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

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

Instrumenting gait with an accelerometer: A system and algorithm examination

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required

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

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

The Haptic Bracelets: Gait rehabilitation after Stroke

Restoring mobility and rehabilitation of gait are high priorities for rehabilitation of neurological conditions. Cueing using metronomic rhythmic sensory stimulation has been shown to improve gait, but most versions of this approach have used auditory and visual cues. In contrast, we propose the development of a prototype wearable system for rhythmic cueing based on haptics. The main aim of this research is to investigate how real-time gait monitoring and rhythmic haptic cueing can assist with gait rehabilitation for neurological conditions

What Can Quantitative Gait Analysis Tell Us about Dementia and Its Subtypes? A Structured Review

Distinguishing dementia subtypes can be difficult due to similarities in clinical presentation. There is increasing interest in discrete gait characteristics as markers to aid diagnostic algorithms in dementia. This structured review explores the differences in quantitative gait characteristics between dementia and healthy controls, and between four dementia subtypes under single-task conditions: Alzheimer’s disease (AD), dementia with Lewy bodies and Parkinson’s disease dementia, and vascular dementia. Twenty-six papers out of an initial 5,211 were reviewed and interpreted using a validated model of gait. Dementia was associated with gait characteristics grouped by slower pace, impaired rhythm, and increased variability compared to normal aging. Only four studies compared two or more dementia subtypes. People with AD are less impaired in pace, rhythm, and variability domains of gait compared to non-AD dementias. Results demonstrate the potential of gait as a clinical marker to discriminate between dementia subtypes. Larger studies using a more comprehensive battery of gait characteristics and better characterized dementia sub-types are required