Model-based 3D gait biometrics

There have as yet been few gait biometrics approaches which use temporal 3D data. Clearly, 3D gait data conveys more information than 2D data and it is also the natural representation of human gait perceived by human. In this paper we explore the potential of using model-based methods in a 3D volumetric (voxel) gait dataset. We use a structural model including articulated cylinders with 3D Degrees of Freedom (DoF) at each joint to model the human lower legs. We develop a simple yet effective model-fitting algorithm using this gait model, correlation filter and a dynamic programming approach. Human gait kinematics trajectories are then extracted by fitting the gait model into the gait data. At each frame we generate a correlation energy map between the gait model and the data. Dynamic programming is used to extract the gait kinematics trajectories by selecting the most likely path in the whole sequence. We are successfully able to extract both gait structural and dynamics features. Some of the features extracted here are inherently unique to 3D data. Analysis on a database of 46 subjects each with 4 sample sequences, shows an encouraging correct classification rate and suggests that 3D features can contribute even more

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Extraction of bodily features for gait recognition and gait attractiveness evaluation

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1319-2. Copyright @ 2012 Springer.Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.Dorothy Hodgkin Postgraduate Award and HEFCE

BIOMECHANICAL MODELS AND MEASURING TECHNIQUES FOR ULTRASOUND-BASED MEASURING SYSTEM DURING GAIT

Nowadays clinical motion analysis is a usual method. More and more laboratories offer their facilities and use this investigation for supporting doctors in their decisions. During the past two years a new and modern on-line motion analysis system was established at the Department of Applied Mechanics which is capable for a complex analysis of the upper limb, the gait, the run, other cyclic movements, and the spine. This paper focuses on the presentation of a new 3D motion analysis technique for treadmill walking. An ultrasound-based 3D measurement system and a measuring arrangement developed were used to measure and determine gait parameters during treadmill walking. The model considers each limb segment to be a rigid body, linked to each other by a joint. This paper also presents a new 3D motion analysis software package for treadmill walking and introduce the DataManager developed. We studied knee kinematics and temporal-distance gait measurement parameters (step length, stride length, stride width, etc.) to be obtained from treadmill walking. Treadmill walking allows the analysis of several cycles of each subject. On the basis of the analysis the standard deviation of temporal-gait parameters and the knee kinematics data of each subject can be established. The 3D movement analysis system presented is a suitable and standardized procedure for quick gait analysis

Performance analysis of gait recognition with large perspective distortion

In real security scenarios, gait data may be highly distorted due to perspective effects and there may be significant change in appearance, orientation and occlusion between different measurements. To deal with this problem, a new identification technique is proposed by reconstructing 3D models of the walking subject, which are then used to identify subject images from an arbitrary camera. 3D models in one gait cycle are aligned to match silhouettes in a 2D gait cycle by estimating the positions of a 3D and 2D gait cycles in a 3D space. This allows the gait data in a gallery and probe share the same appearance, perspective and occlusion. Generic Fourier Descriptors are used as gait features. The performance is evaluated using a new collected dataset of 17 subjects walking in a narrow walkway. A Correct Classification Rate of 98:8% is achieved. This high recognition rate has still been achieved using a modest number of features. The analysis indicate that the technique can handle truncated gait cycles of different length and is insensitive to noisy silhouettes. However, calibration errors have a negative impact upon recognition performance

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

THE EFFICACY OF VIDEO-BASED MARKER-LESS TRACKING SYSTEM IN GAIT ANALYSIS

An alternative to the 3D motion capture is the marker-less 3D video tracking system. Though not rigorously tested yet, the 3D marker-less video tracker would break new grounds if it is possible of extracting similar kinematic parameters as the gold standard 3D marker based motion capturers. The aim of our study is to explore the feasibility of a video based marker-less system. A series of gait analysis tests were carried out on ten subjects with a marker and marker-less system simultaneously. The study suggests potential applications in gait analysis in the academic classrooms and clinical settings where observations of anatomical motions provide meaningful feedback

An Embedded Gait Analysis System for CNS Injury Patients

Clinical evaluation of CNS injury patients before and after treatment is an essential step in gait rehabilitation. Medical care of gait disturbance for stroke patients is based on different treatments based on clinical and functional evaluations. Evaluation of gait aims at characterizing the motor performance to provide clinicians with information on the patient’s organizational or performance status and to allow them to consider the most appropriate treatment options. A 3D instrumented gait analysis system allows quantification of several parameters at each instant of walking but does not represent gait in daily life conditions. The absence of devices usable in daily life situation constitutes a lack pointed out by clinical practitioners and is at the origin of this work. In the following are described the design and implementation of a wireless embedded system for the collection of spatiotemporal parameters of pathological gait in everyday life. Algorithms estimate joint angles, step length, and gait events and automatically partition data into gait cycles. Experiments have been carried out to accurately evaluate the joint angles, the precision of sensor synchronization, the precision of gait event detection, and the robustness in the case of pathological walk. Comparisons with references given by the 3D instrumented gait analysis system are detailed

Recording and analysis of locomotion in dairy cows with 3D accelerometers

An automated method for lameness detection can be an alternative for detection by regular observations. Accelerometers attached to a leg of the dairy cow can be used to record the locomotion of a dairy cow. In an experiment the 3D acceleration of the right hind leg during walking of three dairy cows was measured and analysed. Nodes with a 3D accelerometer in a wireless sensor network were applied to measure with a frequency of 50 Hz. After data filtering, the data analysis was in two steps: first step detection and secondly the determination of step parameters. Variance analysis was used for step detection. For each step the parameters step length and step time were calculated. The steps and step parameters can be used in future research for gait analysis of lame and non-lame cows. The aim of this paper is to describe the collection and analysis of data in this experiment and to assess the possibilities for gait analysis. It can be concluded that the application of accelerometers in a wireless sensor network gives promising results. Step detection is possible and step parameters can be derived

THE EFFICACY OF VIDEO-BASED MARKER-LESS TRACKING SYSTEM IN GAIT ANALYSIS

An alternative to the 3D motion capture is the marker-less 3D video tracking system. Though not rigorously tested yet, the 3D marker less video tracker would break new grounds if it is possible of extracting similar kinematic parameters as the gold standard 3D marker based motion capturers. The aim of our study is to explore the feasibility of a video based marker-less system which is as accurate and precise as its marker based counterpart. A series of gait analysis tests were carried out on ten subjects with a marker and marker-less system simultaneously. The study suggests potential applications in gait analysis in the academic classrooms and clinical settings where observations of anatomical motions provide meaningful feedback