Academic literature on the topic 'Gait analysis'

Two methods were used in this research. The pedobarograph is a relatively novel method of gait analysis for animals which enables footfall patterns to be recorded, enabling spatial parameters (step length, width and angle) and plantar pressure patterns to be described and measured. A Kistler force plate was then used to measure the three-dimensional ground reaction forces (GRF's) produced during walking. Speed and cadence can be calculated using either system. Gait patterns are described for normal birds, and for different strains of broilers, raised on different feeding regimes. All the gait parameters were very variable, both between birds, and within the same bird, even when bodyweight and speed were controlled for. Despite the high variability, however, significant differences were identified in many of the gait parameters between the different groups. The vertical and craniocaudal GRF's of Brown Leghorns showed similar characteristics to those produced in human walking. The peak vertical forces were of a similar order of magnitude in the birds as in humans (125-150 % bodyweight), and the peak craniocaudal forces, and the rate of change of force, were closely tied to speed. All the GRF's in the birds increased significantly with increasing speed, except for braking rate (which was more variable) and stance time (which decreased significantly). The mediolateral forces were much greater in the birds than have been reported for other species, however, with peaks of 10-22% bodyweight. Analysis of plantar pressures showed that the pressure were concentrated on the digital pads, with the lowest pressure on the metatarsal pad (131 kNm-2), and highest pressure on the medial toe (up to 218 kNm-2).Combined gait analysis and morphometric studies of ad libitum-fed selected broilers identified many ways in which their gait deviated from that of relaxed broilers and Brown Leghorns, in ways which would serve to increase stability and decrease stresses on the skeleton.

This master’s thesis project “Instrumentation of Gait Analysis” was carried out at and funded by Integrum AB, Gothenburg, Sweden. Force analysis is critical during rehabilitation process of amputation patients, since overloading might place the bone-implant interface at risk; while underloading might extend unnecessarily the already long rehabilitation program [1]. Highly developed sensor and data acquisition technology provides an easy and reliable way to do force analysis. This thesis introduces the problem and provides background material regarding Orthotics and Prosthetics, including osseointegration. The existing gait analysis techniques and sensor technology will be described. Based upon the criteria that are introduced, a suitable sensor and integration platform was selected to implement a new gait analysis system. Several trials of different gait states are proposed using the prototype to do gait analysis, the results are presented and analyzed. The success of this prototype has lead to plans to design an Osseointegrated Prostheses for the Rehabilitation of Amputees(OPRA) product

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 121-124).<br>This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features.<br>by Lily Lee.<br>Ph.D.

Human gait identification has become an active area of research due to increased security requirements. Human gait identification is a potential new tool for identifying individuals beyond traditional methods. The emergence of motion capture techniques provided a chance of high accuracy in identification because completely recorded gait information can be recorded compared with security cameras. The aim of this research was to build a practical method of gait identification and investigate the individual characteristics of gait. For this purpose, a gait identification approach was proposed, identification results were compared by different methods, and several studies about the individual characteristics of gait were performed. This research included the following: (1) a novel, effective set of gait features were proposed; (2) gait signatures were extracted by three different methods: statistical method, principal component analysis, and Fourier expansion method; (3) gait identification results were compared by these different methods; (4) two indicators were proposed to evaluate gait features for identification; (5) novel and clear definitions of gait phases and gait cycle were proposed; (6) gait features were investigated by gait phases; (7) principal component analysis and the fixing root method were used to elucidate which features were used to represent gait and why; (8) gait similarity was investigated; (9) gait attractiveness was investigated. This research proposed an efficient framework for identifying individuals from gait via a novel feature set based on 3D motion capture data. A novel evaluating method of gait signatures for identification was proposed. Three different gait signature extraction methods were applied and compared. The average identification rate was over 93%, with the best result close to 100%. This research also proposed a novel dividing method of gait phases, and the different appearances of gait features in eight gait phases were investigated. This research identified the similarities and asymmetric appearances between left body movement and right body movement in gait based on the proposed gait phase dividing method. This research also initiated an analysing method for gait features extraction by the fixing root method. A prediction model of gait attractiveness was built with reasonable accuracy by principal component analysis and linear regression of natural logarithm of parameters. A systematic relationship was observed between the motions of individual markers and the attractiveness ratings. The lower legs and feet were extracted as features of attractiveness by the fixing root method. As an extension of gait research, human seated motion was also investigated.

