Gait recognition with shifted energy image and structural feature extraction

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems

Gait recognition using HMMs and dual discriminative observations for sub-dynamics analysis

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We propose a new gait recognition method that combines holistic and model-based features. Both types of features are extracted automatically from gait silhouette sequences and their combination takes place by means of a pair of hidden Markov models. In the proposed system, the holistic features are initially used for capturing general gait dynamics whereas, subsequently, the model-based features are deployed for capturing more detailed sub-dynamics by refining upon the preceding general dynamics. Furthermore, the holistic and model-based features are suitably processed in order to improve the discriminatory capacity of the final system. The experimental results show that the proposed method exhibits performance advantages in comparison with popular existing methods

Autism Spectrum Disorder and Normal Gait Classification Using Machine Learning Approach

Previous research has reported that children with autism spectrum disorder (ASD) exhibit unusual movement and atypical gait patterns. Automated classification of abnormal gait from normal gait can serve as a potential tool for early and objective diagnosis as well as post-treatment monitoring. The aim of this study is to employ machine learning approaches to differentiate between children with ASD and healthy controls by utilizing gait features extracted from three-dimensional (3D) gait analysis data. The gait data of 30 children with ASD and 30 healthy controls were obtained using 3D gait analysis during walking at a normal pace. Time-series parameterization techniques were applied to the kinematic and kinetic waveforms to extract useful gait features. Further, the dominant gait features were selected using statistical feature selection techniques. To highlight the efficacy of different machine learning classifiers towards devising an accurate gait classification, four machine learning classifiers were trained to classify ASD and control gait based on the selected dominant gait features. The classifiers are Artificial Neural Networks (ANN), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA). The 10-fold cross-validation test results indicate that the ANN-SCG classifier with six dominant gait features was able to produce the optimum classification performance with 98.3% accuracy, 96.7% sensitivity, and 100% specificity. The findings indicate that the ANN classifier has the potential to serve as a valuable tool for assisting in the diagnosis of ASD gait and evaluating treatment programs

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness