Test Gait Pattern Research Articles

Detecting the presence of anterior cruciate ligament injury based on gait dynamics disparity and neural networks

The aim of this study is to develop a new pattern recognition-based method to model and discriminate gait dynamics disparity between anterior cruciate ligament (ACL) deficient (ACL-D) knee and contralateral ACL-intact (ACL-I) knee in patients with unilateral ACL deficiency by using kinematic features and neural networks. Thereby the capabilities of these features to detect the presence of injury can be assessed. The proposed method consists of two stages. In the first (training) stage, gait analysis is performed. A two-dimensional five-link biped model used for imitating human gait locomotion is employed to demonstrate that functions containing kinematic data of lower extremities, including knee and hip flexion/extension angles and angular velocities, characterize the gait system dynamics. Knee angle-hip angle cyclograms, knee and hip angle-angular velocity phase portraits visually demonstrate the significant disparity of gait dynamics between the lower extremities of patients with unilateral ACL deficiency. Gait dynamics underlying gait patterns of ACL-D and ACL-I knees are locally accurately modeled and approximated by radial basis function (RBF) neural networks via deterministic learning theory. The derived knowledge of approximated gait dynamics is preserved in constant RBF networks. In the second (classification) stage, a bank of dynamical estimators is constructed using the preserved constant RBF networks to represent the learned training gait patterns. By comparing the set of estimators with a test gait pattern, the generated average $$L_1$$ norms of errors are taken as the disparity and classification measure between the training and test gait patterns to differentiate between ACL-D and ACL-I knees. Finally, experiments are carried out on forty-three patients to assess the effectiveness of the proposed method. By using the leave-one-out cross-validation style under normal and fast walking speed conditions, the correct classification rates for discriminating between ACL-D and ACL-I knees are reported to be 95.61$$\%$$ and 93.03$$\%$$, respectively. Compared with other state-of-the-art methods, the results demonstrate that gait alterations in the presence of chronic ACL deficiency can be visualized through cyclograms and phase portraits, and can be detected with superior performance.

Classification of Gait Patterns Using Kinematic and Kinetic Features, Gait Dynamics and Neural Networks in Patients with Unilateral Anterior Cruciate Ligament Deficiency

The anterior cruciate ligament (ACL) plays an important role in controlling knee joint stability. The literature provides conflicting information on whether patients with ACL deficiency exhibits gait adaptations. The aim of this study is to investigate if the use of neural networks with a new pattern recognition-based method can differentiate gait patterns between ACL deficient (ACL-D) knee and contralateral ACL-intact (ACL-I) knee in patients with unilateral ACL deficiency. The proposed method is divided into two stages. In the training stage, kinematic and kinetic gait variables are measured and compared between the two lower extremities. Gait dynamics underlying gait patterns of ACL-D and ACL-I knees are locally accurately modeled and approximated by radial basis function (RBF) neural networks via deterministic learning theory. The derived knowledge of approximated gait dynamics is preserved in constant RBF networks. In the classification stage, a bank of dynamical estimators is constructed using the preserved constant RBF networks to represent the learned training gait patterns. By comparing the set of estimators with a test gait pattern, the generated average $$L_1$$ norms of errors are taken as the difference and classification measure between the training and test gait patterns. Finally, experiments are carried out on forty-three patients to assess the effectiveness of the proposed method. By using the leave-one-out cross-validation style under normal and fast walking speed conditions, the correct classification rates for discriminating between ACL-D and ACL-I knees are reported to be 95.9 $$\%$$ and 94.0 $$\%$$ , respectively. Compared with other state-of-the-art methods, the results demonstrate that gait alterations in the presence of chronic ACL deficiency can be detected with superior performance and support the validity of the proposed method.

Parkinson's disease classification using gait analysis via deterministic learning

Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). In this paper, we propose a method to classify (diagnose) patients with PD and healthy control subjects using gait analysis via deterministic learning theory. The classification approach consists of two phases: a training phase and a classification phase. In the training phase, gait characteristics represented by the gait dynamics are derived from the vertical ground reaction forces under the usual and self-selected paces of the subjects. The gait dynamics underlying gait patterns of healthy controls and PD patients are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. The gait patterns of healthy controls and PD patients constitute a training set. In the classification phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern of a certain PD patient to be classified (diagnosed), a set of classification errors are generated. The average L1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test PD gait pattern according to the smallest error principle. When the gait patterns of 93 PD patients and 73 healthy controls are classified with five-fold cross-validation method, the accuracy, sensitivity and specificity of the results are 96.39%, 96.77% and 95.89%, respectively. Based on the results, it may be claimed that the features and the classifiers used in the present study could effectively separate the gait patterns between the groups of PD patients and healthy controls.

