Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System

Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis

Finite element modelling of an energy–storing prosthetic foot during the stance phase of transtibial amputee gait

Energy-storing prosthetic feet are designed to store energy during mid-stance motion and to recover it during latestance motion. Gait analysis is the most commonly used method to characterize prosthetic foot behaviour during walking. In using this method, however, the foot is generally modelled as a rigid body. Therefore, it does not take into account the ability of the foot to deform. However, the way this deformation occurs is a key parameter of various foot properties under gait conditions. The purpose of this study is to combine finite element modelling and gait analysis in order to calculate the strain, stress and energy stored in the foot along the stance phase for self-selected and fast walking speeds. A finite element model, validated using mechanical testing, is used with boundary conditions collected experimentally from the gait analysis of a single transtibial amputee. The stress, strain and energy stored in the foot are assessed throughout the stance phase for two walking speed conditions: a self-selected walking speed (SSWS), and a fast walking speed (FWS). The first maximum in the strain energy occurs during heel loading and reaches 3 J for SSWS and 7 J for FWS at the end of the first double support phase. The second maximum appears at the end of the single support phase, reaching 15 J for SSWS and 18 J for FWS. Finite element modelling combined with gait analysis allows the calculation of parameters that are not obtainable using gait analysis alone. This modelling can be used in the process of prosthetic feet design to assess the behaviour of a prosthetic foot under specific gait conditions

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

On including quality in applied automatic gait recognition

Many gait recognition approaches use silhouette data. Imperfections in silhouette extraction have a negative effect on the performance of a gait recognition system. In this paper we extend quality metrics for gait recognition and evaluate new ways of using quality to improve a recognition system. We demonstrate use of quality to improve silhouette data and select gait cycles of best quality. The potential of the new approaches has been demonstrated experimentally on a challenging dataset, showing how recognition capability can be dramatically improved. Our practical study also shows that acquiring samples of adequate quality in arbitrary environments is difficult and that including quality analysis can improve performance markedly

Gait and cognition: mapping the global and discrete relationships in ageing and neurodegenerative disease

Recent research highlights the association of gait and cognition in older adults but a stronger understanding is needed to discern coincident pathophysiology, patterns of change, examine underlying mechanisms and aid diagnosis. This structured review mapped associations and predictors of gait and cognition in older adults with and without cognitive impairment, and Parkinson's disease. Fifty papers out of an initial yield of 22,128 were reviewed and a model of gait guided analysis and interpretation. Associations were dominated by the pace domain of gait; the most frequently studied domain. In older adults pace was identified as a predictor for cognitive decline. Where comprehensive measurement of gait was conducted, more specific pathological patterns of association were evident highlighting the importance of this approach. This review confirmed a robust association between gait and cognition and argues for a selective, comprehensive measurement approach. Results suggest gait may be a surrogate marker of cognitive impairment and cognitive decline. Understanding the specific nature of this relationship is essential for refinement of diagnostics and development of novel therapies

Gait speed characteristics and Its spatiotemporal determinants in nursing home residents: A cross-sectional study

Fien, S ORCiD: 0000-0003-0181-5458BACKGROUND AND PURPOSE: Low and slowing gait speeds among nursing home residents are linked to a higher risk of disability, cognitive impairment, falls, and mortality. A better understanding of the spatiotemporal parameters of gait that influence declining mobility could lead to effective rehabilitation and preventative intervention. The aims of this study were to objectively quantify the spatiotemporal characteristics of gait in the nursing home setting and define the relationship between these parameters and gait speed. METHODS: One hundred nursing home residents were enrolled into the study and completed 3 habitual gait speed trials over a distance of 3.66 m. Trials were performed using an instrumented gait analysis. The manner in which the spatiotemporal parameters predicted gait speed was examined by univariate and multivariable regression modeling. RESULTS: The nursing home residents had a habitual mean (SD) gait speed of 0.63 (0.19) m/s, a stride length of 0.83 (0.15) m, a support base of 0.15 (0.06) m, and step time of 0.66 (0.12) seconds. Multivariable linear regression revealed stride length, support base, and step time predicted gait speed (R = 0.89, P < .05). Step time had the greatest influence on gait speed, with each 0.1-second decrease in step time resulting in a 0.09 m/s (95% confidence interval, 0.08-0.10) increase in habitual gait speed. CONCLUSIONS: This study revealed step time, stride length, and support base are the strongest predictors of gait speed among nursing home residents. Future research should concentrate on developing and evaluating intervention programs that were specifically designed to focus on the strong predictors of gait speed in nursing home residents. We would also suggest that routine assessments of gait speed, and if possible their spatiotemporal characteristics, be done on all nursing home residents in an attempt to identify residents with low or slowing gait speed