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Quantifying movement coordination in cross‐country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula‐informed distinct elements of the V2 ski‐skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set‐down, leg push‐off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element‐specific principal component analyses (PCAs)—driven by the variance produced by the technique extremes—resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set‐down, and leg push‐off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski‐skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists. [ABSTRACT FROM AUTHOR]

Nowadays, the diagnosis of breast cancer (DBC) helps doctors make early detection of breast cancer into non-cancerous (benign B) and cancerous (malignant M). Therefore, using machine learning (ML) algorithms is a solution to diagnosing and predicting symptoms related to DBC. The increased computational complexity, data size, overfitting, and longer training times harm early diagnosis accuracy. In this paper, propose a dimensionality reduction model integrating PCA and KNN for early breast cancer detection. which is used to diagnose and predict breast cancer (DPBC) based on reduced data size by selecting the best features that capture most of the variance in the data. The performance of the proposed model is evaluated with indices such as accuracy, precision, and f-score. Results for the DPBC model were obtained by using the Breast Cancer Wisconsin medical datasets(BCW). [ABSTRACT FROM AUTHOR]

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. 'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. 'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

This research investigates the impact of stone washing treatment on denim fabrics. Garments were washed at different conditions, and then the bagging behavior properties and the mechanical properties using the Kawabata evaluation system KES-FB of the treated and untreated denim fabric were analyzed and investigated. Furthermore, to predict the appearance and the bagging ability of denim fabric, the significant contribution of the inputs/outputs parameters on the bagging behavior is discussed using the principal component analysis (PCA) technique. Thanks to this method, the numbers of the influent parameters were reduced at a percentage of 45.71%. The results show the accuracy of the linear regression method to predict the bagging behavior in the experimental design of interest. Moreover, the R2 ranged from 83.6% to 92.7%, traduces that the obtained models seem effective and can help industrials to explain the effect of washing treatment in the bagging behavior of denim fabric.

Paper materials and works of art on paper such as drawings, watercolors, prints, books, and manuscripts represent a large portion of museum, archive, and library collections. However, paper materials are infrequently the subject of technical studies due to inherent limitations in their analysis such as the fragility of the paper substrate, a lack of suitable sampling opportunities, and the presence of mixed, but chemically similar cellulosic materials. The application of principal component analysis (PCA) modeling to specular reflection FTIR data has the potential to provide a non-invasive means of analysis for major and minor components in paper materials. Using known study collection objects, PCA models distinguishing paper sizing materials and fiber types based on specular reflection FTIR data were successfully demonstrated thus providing a plausible alternative method for the identification of paper materials in collection objects without the need for destructive testing or sampling of the object.

This research investigates the impact of stone washing treatment on denim fabrics. Garments were washed at different conditions, and then the bagging behavior properties and the mechanical properties using the Kawabata evaluation system KES-FB of the treated and untreated denim fabric were analyzed and investigated. Furthermore, to predict the appearance and the bagging ability of denim fabric, the significant contribution of the inputs/outputs parameters on the bagging behavior is discussed using the principal component analysis (PCA) technique. Thanks to this method, the numbers of the influent parameters were reduced at a percentage of 45.71%. The results show the accuracy of the linear regression method to predict the bagging behavior in the experimental design of interest. Moreover, the R2 ranged from 83.6% to 92.7%, traduces that the obtained models seem effective and can help industrials to explain the effect of washing treatment in the bagging behavior of denim fabric. [ABSTRACT FROM AUTHOR]

The settlement and compressibility magnitude of the major clayey and marly sediments in Tebessa area (N-E of Algeria) depends on several geotechnical parameters such as compression Cc and recompression Cs indices. The aim of this study was to investigate the parameters related to soil compressibility through tools of statistical analysis, which save time in comparison to multiply repeated laboratory tests. The study also adopted the principal component analysis (PCA) method to eliminate a number of uncorrelated variables that have no influence on the compressibility magnitude, or their impact is insignificant. The highest mean correlation coefficients were obtained for different contributing parameters. Multiple regression analysis has been performed to obtain the best fit model of the output Cc parameter taking into account the best correlation by adding parameters as regressors to reach the highest coefficient of regression R2. The final obtained model of the present case study gives the best fit model with R2 of 0.92 which is a better value compared to different published models in the literature (R2 of 0.7 as maximum). The chosen input parameters using PCA combined with multiple regression analysis allow identifying the most important input parameters that noticeably affect the soil compression index, and provide with the best model for estimating the Cc index. [ABSTRACT FROM AUTHOR]

A growing number of studies apply Principal Component Analysis (PCA) on whole-body kinematic data to facilitate an analysis of posture changes in human movement. An unanswered question is, how much the PCA outcomes depend on the chosen measurement device. This study aimed to assess the internal consistency of PCA outcomes from treadmill walking motion capture data simultaneously collected through laboratory-grade optical motion capture and field-suitable inertial-based motion tracking. Data was simultaneously collected using VICON (whole-body plug-in gait marker positions) and Xsens (body segment positions) from 20 participants during 2-min treadmill walking. Using PCA, Principal Movements (PMs) were determined using two commonly used practices: on an individual and a grouped basis. For both, correlation matrices were used to determine internal consistency between outcomes from either measurement system for each PM. Both individual and grouped approach showed excellent internal consistency between outcomes from the two systems among the lower order PMs. For the individual analysis, high correlations were only found along the diagonal of the correlation matrix while the grouped analysis also showed high off-diagonal correlations. These results have important implications for future application of PCA in terms of the independence of the resulting PM data, the way group-differences are expressed in higher-order PMs and the interpretation of movement complexity. Concluding, while PCA-outcomes from the two systems start to deviate in the higher order PMs, excellent internal consistency was found in the lower order PMs which already represent about 98% of the variance in the dataset.

