Gait variability: a theoretical framework for gait analysis and biomechanics

Stergiou, Nick

doi:10.1016/b978-0-12-813372-9.00008-7

Cited by 9 publications

(7 citation statements)

References 80 publications

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“…An average gait length was assumed to be 1 second [37], and it takes 4 steps to go from one side of the walkway (embedded with the force plate) to the other, meaning subjects averaged approximately 125 steps on the force plate [38]. Taking more steps (longer time) would increase the strength of the gait pattern however can fatigue the subjects.…”

Section: Gait Pattern Generationmentioning

confidence: 99%

Gait Phase Estimation of Unsupervised Outdoors Walking Using IMUs and a Linear Regression Model

et al. 2022

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Human gait analysis and detection are critical for many applications, including wearable and rehabilitation robotic devices, reducing or tracking injury risk. The proposed work allows researchers to study the gait phase of human subjects in an unsupervised outdoor environment without the need for fixed thresholds and sensor-embedded insoles. We present an experimental protocol to label gait events based on patterns in human subjects from two body-worn inertial measurement units (IMUs). Gait patterns are developed using a force plate and a motion capture system. Upon defining the gait pattern, human subjects walk outdoors for forty minutes to train and test a principal component analysis (PCA)-based linear regression model. Next, gait phase estimation is performed using the defined patterns from other human subjects to accommodate cases where motion capture and force plate data are unavailable. Results showed a minimum normalized gait phase estimation error of 1.81 %, a maximum of 2.48 %, and an average of 2.21 ± 0.258 % for all subjects involved. Results are particularly significant because the proposed work can be expanded to precise control of human-assistive devices, rehabilitation devices, and clinical gait analysis.

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Section: Gait Pattern Generationmentioning

confidence: 99%

Gait Phase Estimation of Unsupervised Outdoors Walking Using IMUs and a Linear Regression Model

et al. 2022

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“…This explanation, however, has several problems. First, the true nature of STV seems to be much more complicated than simply errors in execution [30]. That is, variability is likely to represent both errors and flexibility, meaning that some level of variability is actually desirable [8,30].…”

Section: Discussionmentioning

confidence: 99%

“…First, the true nature of STV seems to be much more complicated than simply errors in execution [30]. That is, variability is likely to represent both errors and flexibility, meaning that some level of variability is actually desirable [8,30]. Second, while steps shorten temporally as velocity increases, they become spatially longer, but both temporal and spatial variability decreased as walking velocity increased (see Figure 1 a,b).…”

Section: Discussionmentioning

confidence: 99%

Increased Spatiotemporal Variability after Stroke is not Just the Outcome of Walking Velocity

Koren

Barzel²,

Shmuelof

et al. 2023

Preprint

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Background: Increased spatiotemporal gait variability is considered a clinical biomarker of ageing and pathology, and a predictor of future falls. Nevertheless, it is unclear whether the increased spatiotemporal variability observed in persons with stroke (PwS) is directly related to the pathology or simply reflects their choice of walking velocity. Research question: Does increased spatiotemporal gait variability directly relate to motor coordination deficits after stroke? Methods: Forty PwS and 20 healthy adults participated in this cross-sectional study. Participants performed the lower-extremity motor coordination test (LEMOCOT) on an electronic mat equipped with force sensors. Then, participants walked for 120 s over a computerized treadmill at their comfortable walking velocity. For the LEMOCOT we used the traditional score of in-target touch count and computed the absolute and variable error around the targets. For gait variability, we extracted the standard deviation of step time, step length, step velocity, and step width. Using a data-based approach, we generated a model of the relationship between velocity and variability and tested the correlations of the velocity-controlled values with the outcome measures from the LEMOCOT. Results: PwS demonstrated increased variability in step time, step length, and step velocity, as well as decreased variability in step width, in comparison to healthy adults, even after controlling for walking velocity. After controlling for walking velocity, we observed that for PwS the LEMOCOT score correlated with the variance in step time, and the variable error in the LEMOCOT correlated with the variance in step length, in step width, and in step velocity. No significant correlation with any of the velocity-controlled step parameters was found for the absolute error in the LEMOCOT. Significance: Decreased performance in the LEMOCOT was associated with increased spatiotemporal variability in PwS, regardless of their walking velocity. Our results demonstrate the connection between lower-extremity coordination impairments and deficits in gait function.

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“…Human gait represents a highly skilled form of movement that is characterized by relatively low fluctuations in timing and positioning of consecutive steps in healthy adults. It is theorized that a lower level of gait variability enables better adaptability of the walking pattern to the environmental changes 3 , 4 . However, heightened gait variability during minimal physical or task constraints is linked with poor behavioral outcomes such as accidental falls, decline in cognition, and mental health 4 – 10 .…”

Section: Introductionmentioning

confidence: 99%

Increased temporal stride variability contributes to impaired gait coordination after stroke

Patel

Enzastiga

Casamento‐Moran

et al. 2022

Sci Rep

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Heightened motor variability is a prominent impairment after stroke. During walking, stroke survivors show increased spatial and temporal variability; however, the functional implications of increased gait variability are not well understood. Here, we determine the effect of gait variability on the coordination between lower limbs during overground walking in stroke survivors. Ambulatory stroke survivors and controls walked at a preferred pace. We measured stride length and stride time variability, and accuracy and consistency of anti-phase gait coordination with phase coordination index (PCI). Stroke survivors showed increased stride length variability, stride time variability, and PCI compared with controls. Stride time variability but not stride length variability predicted 43% of the variance in PCI in the stroke group. Stride time variability emerged as a significant predictor of error and consistency of phase. Despite impaired spatial and temporal gait variability following stroke, increased temporal variability contributes to disrupted accuracy and consistency of gait coordination. We provide novel evidence that decline in gait coordination after stroke is associated with exacerbated stride time variability, but not stride length variability. Temporal gait variability may be a robust indicator of the decline in locomotor function and an ideal target for motor interventions that promote stable walking after stroke.

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Gait variability: a theoretical framework for gait analysis and biomechanics

Cited by 9 publications

References 80 publications

Gait Phase Estimation of Unsupervised Outdoors Walking Using IMUs and a Linear Regression Model

Gait Phase Estimation of Unsupervised Outdoors Walking Using IMUs and a Linear Regression Model

Increased Spatiotemporal Variability after Stroke is not Just the Outcome of Walking Velocity

Increased temporal stride variability contributes to impaired gait coordination after stroke

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