Kinematic and ground reaction force accommodation during weighted walking

Weighted walking is a functional activity common in daily life and can influence risks for musculoskeletal loading, injury and falling. Much information exists about weighted walking during military, occupational and recreational tasks, but less is known about strategies used to accommodate to weight carriage typical in daily life. The purposes of the study were to examine the effects of weight carriage on kinematics and peak ground reaction force (GRF) during walking, and explore relationships between these variables. Twenty subjects walked on a treadmill while carrying 0, 44.5 and 89 N weights in front of the body. Peak GRF, sagittal plane joint/segment angular kinematics, stride length and center of mass (COM) vertical displacement were measured. Changes in peak GRF and displacement variables between weight conditions represented accommodation. Effects of weight carriage were tested using analysis of variance. Relationships between peak GRF and kinematic accommodation variables were examined using correlation and regression. Subjects were classified into sub-groups based on peak GRF responses and the correlation analysis was repeated. Weight carriage increased peak GRF by an amount greater than the weight carried, decreased stride length, increased vertical COM displacement, and resulted in a more extended and upright posture, with less hip and trunk displacement during weight acceptance. A GRF increase was associated with decreases in hip extension (|r| = .53, p = .020) and thigh anterior rotation (|r| = .57, p = .009) displacements, and an increase in foot anterior rotation displacement (|r| = .58, p = .008). Sub-group analysis revealed that greater GRF increases were associated with changes at multiple sites, while lesser GRF increases were associated with changes in foot and trunk displacement. Weight carriage affected walking kinematics and revealed different accommodation strategies that could have implications for loading and stability.     

Dual relationship activities: principal component analysis of counselors' attitudes

The British Columbian Members of the Canadian Guidance and Counselling Association were surveyed to explore their attitudes regarding dual relationships. Of 529 deliverable surveys, 206 usable returns yielded a response rate of 39%. The survey instrument collected data regarding respondents' characteristics and ethicality ratings of 39 dual relationship activity items. An exploratory principal components analysis was performed on responses, resulting in a 4-factor equation, which accounted for 44% of the total variance. The results suggest that, although conceptual considerations of dual relationship typology do underlay the resultant factors, the relative ethicality of each item is also influential.     

Physical activity and sedentary lifestyle in children as time-limited functions: Usefulness of the principal component analysis method

This study was designed to examine the hourly variation in and the interplay between physical activity and sedentary behavior (SB) in order to highlight key time periods for physical activity interventions for children. Data for physical activity and SB obtained with ActiGraph in 56 boys and 47 girls aged from 8 to 11 years. These data were divided into sixty minute-time samples for moderate-to-vigorous physical activity (MVPA) and SB, and analyzed using a principal component analysis (PCA) and correlation statistics. The PCA provides 10 factors which account for 80.4% of the inertia. Only two of these factors did not display competition between MVPA and SB. Contrary to some reports, a coefficient of correlation of -.68 (p < 10(-4)) was found between daily time spent at MVPA and SB. Some salient traits of children's behaviors were shown through PCA. The results suggested that efficacy of interventions targeting the morning hours (07:00 AM-11:59 AM) and the afternoon period (02:00 PM-05:59 PM) warrants attention.     

External analysis with three-mode principal component models

Through external analysis of two-mode data one attempts to map the elements of one mode (e.g., attributes) as vectors in a fixed space of the elements of the other mode (e.g., stimuli). This type of analysis is extended to three-mode data, for instance, when the ratings are made by more individuals. It is described how alternating least squares algorithms for three-mode principal component analysis (PCA) are adapted to enable external analysis, and it is demonstrated that these techniques are useful for exploring differences in the individuals' mappings of the attribute vectors in the fixed stimulus space. Conditions are described under which individual differences may be ignored. External three-mode PCA is illustrated with data from a person perception experiment, designed after two studies by Rosenberg and his associates whose results were used as external information.We gratefully acknowledge the assistance of Piet Brouwer in implementing the external analysis options in the TUCKALS programs.     

Multiple visualisation in data analysis: a ViSta application for principal component analysis

Multiple visualisation (MV) is a statistic graphical method barely applied in data analysis practice, even though it provides interesting features for this purpose. This paper: (1) describes the application of the MV graphical method; (2) presents a number of rules related to the design of an MV; (3) introduces a general outline for developing MVs and shows how MV may be implemented in the ViSta statistical system; (4) illustrates this strategy by means of an example of MV oriented to principal component analysis; and, finally, (5) discusses some limitations of using and developing MVs.     

