Explore the vast range of titles available.

Human upright posture is maintained by postural movements, which can be quantified by “principal movements” (PMs) obtained through a principal component analysis (PCA) of kinematic marker data. The current study expands the concept of “principal movements” in analogy to Newton׳s mechanics by defining “principal position” (PP), “principal velocity” (PV), and “principal acceleration” (PA) and demonstrates that a linear combination of PPs and PAs determines the center of pressure (COP) variance in upright standing. Twenty-one subjects equipped with 27-markers distributed over all body segments stood on a force plate while their postural movements were recorded using a standard motion tracking system. A PCA calculated on normalized and weighted posture vectors yielded the PPs and their time derivatives, the PVs and PAs. COP variance explained by the PPs and PAs was obtained through a regression analysis. The first 15 PMs quantified 99.3% of the postural variance and explained 99.60%±0.22% (mean±SD) of the anterior–posterior and 98.82±0.74% of the lateral COP variance in the 21 subjects. Calculation of the PMs thus provides a data-driven definition of variables that simultaneously quantify the state of the postural system (PPs and PVs) and the activity of the neuro-muscular controller (PAs). Since the definition of PPs and PAs is consistent with Newton׳s mechanics, these variables facilitate studying how mechanical variables, such as the COP motion, are governed by the postural control system.

Missing information in motion capture data caused by occlusion or detachment of markers is a common problem that is difficult to avoid entirely. The aim of this study was to develop and test an algorithm for reconstruction of corrupted marker trajectories in datasets representing human gait. The reconstruction was facilitated using information of marker inter-correlations obtained from a principal component analysis, combined with a novel weighting procedure. The method was completely data-driven, and did not require any training data. We tested the algorithm on datasets with movement patterns that can be considered both well suited (healthy subject walking on a treadmill) and less suited (transitioning from walking to running and the gait of a subject with cerebral palsy) to reconstruct. Specifically, we created 50 copies of each dataset, and corrupted them with gaps in multiple markers at random temporal and spatial positions. Reconstruction errors, quantified by the average Euclidian distance between predicted and measured marker positions, was ≤ 3 mm for the well suited dataset, even when there were gaps in up to 70% of all time frames. For the less suited datasets, median reconstruction errors were in the range 5-6 mm. However, a few reconstructions had substantially larger errors (up to 29 mm). Our results suggest that the proposed algorithm is a viable alternative both to conventional gap-filling algorithms and state-of-the-art reconstruction algorithms developed for motion capture systems. The strengths of the proposed algorithm are that it can fill gaps anywhere in the dataset, and that the gaps can be considerably longer than when using conventional interpolation techniques. Limitations are that it does not enforce musculoskeletal constraints, and that the reconstruction accuracy declines if applied to datasets with less predictable movement patterns.

Marker-based human motion analysis is an important tool in clinical research and in many practical applications. Missing marker information caused by occlusions or a marker falling off is a common problem impairing data quality. The current paper proposes a conceptually new gap filling algorithm and presents results from a proof-of-principle analysis. The underlying idea of the proposed algorithm was that a multitude of internal and external constraints govern human motion and lead to a highly subject-specific movement pattern in which all motion variables are intercorrelated in a specific way. Two principal component analyses were used to determine how the coordinates of a marker with gaps correlated with the coordinates of the other, gap-free markers. Missing marker data could then be reconstructed through a series of coordinate transformations. The proposed algorithm was tested by reconstructing artificially created gaps in a 20-step walking trial and in an 18-s one-leg balance trial. The measurement accuracy's dependence on the marker position, the length of the gap, and other parameters were evaluated. Even if only 2 steps of walking or 1.8 s of postural sway (10% of the whole marker data) were provided as input in the current study, the reconstructed marker trajectory differed on average no more than 11 mm from the originally measured trajectory. The reconstructed result improved further, on average, to distances below 5 mm if the marker trajectory was available more than 50% of the trial. The results of this proof-of-principle analysis supported the assumption that missing marker information can be reconstructed from the intercorrelations between marker coordinates, provided that sufficient data with complete marker information is available. Estimating missing information cannot be avoided entirely in many situations in human motion analysis. For some of these situations, the proposed reconstruction method may provide a better solution than what is currently available.