View-invariant gait recognition via deterministic learning

Performance of gait recognition can be affected by many factors, especially by the variation of view angle which will significantly change the available visual features for matching. In this paper, we present a new method to eliminate the effect of view angle for efficient gait recognition via deterministic learning theory. The width of the binarized silhouette models the periodic deformation of human gait shape and is selected as the gait feature. It captures the spatio-temporal characteristics of each individual, represents the dynamics of gait motion, and sensitively reflects the variance between gait patterns across various views. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, gait dynamics underlying different individuals׳ gaits observed from different view angles are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In order to address the problem of view change no matter the variation is small or significantly large, the training patterns from different views constitute a uniform training dataset containing all kinds of gait dynamics of each individual observed across various views. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern whose view pattern contained in the prior training dataset, a set of recognition errors are generated. The average L1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, comprehensive experiments are carried out on the widely adopted multiview gait databases: CASIA-B and CMU MoBo to demonstrate the effectiveness of the proposed approach.

Gait recognition across different walking speeds via deterministic learning

Deformation of gait silhouettes caused by objects under different walking speeds has a significant effect on the performance of gait recognition. In this paper, we present an algorithm via deterministic learning theory to eliminate the effect of walking speed for efficient gait recognition in the lateral view. Three kinds of silhouette features are selected. They capture the spatio-temporal characteristics of each individual׳s movement and represent the dynamics of gait motion. They also can sensitively reflect the variance between gait patterns under different walking speeds. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, human gait dynamics underlying different individuals׳ gaits across different walking speeds are locally accurately approximated by radial basis function (RBF) neural networks. Obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In order to handle the problem of speed change no matter the variation is small or significantly large, the training patters under different walking speeds constitute a uniform training dataset containing all kinds of gait dynamics of each individual under different walking speeds. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern whose speed pattern included in the prior training dataset, a set of recognition errors are generated. The average L1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Finally, comprehensive experiments are carried out on the most well-known public gait databases: the CMU, the OU-ISIR, and the CASIA gait database C to demonstrate the recognition performance of the proposed algorithm.

Silhouette-based gait recognition via deterministic learning

In this paper, we present a new silhouette-based gait recognition method via deterministic learning theory, which combines spatio-temporal motion characteristics and physical parameters of a human subject by analyzing shape parameters of the subject׳s silhouette contour. It has been validated only in sequences with lateral view, recorded in laboratory conditions. The ratio of the silhouette׳s height and width (H–W ratio), the width of the outer contour of the binarized silhouette, the silhouette area and the vertical coordinate of centroid of the outer contour are combined as gait features for recognition. They represent the dynamics of gait motion and can more effectively reflect the tiny variance between different gait patterns. The gait recognition approach consists of two phases: a training phase and a test phase. In the training phase, the gait dynamics underlying different individuals׳ gaits are locally accurately approximated by radial basis function (RBF) networks via deterministic learning theory. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the test phase, a bank of dynamical estimators is constructed for all the training gait patterns. The constant RBF networks obtained from the training phase are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, the recognition performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches on the most well-known public gait databases: CASIA, CMU MoBo and TUM GAID.

Model-Based Human Gait Recognition Via Deterministic Learning

In this paper, we present a new model-based approach for human gait recognition in the sagittal plane via deterministic learning (DL) theory. Side silhouette lower limb joint angles characterize the gait system dynamics and are selected as the gait feature. Locally accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks through DL. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern can be rapidly recognized according to the smallest error principle. In contrast to other existing approaches, the main focus of this paper is on obtaining and reusing the knowledge of the gait system dynamics. Finally, experiments are carried out on the CASIA-A and CASIA-B gait databases to benchmark the effectiveness of the proposed approach.

Human gait recognition via deterministic learning

Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. Human gait recognition is a typical difficulty in the area of dynamical pattern recognition. It classifies and identifies individuals by their time-varying gait signature data. Recently, a new dynamical pattern recognition method based on deterministic learning theory was presented, in which a time-varying dynamical pattern can be effectively represented in a time-invariant manner and can be rapidly recognized. In this paper, we present a new model-based approach for human gait recognition via the aforementioned method, specifically for recognizing people by gait. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, side silhouette lower limb joint angles and angular velocities are selected as gait features. A five-link biped model for human gait locomotion is employed to demonstrate that functions containing joint angle and angular velocity state vectors characterize the gait system dynamics. Due to the quasi-periodic and symmetrical characteristics of human gait, the gait system dynamics can be simplified to be described by functions of joint angles and angular velocities of one side of the human body, thus the feature dimension is effectively reduced. Locally-accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks (NNs) through deterministic learning. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles versus angular velocities. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. In the test phase, by comparing the set of estimators with the test gait pattern, a set of recognition errors are generated, and the average L(1) norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern similar to one of the training gait patterns can be rapidly recognized according to the smallest error principle. Finally, experiments are carried out on the NLPR and UCSD gait databases to demonstrate the effectiveness of the proposed approach.