Early changes in muscle activation patterns of toddlers during walking

Early locomotor behavior has been the focus of considerable attention by developmentalists over several decades. Few studies have addressed explicitly patterns of muscle activity that underlie this coordination pattern. Our purposes were to illustrate a method to determine objectively the onset and offset of muscle firings during early walking and to investigate the emergence of patterns of activation of the core locomotor muscles. We tested eight toddlers as they walked overground at walking onset (max. of 3-6 independent steps) and after three months of walking experience. Surface electrodes monitored activity of the gastrocnemius, tibialis anterior, quadriceps, and hamstrings. We reduced EMG signals to a frame-by-frame designation of "on-off," followed by muscle state and co-contraction analyses, and probability distributions for each muscle's activity across multiple cycles. Our results clearly show that at walking onset muscle activity was highly variable with few, if any, muscles showing recurring patterns of behavior, within or among toddlers. Variability and co-activation decreased with walking experience but remained inconsistent, in contrast to the significant increase in stability shown for joint coordination and endpoint (foot placement) parameters. We propose this trend emerges because of the high number of options (muscle combinations) available. Toddlers learn first to marshal sufficient force to balance and make forward progress but slowly discover how to optimize these resources.     

Muscle coordination patterns in regulation of medial gastrocnemius activation during walking

The ankle plantar flexion in the late stance phase is referred to as the ankle push-off. When the ankle push-off force is enhanced, compensatory adjustments occur in the adjacent phases. The muscle control that achieves these compensatory movements remains unknown, although they are expected to be coordinately regulated across multiple muscles and phases. Muscle synergy is used as a quantification technique for muscle coordination, and this analysis enables the comparison of synchronized activity between multiple muscles. Therefore, this study aimed to elucidate the tuning of muscle synergies in muscle activation adjustment of push-off. It is hypothesized that muscle activation adjustment of push-off is performed in the muscle synergy related to ankle push-off and in the muscle synergy that activates during the adjacent push-off phase. Eleven healthy men participated, and participants manipulated the activity of the medial gastrocnemius during walking through visual feedback. Two conditions were compared as experimental conditions: increasing the muscle activity to 1.6 times that during normal walking (High) and matching it with that during normal walking (Normal). Twelve muscle activities in the trunk and lower limb and kinematic data were recorded. Muscle synergies were extracted by the non-negative matrix factorization. No significant difference was observed in the number of synergies (High: 3.5 ± 0.8, Normal: 3.7 ± 0.9, p = 0.21) and muscle synergy activation timing and duration between the High and Normal conditions (p > 0.27). However, significant differences were observed in the peak muscle activity during the late stance phase of the rectus femoris (RF), biceps femoris (BF) between conditions (RF at High: 0.32 ± 0.21, RF at Normal: 0.45 ± 0.17, p = 0.02; BF at High: 0.16 ± 0.01, BF at Normal: 0.08 ± 0.06 p = 0.02). Although the quantification of force exertion has not been conducted, the modulation of RF and BF activation could have occurred due to the attempts to help knee flexion. Muscle synergies during normal walking are therefore maintained, and slight adjustments in the amplitude of muscle activity occurred for each muscle.     

Asymptotic biases of the unrotated/rotated solutions in principal component analysis

Asymptotic biases of the parameter estimates in principal component analysis with substantial misspecification are derived. The solutions for unstandardized and standardized observed variables are considered with and without orthogonal and oblique rotations. The distribution of observed variables can be non‐normal as long as the finite fourth‐order moments of the observed variables exist. When multivariate normality holds for the observed variables, substantial reduction of the amount of computation can be achieved. Numerical examples with simulations are given, with some discussion on the tendency of the biases to reduce the absolute values of parameter estimates.     

Beyond principal component analysis: A trilinear decomposition model and least squares estimation

The paper derives sufficient conditions for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis.     

Biomechanical motor patterns in normal walking

Motor patterns in normal human gait are evident in several biomechanical and EMG analyses over the stride period. Some of these patterns are invariant over the stride period with changes of cadence, whole others are closely correlated with speed changes. The findings for slow, natural, and fast walking are summarized: 1. Joint angle patterns over the stride period are quite invariant, and do not change with cadence; 2. Moment of force patterns at the ankle are least variable and quite consistent at all speeds; 3. A recently defined support moment is quite consistent at all speeds. 4. Moments at the knee and hip are highly variable at all cadences but decrease their variability as cadence increases; 5. Mechanical power patterns at all joints show consistent timing over the stride period; 6. EMG profiles of 5 muscles show consistent timing over the stride, but the amplitude increases as walking speed increases. Arguments are presented to support the concept that walking speed is largely controlled by gain and that the timing of the motor patterns, which is extremely tightly synchronized with the anatomical position, is under major afferent control.     