Visual guidance of gait is an important skill for everyday mobility. While this has often been studied using eye-tracking techniques, recent studies have shown that visual exploration involves more than just the eye; head movement and potentially the whole body is involved for successful visual exploration. This study aimed to assess coordinative patterns associated with head movement and it was hypothesized that these patterns would span across the body, rather than being localized. Twenty-one (after exclusions) healthy young adult volunteers followed a treadmill walking protocol designed to elicit different types of head movements (no stimuli compared to stimuli requiring horizontal, vertical, and mixed gaze shifts). Principal Component Analysis was used to establish whole-body correlated patterns of marker movement (Principal Movements; PMs) related to the activity of the head. In total 37 higher order PMs were found to be associated with head movement, two of these showed significant differences between trials associated with strong head rotations in the horizontal and sagittal plane. Both of these were associated with a whole-body pattern of activity. An analysis of the higher order components revealed that exploratory head movements are associated with distinct movement patterns, which span across the body. This shows that visual exploration can produce whole-body movement patterns that have a potentially destabilizing influence. These findings shed new light on established results in visual search research and hold relevance for fall and injury prevention.

The aim of this study was to identify movement synergies during normal-walking that can differentiate healthy adults in terms of gait adaptability at various speeds. To this end, the association between movement synergies and lower-limb coordination variability or Deviation Phase (DP) was investigated. This study also investigated the moderating effect of movement synergies on the relationship between DP and the smoothness of arm-swing motion (NJI). A principal component analysis of whole-body marker trajectories from normal-walking treadmill trials at 0.8m/s, 1.2m/s and 1.6m/s was undertaken. Both DP and NJI were derived from approx. 8 minutes of perturbed-walking treadmill trials. Principal movement components, PMk, were derived and the RMS of the 2nd-order differentiation of these PMk (PAkRMS) were included as independent variables representing the magnitude of neuromuscular control in each PMk. Each PAkRMS were input into maximal linear mixed-effects models against DP and (DP x NJI) respectively. A stepwise elimination of terms and comparison of models using Anova identified optimal models for both aims. The principal movement related to the push-off mechanism of gait (PA4RMS) was identified as an optimal model and demonstrated a significant negative effect on DP however this effect may differ considerably across walking-speeds. An optimal model for describing the variance in (DP x NJI) included a fixed-effect of PA6RMS representing Right-Left side weight transfer was identified. The hypotheses that individuals who exhibited greater control on specific kinematic synergies would exhibit variations during perturbed walking was substantiated. Supporting evidence for the role of movement synergies during the double-support phase of gait in proactively correcting balance was presented as well as the potential for this approach in targeted rehabilitation. The potential influence of leg dominance on gait adaptability was also discussed. Future studies should investigate further the role of walking-speed and leg dominance on movement synergies and look to generalize these findings to patient populations.

Leg dominance reflects the preferential use of one leg over another and is typically attributed to asymmetries in the neural circuitry. Detecting leg dominance effects on motor behavior, particularly during balancing exercises, has proven difficult. The current study applied a principal component analysis (PCA) on kinematic data, to assess bilateral asymmetry on the coordinative structure (hypothesis H1) or on the control characteristics of specific movement components (hypothesis H2). Marker-based motion tracking was performed on 26 healthy adults (aged 25.3 ± 4.1 years), who stood unipedally on a multiaxial unstable board, in a randomized order, on their dominant and non-dominant leg. Leg dominance was defined as the kicking leg. PCA was performed to determine patterns of correlated segment movements (“principal movements” PMks). The control of each PMk was characterized by assessing its acceleration (second-time derivative). Results were inconclusive regarding a leg-dominance effect on the coordinative structure of balancing movements (H1 inconclusive); however, different control (p = 0.005) was observed in PM3, representing a diagonal plane movement component (H2 was supported). These findings supported that leg dominance effects should be considered when assessing or training lower-limb neuromuscular control and suggest that specific attention should be given to diagonal plane movements.

Walking stability is considered a necessary physical performance for preserving independence and preventing falls. The current study investigated the correlation between walking stability and two clinical markers for falling risk. Principal component analysis (PCA) was applied to extract the three-dimensional (3D) lower-limb kinematic data of 43 healthy older adults (69.8 ± 8.5 years, 36 females) into a set of principal movements (PMs), showing different movement components/synergies working together to accomplish the walking task goal. Then, the largest Lyapunov exponent (LyE) was applied to the first five PMs as a measure of stability, with the interpretation that the higher the LyE, the lower the stability of individual movement components. Next, the fall risk was determined using two functional motor tests—a Short Physical Performance Battery (SPPB) and a Gait Subscale of Performance-Oriented Mobility Assessment (POMA-G)—of which the higher the test score, the better the performance. The main results show that SPPB and POMA-G scores negatively correlate with the LyE seen in specific PMs (p ≤ 0.009), indicating that increasing walking instability increases the fall risk. The current findings suggest that inherent walking instability should be considered when assessing and training the lower limbs to reduce the risk of falling.