This thesis analyzes the human action recognition problem. Human actions are modeled as a time evolving temporal texture. Gabor filters, which are proved to be a robust 2D texture representation tool by detecting spatial points with high variation, is extended to 3D domain to capture motion texture features. A well known filtering algorithm and a recent unsupervised clustering algorithm, the Genetic Chromodynamics, are combined to select salient spatio-temporal features of the temporal texture and to segment the activity sequence into temporal texture primitives. Each activity sequence is represented as a composition of temporal texture primitives with its salient spatio-temporal features, which are also the symbols of our codebook. To overcome temporal variation between different performances of the same action, a Profile Hidden Markov Model is applied with Viterbi Path Counting (ensemble training). Not only parameters and structure but also codebook is learned during training.

Background: Gait analysis is used to measure objectively the ability to walk before and after hip and knee joint replacement. Most gait literature to date has measured patients walking at slow speeds, which may have unintentionally characterized successful lower limb operations as the same regardless of its operation type. The aim of this thesis was to use an instrumented treadmill to (1) test the upper limits of gait performance of patients awaiting and after lower limb arthroplasty and (2) describe and compare different joint procedures to healthy controls. Method: Gait analysis was carried out using an instrumented treadmill. Study participants walked at their preferred walking and top walking speed on the treadmill. Their vertical ground reaction force and spatiotemporal data was captured for both limbs by tandem force plates beneath the treadmill's belt. Results: The results suggest that at preferred walking speed on the flat, all subjects' gait characteristics were similar irrespective of joint state. The differences between patient groups became more apparent at top walking speed. Weight acceptance, step length and stride length were the most useful variables assessing these differences. Unicompartmental knee replacement and hip resurfacing had closer to normal gait patterns when compared to total knee replacement and conventional total hip replacement respectively. Predictably patients with knee osteoarthritis had asymmetrical gait patterns, impulse was found to be the best variable distinguishing patients with knee osteoarthritis from healthy controls. The downhill walking assessment established that patients with unicompartmental knee replacements had a more normal gait pattern than total knee replacement patients. Conclusion: The gait results suggest that an instrumented treadmill is a useful metric to describe patient gait patterns. Testing performance at higher walking speeds allowed differences to be detected, which were undetectable at slower speeds. Anatomically conserving procedures such as unicompartmental knee replacement and hip resurfacing appear to have convincing functional advantages compared to less conserving joint replacement procedures.

One of the unique advantages of human gait is that it can be perceived from a distance. A varied range of research has been undertaken within the field of gait recognition. However, in almost all circumstances subjects have been constrained to walk fronto-parallel to the camera with a single walking speed. In this thesis we show that gait has sufficient properties that allows us to exploit the structure of articulated leg motion within single view sequences, in order to remove the unknown subject pose and reconstruct the underlying gait signature, with no prior knowledge of the camera calibration. Articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The variation of motion out of this plane is subtle and negligible in comparison to this major plane of motion. Subsequently, we can model human motion by employing a cardboard person assumption. A subject's body and leg segments may be represented by repeating spatio-temporal motion patterns within a set of bilaterally symmetric limb planes. The static features of gait are defined as quantities that remain invariant over the full range of walking motions. In total, we have identified nine static features of articulated leg motion, corresponding to the fronto-parallel view of gait, that remain invariant to the differences in the mode of subject motion. These features are hypothetically unique to each individual, thus can be used as suitable parameters for biometric identification. We develop a stratified approach to linear trajectory gait reconstruction that uses the rigid bone lengths of planar articulated leg motion in order to reconstruct the fronto-parallel view of gait. Furthermore, subject motion commonly occurs within a fixed ground plane and is imaged by a static camera. In general, people tend to walk in straight lines with constant velocity. Imaged gait can then be split piecewise into natural segments of linear motion. If two or more sufficiently different imaged trajectories are available then the calibration of the camera can be determined. Subsequently, the total pattern of gait motion can be globally parameterised for all subjects within an image sequence. We present the details of a sparse method that computes the maximum likelihood estimate of this set of parameters, then conclude with a reconstruction error analysis corresponding to an example image sequence of subject motion.

Gait recognition algorithms are being increasingly widely researched, however a common assumption is that the subject will be presented side on to the camera. In practice it may not be possible to capture data from this view, so a useful gait recognition algorithm will have to provide a measure of orientation independence. Three gait recognition algorithms are examined and found to perform poorly with nonnormal orientation. The complex detail used for recognition can not be translated between orientations in a holistic silhouette manner. It is shown that orientation independent features can be extracted using a human model. The algorithm is developed and tested on live captured data and found to perform better across orientations than silhouette based approaches. The performance recorded at a single orientation is lower than that of other approaches, however only the motion of the subject is currently used for recognition. More accurate motion estimation will increase performance as will the inclusion of other model based features.