Kinematic motion analysis and muscle activation patterns of continuous reaching in survivors of stroke

Coordinated reaching requires continuous interaction between the efferent motor output and afferent feedback; this interaction may be significantly compromised following a stroke. The authors sought to characterize how survivors of stroke generate continuous, goal-directed reaching. Sixteen survivors of stroke completed functional testing of the stroke-affected side and a continuous reaching task between 2 targets with both sides. Motion analysis and electromyography data were collected to determine segmental contributions to reach (e.g., amount of compensatory trunk), spatiotemporal parameters (e.g., peak velocities), and muscle activation patterns (MAP). Repeated measures analyses of variance compared how survivors of stroke reach with the stroke-affected versus less affected sides. Correlations were determined between kinematic outcomes and functional ability. Participants used significantly more trunk movement and less shoulder flexion and elbow extension when reaching with the stroke-affected side. This corresponded with less muscle activity in the proximal musculature including the anterior, middle, and posterior deltoid on the stroke-affected side. There were significant correlations between the segmental contributions to reach, functional ability, and MAPs. Survivors of stroke generate reduced MAPs in the stroke-affected side corresponding to altered segmental kinematics and function ability. These findings suggest that impairments in the ability to generate sufficient MAPs may contribute to the difficulty in generating continuous reaching motions.     

CPCA: A program for principal component analysis with external information on subjects and variables

A program is described for principal component analysis with external information on subjects and variables. This method is calledconstrained principal component analysis (CPCA), in which regression analysis and principal component analysis are combined into a unified framework that allows a full exploration of data structures both within and outside known information on subjects and variables. Many existing methods are special cases of CPCA, and the program can be used for multivariate multiple regression, redundancy analysis, double redundancy analysis, dual scaling with external criteria, vector preference models, and GMANOVA (growth curve models).     

Physical activity,motor competence and movement and gait quality: A principal component analysis

ObjectiveWhile novel analytical methods have been used to examine movement behaviours, to date, no studies have examined whether a frequency-based measure, such a spectral purity, is useful in explaining key facets of human movement. The aim of this study was to investigate movement and gait quality, physical activity and motor competence using principal component analysis.MethodsSixty-five children (38 boys, 4.3 ± 0.7y, 1.04 ± 0.05 m, 17.8 ± 3.2 kg, BMI; 16.2 ± 1.9 kg∙m²) took part in this study. Measures included accelerometer-derived physical activity and movement quality (spectral purity), motor competence (Movement Assessment Battery for Children 2nd edition; MABC2), height, weight and waist circumference. All data were subjected to a principal component analysis, and the internal consistency of resultant components were assessed using Cronbach's alpha.ResultsTwo principal components, with excellent internal consistency (Cronbach α >0.9) were found; the 1st principal component, termed “movement component”, contained spectral purity, traffic light MABC2 score, fine motor% and gross motor% (α = 0.93); the 2nd principal component, termed “anthropometric component”, contained weight, BMI, BMI% and body fat% (α = 0.91).ConclusionThe results of the present study demonstrate that accelerometric analyses can be used to assess motor competence in an automated manner, and that spectral purity is a meaningful, indicative, metric related to children's movement quality.     

Kernel principal component analysis combining rotation forest method for linearly inseparable data

Rotation forest (RoF) is an ensemble classifier combining linear analysis theories and decision tree algorithms. In recent existing works, RoF was widely applied to various fields with outstanding performance compared to traditional machine learning techniques, given that a reasonable number of base classifiers is provided. However, the conventional RoF algorithm suffers from classifying linearly inseparable datasets. In this study, a hybrid algorithm integrating kernel principal component analysis (KPCA) and the conventional RoF algorithm is proposed to overcome the classification difficulty for linearly inseparable datasets. The radial basis function (RBF) is selected as the kernel for the KPCA method to establish the nonlinear mapping for linearly inseparable data. Moreover, we evaluate various kernel parameters for better performance. Experimental results show that our algorithm improves the performance of RoF with linearly inseparable datasets, and therefore provides higher classification accuracy rates compared with other ensemble machine learning methods.     

An electro-physiological temporal principal component analysis of processing stages of number comparison in developmental dyscalculia

Developmental dyscalculia (DD) still lacks a generally accepted definition. A major problem is that the cognitive component processes contributing to arithmetic performance are still poorly defined. By a reanalysis of our previous event-related brain potential (ERP) data (Soltész et al., 2007) here our objective was to identify and compare cognitive processes in adolescents with DD and in matched control participants in one-digit number comparison. To this end we used temporal principal component analysis (PCA) on ERP data. First, PCA has identified four major components explaining the 85.8% of the variance in number comparison. Second, the ERP correlate of the most frequently used marker of the so-called magnitude representation, the numerical distance effect, was intact in DD during all processing stages identified by PCA. Third, hemispheric differences in the first temporal component and group differences in the second temporal component suggest executive control differences between DD and controls.     

