Altering gait variability with an ankle exoskeleton

Antonellis, Prokopios; Galle, Samuel; Clercq, Dirk De; Malcolm, Philippe

doi:10.1371/journal.pone.0205088

Cited by 30 publications

(23 citation statements)

References 67 publications

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“…Although there is no direct measurement of the time profile of metabolic rate during walking, we believe there could be opportunities to obtain additional information by conducting perturbation studies with devices such as exoskeletons [ 18 , 85 , 86 ] or tethers [ 87 , 88 ] that can be programmed to act during specific phases of the gait cycle. Optimizing coefficients in simulation models so that the estimations of the stride average metabolic rate match trends from “rich” datasets with perturbations to different parts of the gait cycle could potentially provide more confidence in estimations of the metabolic rate time profile.…”

Section: Discussionmentioning

confidence: 99%

Differences between joint-space and musculoskeletal estimations of metabolic rate time profiles

2020

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Motion capture laboratories can measure multiple variables at high frame rates, but we can only measure the average metabolic rate of a stride using respiratory measurements. Biomechanical simulations with equations for calculating metabolic rate can estimate the time profile of metabolic rate within the stride cycle. A variety of methods and metabolic equations have been proposed, including metabolic time profile estimations based on joint parameters. It is unclear whether differences in estimations are due to differences in experimental data or due to methodological differences. This study aimed to compare two methods for estimating the time profile of metabolic rate, within a single dataset. Knowledge about the consistency of different methods could be useful for applications such as detecting which part of the gait cycle causes increased metabolic cost in patients. Here we compare estimations of metabolic rate time profiles using a musculoskeletal and a joint-space method. The musculoskeletal method was driven by kinematics and electromyography data and used muscle metabolic rate equations, whereas the joint-space method used metabolic rate equations based on joint parameters. Both estimations of changes in stride average metabolic rate correlated significantly with large changes in indirect calorimetry from walking on different grades showing that both methods accurately track changes. However, estimations of changes in stride average metabolic rate did not correlate significantly with more subtle changes in indirect calorimetry due to walking with different shoe inclinations, and both the musculoskeletal and joint-space time profile estimations did not correlate significantly with each other except in the most downward shoe inclination. Estimations of the relative cost of stance and swing matched well with previous simulations with similar methods and estimations from experimental perturbations. Rich experimental datasets could further advance time profile estimations. This knowledge could be useful to develop therapies and assistive devices that target the least metabolically economic part of the gait cycle.

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Section: Discussionmentioning

confidence: 99%

Differences between joint-space and musculoskeletal estimations of metabolic rate time profiles

2020

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“…The landscape shape of the dependent variables would be different depending on which coefficients in the statistical model are not significantly different from zero (figure 3). To avoid overfitting and to adapt the model for dependent variables that have linear trends, we removed terms that did not significantly contribute using backward stepwise elimination similar to other studies [51,52]. If the resulting trend showed a minimum versus the foot segment angle and/or treadmill grade, the location of the minima was obtained by calculating the minimum of the equation from the linear mixed-effects model (with coefficients shown in electronic supplementary material, table S1) at the different treadmill grades and shoe angles.…”

Section: Statistical Analysesmentioning

confidence: 99%

Modular footwear that partially offsets downhill or uphill grades minimizes the metabolic cost of human walking

et al. 2020

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Walking on different grades becomes challenging on energetic and muscular levels compared to level walking. While it is not possible to eliminate the cost of raising or lowering the centre of mass (COM), it could be possible to minimize the cost of distal joints with shoes that offset downhill or uphill grades. We investigated the effects of shoe outsole geometry in 10 participants walking at 1 m s −1 on downhill, level and uphill grades. Level shoes minimized metabolic rate during level walking ( P second-order effect < 0.001). However, shoes that entirely offset the (overall) treadmill grade did not minimize the metabolic rate of walking on grades: shoes with a +3° (upward) inclination minimized metabolic rate during downhill walking on a −6° grade, and shoes with a −3° (downward) inclination minimized metabolic rate during uphill walking on a +6° grade ( P interaction effect = 0.023). Shoe inclination influenced (distal) ankle joint parameters, including soleus muscle activity, ankle moment and work rate, whereas treadmill grade influenced (whole-body) ground reaction force and COM work rate as well as (distal) ankle joint parameters including tibialis anterior and plantarflexor muscle activity, ankle moment and work rate. Similar modular footwear could be used to minimize joint loads or assist with walking on rolling terrain.

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“…The measurement of gait variability is a method that has shown promise in predicting falls, distinguishing between pathological and healthy individuals, and as an indicator of overall health of the biological system [ 18 , 19 ]. Traditionally, movement variability was thought of as “noise”, but the current consensus is that it is inherent within all biological systems [ 20 , 21 ]. The idea of “healthy variability” was originally put into application in the study of heart rhythm, but most recently has been studied in the context of gait.…”

Section: Introductionmentioning

confidence: 99%

Gait variability is affected more by peripheral artery disease than by vascular occlusion

et al. 2021

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Background Patients with peripheral artery disease with intermittent claudication (PAD-IC) have altered gait variability from the first step they take, well before the onset of claudication pain. The mechanisms underlying these gait alterations are poorly understood. Aims To determine the effect of reduced blood flow on gait variability by comparing healthy older controls and patients with PAD-IC. We also determined the diagnostic value of gait variability parameters to identify the presence of PAD. Methods A cross-sectional cohort design was used. Thirty healthy older controls and thirty patients with PAD-IC walked on a treadmill at their self-selected speed in pain free walking (normal walking for healthy older controls; prior to claudication onset for PAD) and reduced blood flow (post vascular occlusion with thigh tourniquet for healthy older controls; pain for PAD) conditions. Gait variability was assessed using the largest Lyapunov exponent, approximate entropy, standard deviation, and coefficient of variation of ankle, knee, and hip joints range of motion. Receiver operating characteristics curve analyses of the pain free walking condition were performed to determine the optimal cut-off values for separating individuals with PAD-IC from those without PAD-IC. Results and discussion Patients with PAD-IC have increased amount of variability for knee and hip ranges of motion compared with the healthy older control group. Regarding the main effect of condition, reduced blood flow demonstrated increased amount of variability compared with pain free walking. Significant interactions between group and condition at the ankle show increased values for temporal structure of variability, but a similar amount of variability in the reduced blood flow condition. This demonstrates subtle interactions in the movement patterns remain distinct between PAD-IC versus healthy older controls during the reduced blood flow condition. A combination of gait variability parameters correctly identifies PAD-IC disease 70% of the time or more. Conclusions Gait variability is affected both by PAD and by the mechanical induction of reduced blood flow. Gait variability parameters have potential diagnostic ability, as some measures had 90.0% probability of correctly identifying patients with PAD-IC.

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Altering gait variability with an ankle exoskeleton

Cited by 30 publications

References 67 publications

Differences between joint-space and musculoskeletal estimations of metabolic rate time profiles

Differences between joint-space and musculoskeletal estimations of metabolic rate time profiles

Modular footwear that partially offsets downhill or uphill grades minimizes the metabolic cost of human walking

Gait variability is affected more by peripheral artery disease than by vascular occlusion

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