Previous research on elderly people has suggested that footwear may improve neuromuscular control of motion. If footwear does in fact improve neuromuscular control, then such an influence might already be present in young, healthy adults. A feature that is often used to assess neuromuscular control of motion is the level of gait asymmetry. The objectives of the study were (a) to develop a comprehensive asymmetry index (CAI) that is capable of detecting gait asymmetry changes caused by external boundary conditions such as footwear, and (b) to use the CAI to investigate whether footwear influences gait asymmetry during running in a healthy, young cohort. Kinematic and kinetic data were collected for both legs of 15 subjects performing five barefoot and five shod over-ground running trials. Thirty continuous gait variables including ground reaction forces and variables of the hip, knee, and ankle joints were computed for each leg. For each individual, the differences between the variables for the right and left leg were calculated. Using this data, a principal component analysis was conducted to obtain the CAI. This study had two main outcomes. First, a sensitivity analysis suggested that the CAI had an improved sensitivity for detecting changes in gait asymmetry caused by external boundary conditions. The CAI may, therefore, have important clinical applications such as monitoring the progress of neuromuscular diseases (e.g. stroke or cerebral palsy). Second, the mean CAI for shod running (131.2 ± 48.5; mean ± standard deviation) was significantly lower (p = 0.041) than the CAI for barefoot running (155.7 ± 39.5). This finding suggests that in healthy, young adults gait asymmetry is reduced when running in shoes compared to running barefoot, which may be a result of improved neuromuscular control caused by changes in the afferent sensory feedback.

Sample entropy (SaEn), calculated for center of pressure (COP) trajectories, is often distinct for compromised postural control, e.g., in Parkinson, stroke, or concussion patients, but the interpretation of COP-SaEn remains subject to debate. The purpose of this paper is to test the hypotheses that COP-SaEn is related (Hypothesis 1; H1) to the complexity of the postural movement structures, i.e., to the utilization and coordination of the mechanical degrees of freedom; or (Hypothesis 2; H2) to the irregularity of the individual postural movement strategies, i.e., to the neuromuscular control of these movements. Twenty-one healthy volunteers (age 26.4 ± 2.4; 10 females), equipped with 27 reflective markers, stood on a force plate and performed 2-min quiet stances. Principal movement strategies (PMs) were obtained from a principal component analysis (PCA) of the kinematic data. Then SaEn was calculated for the COP and PM time-series. H1 was tested by correlating COP-SaEn to the relative contribution of the PMs to the subject specific overall movement and H2 by correlating COP-SaEn and PM-SaEn. Both hypotheses were supported. This suggests that in a healthy population the COP-SaEn is linked to the complexity of the coordinative structure of postural movements, as well as to the irregularity of the neuromuscular control of specific movement components.

Anterior cruciate ligament (ACL) injuries are common, and re-injuries remain high despite advances in rehabilitation. Return-to-sport (RTS) assessments focus on strength, clinical and hop tests, and time-based criteria but often exclude objective movement quality measures. Biomechanical deficits during jump-landings can persist post-reconstruction, contributing to re-injury risk. Fatigue further alters neuromuscular control, potentially exacerbating risk-related movement patterns, yet most RTS tests are conducted in non-fatigued states. This study introduces a motion dataset of 2199 trials across six bilateral (countermovement jump, drop jump) and unilateral (forward hop, countermovement jump, cross-over hop, 90° medial rotation hop) jump-landing tasks, performed under fatigued and non-fatigued conditions. The dataset includes 3D motion capture and ground reaction force data, including full-body inverse kinematics data (joint angles: knee, hip flexion, abduction, rotation, ankle flexion, trunk and pelvis) processed in OpenSim software for 43 participants comprising individuals with prior ACL injury (n = 21) and healthy controls (n = 22). The dataset enables detailed analyses of jump-landing biomechanics under fatigue, aiming to improve RTS decision-making to reduce re-injury risk.