Discriminating between strong and weak structures in three‐mode principal component analysis

Recently, a number of model selection heuristics (i.e. DIFFIT, CORCONDIA, the numerical convex hull based heuristic) have been proposed for choosing among Parafac and/or Tucker3 solutions of different complexity for a given three‐way three‐mode data set. Such heuristics are often validated by means of extensive simulation studies. However, these simulation studies are unrealistic in that it is assumed that the variance in real three‐way data can be split into two parts: structural variance, due to a true underlying Parafac or Tucker3 model of low complexity, and random noise. In this paper, we start from the much more reasonable assumption that the variance in any real three‐way data set is due to three different sources: (1) a strong Parafac or Tucker3 structure of low complexity, accounting for a considerable amount of variance, (2) a weak Tucker3 structure, capturing less prominent data aspects, and (3) random noise. As such, Parafac and Tucker3 simulation studies are run in which the data are generated by adding a weak Tucker3 structure to a strong Parafac or Tucker3 one and perturbing the resulting data with random noise. The design of these studies is based on the reanalysis of real data sets. In these studies, the performance of the numerical convex hull based model selection method is evaluated with respect to its capability of discriminating strong from weak underlying structures. The results show that in about two‐thirds of the simulated cases, the hull heuristic yields a model of the same complexity as the strong underlying structure and thus succeeds in disentangling strong and weak underlying structures. In the vast majority of the remaining third, this heuristic selects a solution that combines the strong structure and (part of) the weak structure.     

In-shoe multi-segment foot kinematics of children during the propulsive phase of walking and running

Certain styles of children’s shoes reduce 1st metatarsophalangeal joint (MTPJ) and midfoot motion during propulsion of walking. However, no studies have investigated if the splinting effect of shoes on children’s 1st MTPJ and midfoot motion occurs during running. This study investigated the effect of sports shoes on multi-segment foot kinematics of children during propulsion of walking and running. Twenty children walked and ran at a self-selected velocity while barefoot and shod in a random order. Reflective markers were used to quantify sagittal plane motion of the 1st MTPJ and three-dimensional motion of the midfoot and ankle. Gait velocity increased during shod walking and running and was considered a covariate in the statistical analysis. Shoes reduced 1st MTPJ motion during propulsion of walking from 36.0° to 10.7° and during running from 31.5° to 12.6°. Midfoot sagittal plane motion during propulsion reduced from 22.5° to 6.2° during walking and from 27.4° to 9.6° during running. Sagittal plane ankle motion during propulsion increased during shod running from 26.7° to 34.1°. During propulsion of walking and running, children’s sports shoes have a splinting effect on 1st MTPJ and midfoot motion which is partially compensated by an increase in ankle plantarflexion during running.     

A biomechanical analysis of the 'high guard' position of arms during walking in toddlers

In this study, we investigated biomechanical contributions of the high guard position of the arms observed only in a subgroup of toddlers at very early stages of gait development. Six healthy toddlers who showed this peculiar arm posture were involved in this study. They participated in two data collection sessions (1 month apart). We used three-dimensional analysis of arm posture during gait to estimate the changes in forces and torques generated by the arms and acting on the upper trunk segment. Across visits, toddlers’ increase in walking speeds coincided with lowering arm postures. Despite the apparent trend of changes in arm posture in this group of toddlers, the interaction between arm posture and upper trunk position created a variety of changes in forces and torques among individuals. Findings of this study exhibited an example of the exploration of dynamics by toddlers in the early stage of gait development.     

Kinematic parameters of hand movement during a disparate bimanual movement task in children with unilateral Cerebral Palsy

Children with unilateral Cerebral Palsy (uCP) experience problems performing tasks requiring the coordinated use of both hands (bimanual coordination; BC). Additionally, some children with uCP display involuntary symmetrical activation of the opposing hand (mirrored movements). Measures, used to investigate therapy-related improvements focus on the functionality of the affected hand during unimanual or bimanual tasks. None however specifically address spatiotemporal integration of both hands. We explored the kinematics of hand movements during a bimanual task to identify parameters of BC. Thirty-seven children (aged 10.9 ± 2.6 years, 20 male) diagnosed with uCP participated. 3D kinematic motion analysis was performed during the task requiring opening of a box with their affected- (AH) or less-affected hand (LAH), and pressing a button inside with the opposite hand. Temporal and spatial components of data were extracted and related to measures of hand function and level of impairment. Total task duration was correlated with the Jebsen–Taylor Test of Hand Function in both conditions (either hand leading with the lid-opening). Spatial accuracy of the LAH when the box was opened with their AH was correlated with outcomes on the Children’s Hand Use Experience Questionnaire. Additionally, we found a subgroup of children displaying non-symmetrical movement interference associated with greater movement overlap when their affected hand opened the box. This subgroup also demonstrated decreased use of the affected hand during bimanual tasks. Further investigation of bimanual interference, which goes beyond small scaled symmetrical mirrored movements, is needed to consider its impact on bimanual task performance following early unilateral brain injury.