A strathclyde cluster model for gait kinematic measurement using functional methods: a study of inter-assessor reliability analysis with comparison to anatomical models

Context expectation influences the gait pattern biomechanics

Beyond classical aspects related to locomotion (biomechanics), it has been hypothesized that walking pattern is influenced by a combination of distinct computations including online sensory/perceptual sampling and the processing of expectations (neuromechanics). Here, we aimed to explore the potential impact of contrasting scenarios ("risky and potentially dangerous" scenario; "safe and comfortable" scenario) on walking pattern in a group of healthy young adults. Firstly, and consistently with previous literature, we confirmed that the scenario influences gait pattern when it is recalled concurrently to participants' walking activity (motor interference). More intriguingly, our main result showed that participants' gait pattern is also influenced by the contextual scenario when it is evoked only before the start of walking activity (motor expectation). This condition was designed to test the impact of expectations (risky scenario vs. safe scenario) on gait pattern, and the stimulation that preceded walking activity served as prior. Noteworthy, we combined statistical and machine learning (Support-Vector Machine classifier) approaches to stratify distinct levels of analyses that explored the multi-facets architecture of walking. In a nutshell, our combined statistical and machine learning analyses converge in suggesting that walking before steps is not just a paradox.

Test-retest reliability of entire time-series data from hip, knee and ankle kinematics and kinetics during one-leg hops for distance: Analyses using integrated pointwise indices

Motion capture systems enable in-depth interpretations of human movements based on data from three-dimensional joint angles and moments. Such analyses carry important bearings for evaluation of movement control during for instance hop landings among sports-active individuals from a performance perspective but also in rehabilitation. Recent statistical development allows analysis of entire time-series of angle and moment during hops using functional data analysis, but the reliability of such multifaceted data is not established. We used integrated pointwise indices (intra-class correlation, ICC; standard error of measurement, SEM) to establish the test-retest reliability of three-dimensional hip, knee and ankle angle and moment curves during landings of one-leg hop for distance (OLHD) in 23 asymptomatic individuals aged 18-28. We contrasted these findings to reliability of discrete variables extracted at specific events (initial contact, peak value). We extended the calculations of ICC and SEM to handle unbalanced situations (varying number of repetitions) to include all available data. Hip and knee angle curves proved reliable with stable ICC curves throughout the landing, with integrated ICCs ≥ 0.71 for all planes except for knee internal/external rotation (ICC = 0.57). Hip and knee moment curves and ankle angle and moments were less reliable and less stable, particularly in the first ~ 10-25% of the landing (integrated ICCs 0.44-0.57). Curve data were generally not in agreement with the results for discrete event data, thus advocating analysis of curve data which contains more information. To conclude, hip and knee angle curve data during OLHD landings can reliably be evaluated, while moment curves necessitate careful consideration.