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respec-tively. GRLVQ permits objective feature reduction by inclu-sion of all processing stages, but is not as accurate as SVM.

scholarly journals Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5449 ◽  
Author(s):  
Yue Ma ◽  
Xinyu Wu ◽  
Can Wang ◽  
Zhengkun Yi ◽  
Guoyuan Liang
Keyword(s):  
Least Squares ◽  
Joint Angle ◽  
Least Squares Method ◽  
Classification Model ◽  
Recursive Least Squares ◽  
Support Vector ◽  
Exoskeleton Robot ◽  
Phase Classification ◽  
Gait Features ◽  
Gait Phase

The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods—the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.

scholarly journals Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

2019 ◽  
Vol 9 (11) ◽  
pp. 2357 ◽  
Author(s):  
Niccolò Dematteis ◽  
Daniele Giordan ◽  
Paolo Allasia
Keyword(s):  
Cross Correlation ◽  
Earth Science ◽  
Operative Procedure ◽  
Support Vector ◽  
Diffuse Illumination ◽  
Monitoring Applications ◽  
Correlation Processing ◽  
Classification Of Images ◽  
Active Processes

In Earth Science, image cross-correlation (ICC) can be used to identify the evolution of active processes. However, this technology can be ineffective, because it is sometimes difficult to visualize certain phenomena, and surface roughness can cause shadows. In such instances, manual image selection is required to select images that are suitably illuminated, and in which visibility is adequate. This impedes the development of an autonomous system applied to ICC in monitoring applications. In this paper, the uncertainty introduced by the presence of shadows is quantitatively analysed, and a method suitable for ICC applications is proposed: The method automatically selects images, and is based on a supervised classification of images using the support vector machine. According to visual and illumination conditions, the images are divided into three classes: (i) No visibility, (ii) direct illumination and (iii) diffuse illumination. Images belonging to the diffuse illumination class are used in cross-correlation processing. Finally, an operative procedure is presented for applying the automated ICC processing chain in geoscience monitoring applications.

Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier

2017 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Taro Nakano ◽  
B.T. Nukala ◽  
J. Tsay ◽  
Steven Zupancic ◽  
Amanda Rodriguez ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Dynamic ◽  
Linear Svm ◽  
Dynamic Gait

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.

scholarly journals Gait Tracking Control of Quadruped Robot Using Differential Evolution Based Structure Specified Mixed SensitivityH∞Robust Control

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Petrus Sutyasadi ◽  
Manukid Parnichkun
Keyword(s):  
Tracking Control ◽  
Control Algorithm ◽  
Joint Angle ◽  
Gait Pattern ◽  
Quadruped Robot ◽  
Robust Controller ◽  
Quadruped Robots ◽  
Parameter Variations ◽  
Real Hardware ◽  
Dynamic Gait

This paper proposed a control algorithm that guarantees gait tracking performance for quadruped robots. During dynamic gait motion, such as trotting, the quadruped robot is unstable. In addition to uncertainties of parameters and unmodeled dynamics, the quadruped robot always faces some disturbances. The uncertainties and disturbances contribute significant perturbation to the dynamic gait motion control of the quadruped robot. Failing to track the gait pattern properly propagates instability to the whole system and can cause the robot to fall. To overcome the uncertainties and disturbances, structured specified mixed sensitivityH∞robust controller was proposed to control the quadruped robot legs’ joint angle positions. Before application to the real hardware, the proposed controller was tested on the quadruped robot’s leg planar dynamic model using MATLAB. The proposed controller can control the robot’s legs efficiently even under uncertainties from a set of model parameter variations. The robot was also able to maintain its stability even when it was tested under several terrain disturbances.

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