Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

Peng, Zhaoqin; Cao, Chun; Liu, Qiusheng; Pan, Wentao

doi:10.1155/2013/143435

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…local minima and maxima of gait signals and time-distance parameters) [11], [14], [15]. Additionally, previous research has shown that global signal representations obtained by principal component analysis (PCA) [16], [17], kernel-based PCA (KPCA) [18], [19] and discrete wavelet transformation (DWT) [10], [11] are suitable for subsequent classification [10], [16]. Typical use cases for automatic gait analysis described in the literature show a moderate to high accuracy in distinguishing between different pathologies or patient groups [4], [7]- [9], [11], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Functional Gait Disorders

Slijepčević

Zeppelzauer

Gorgas

et al. 2018

IEEE J. Biomed. Health Inform.

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This paper proposes a comprehensive investigation of the automatic classification of functional gait disorders (GDs) based solely on ground reaction force (GRF) measurements. The aim of this study is twofold: first, to investigate the suitability of the state-of-the-art GRF parameterization techniques (representations) for the discrimination of functional GDs; and second, to provide a first performance baseline for the automated classification of functional GDs for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with GDs and data from 161 healthy controls (N). Patients were manually classified into four classes with different functional impairments associated with the "hip", "knee", "ankle", and "calcaneus". Different parameterizations are investigated: GRF parameters, global principal component analysis (PCA) based representations, and a combined representation applying PCA on GRF parameters. The discriminative power of each parameterization for different classes is investigated by linear discriminant analysis. Based on this analysis, two classification experiments are pursued: distinction between healthy and impaired gait (N versus GD) and multiclass classification between healthy gait and all four GD classes. Experiments show promising results and reveal among others that several factors, such as imbalanced class cardinalities and varying numbers of measurement sessions per patient, have a strong impact on the classification accuracy and therefore need to be taken into account. The results represent a promising first step toward the automated classification of GDs and a first performance baseline for future developments in this direction.

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Section: Introductionmentioning

confidence: 99%

Automatic Classification of Functional Gait Disorders

Slijepčević

Zeppelzauer

Gorgas

et al. 2018

IEEE J. Biomed. Health Inform.

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“…Similar to earlier study, the retained PCs have eigenvalues greater than one but higher cumulative percentage is selected that is up to 95%. KPCA has outperformed PCA in selecting the significant features but KPCA requires more runtime due to higher dimensional space requirement as reported in [22]. Further, study on walker-assisted gait has retained the PCs in line with these three characteristics viz.…”

Section: Feature Extraction Methodsmentioning

confidence: 96%

Review on anomalous gait behavior detection using machine learning algorithms

Razak¹,

Tahir²

2020

Bulletin EEI

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A review on anomalous behavior in crime by other researchers is discussed in this study that focused specifically on the linkage between anomalous behaviors. Next, comprehensive reviews related to gait recognition in utilizing machine learning algorithms for detection and recognition of anomalous behavior is elaborated too. The review begins with the conventional approach of gait recognition that includes feature extraction and classification using PCA, OLS, ANN, and SVM. Further, the review focused on utilization of deep learning namely CNN for anomalous gait behavior detection and transfer learning using pre-trained CNNs such as AlexNet, VGG, and a few more. To the extent of our knowledge, very few studies investigated and explored crime related anomalous behavior based on their gaits, hence this will be the next study that we will explore.

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“…Accordingly, the representation of individual gait is sent to the characteristic matcher to authenticate a walking person. The identification of GRF is conducted by using the SVM (support vector machine) classifier, which resolves problems of highdimensional data as presented in [46]. The RBF (radial basis function) kernel is taken for data training and testing.…”

Section: Grf Testing Algorithmmentioning

confidence: 99%

Remote Identity Verification Using Gait Analysis and Face Recognition

Zhang

et al. 2020

Wireless Communications and Mobile Computing

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Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2% is obtained, which makes it a promising basis for the realization of remote identity verification.

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Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

Cited by 14 publications

References 34 publications

Automatic Classification of Functional Gait Disorders

Automatic Classification of Functional Gait Disorders

Review on anomalous gait behavior detection using machine learning algorithms

Remote Identity Verification Using Gait Analysis and Face Recognition

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