Paper materials and works of art on paper such as drawings, watercolors, prints, books, and manuscripts represent a large portion of museum, archive, and library collections. However, paper materials are infrequently the subject of technical studies due to inherent limitations in their analysis such as the fragility of the paper substrate, a lack of suitable sampling opportunities, and the presence of mixed, but chemically similar cellulosic materials. The application of principal component analysis (PCA) modeling to specular reflection FTIR data has the potential to provide a non-invasive means of analysis for major and minor components in paper materials. Using known study collection objects, PCA models distinguishing paper sizing materials and fiber types based on specular reflection FTIR data were successfully demonstrated thus providing a plausible alternative method for the identification of paper materials in collection objects without the need for destructive testing or sampling of the object. [ABSTRACT FROM AUTHOR]

Continuous monitoring was carried out on the Ile Vierge lighthouse, the tallest stone lighthouse in Europe (82 m), built in 1902, where the upper part of the tower presents a crack pattern. The monitoring was implemented over 2.5 years to characterize the actions of temperature and wind and to determine their links with crack opening. The variation of each factor measured was analysed and possible correlations between the variables were sought. In addition, the temperatures measured on site were compared with the actions required by Eurocode 1. The quantity of data extracted made it necessary to develop a data analysis method combining Principal Component Analysis, Multiple Regression Analysis, and Artificial Neural Network Analysis. Thus, the long- and short-term behaviour of the instrumented cracks could be identified. The relative influences of the wind and the temperature on crack opening were given by a percentage assessment. [ABSTRACT FROM AUTHOR]

Objective Nutrition plays a role in the development of Crohn's disease [CD] and ulcerative colitis [UC]. However, prospective data on nutrition and disease onset are limited. Here, we analysed dietary patterns and scores in relation to inflammatory bowel disease [IBD] development in a prospective population-based cohort. Methods We analysed 125 445 participants of whom 224 individuals developed de novo UC and 97 CD over a maximum 14-year follow-up period. Participants answered health-related [also prospectively] and dietary questionnaires [FFQ] at baseline. Principal component analysis [PCA] was conducted deriving a-posteriori dietary patterns. Hypotheses-based a-priori dietary scores were also calculated, including the protein score, Healthy Eating Index, LifeLines Diet Score [LLDS], and alternative Mediterranean Diet Score. Logistic regression models were performed between dietary patterns, scores, and IBD development. Results PCA identified five dietary patterns. A pattern characterised by high intake of snacks, prepared meals, non-alcoholic beverages, and sauces along with low vegetables and fruit consumption was associated with higher likelihood of CD development (odds ratio [OR]: 1.16, 95% confidence interval [CI]: 1.03-1.30, p = 0.013). A pattern comprising red meat, poultry, and processed meat, was associated with increased likelihood of UC development [OR: 1.11, 95% CI: 1.01-1.20, p = 0.023]. A high diet quality score [LLDS] was associated with decreased risk of CD [OR: 0.95, 95% CI: 0.92-0.99, p = 0.009]. Conclusions A Western dietary pattern was associated with a greater likelihood of CD development and a carnivorous pattern with UC development, whereas a relatively high diet quality [LLDS] was protective for CD development. Our study strengthens the importance of evaluating dietary patterns to aid prevention of IBD in the general population. [ABSTRACT FROM AUTHOR]

Chronic non-healing wounds represent an increasing problem. In order to enable physicians and nurses to make evidence based decisions on wound treatment, the professional societies call for supporting tools to be offered to physicians. Oxygen supply, bacteria colonization and other parameters influence the healing process. So far, these parameters cannot be monitored in an objective and routinely manner. Existing methods like the microbiological analysis of wound swabs, mean a great deal of effort and partly a long delay. In this paper 42 fluorescence images from 42 patients with diabetic foot ulcer, recorded with a hyperspectral imaging system (TIVITA®), converted for fluorescence imaging, were analysed. Beside the fluorescence images, information about the bacterial colonization is available from microbiological analysis of wound swabs. After preprocessing, principal component analysis, PCA, is used for data analysis with a 405 nm excitation wavelength, the emission wavelength range 510 - 745 nm is used for analysis. After dividing the data into a training and a test dataset it could be shown, that bacteria are detectable in the wound area. A quantification in bacterial colonization counts (BCC) was not in the focus of the research in this study stage.

This study compared whole body kinematics of the clean movement when lifting three different loads, implementing two data analysis approaches based on principal component analysis (PCA). Nine weightlifters were equipped with 39 markers and their motion captured with 8 Vicon cameras at 100 Hz. Lifts of 60, 85, and 95% of the one repetition maximum were analyzed. The first PCA (PCAtrial) analyzed variance among time-normed waveforms compiled from subjects and trials; the second PCA (PCAposture) analyzed postural positions compiled over time, subjects and trials. Load effects were identified through repeated measures ANOVAs with Bonferroni-corrected post-hocs and through Cousineau-Morey confidence intervals. PCAtrial scores differed in the first (p < 0.016, ηp2 = 0.694) and fifth (p < 0.006, ηp2 = 0.768) principal component, suggesting that increased barbell load produced higher initial elevation, lower squat position, wider feet position after squatting, and less inclined arms. PCAposture revealed significant timing differences in all components. We conclude, first, barbell load affects specific aspects of the movement pattern of the clean; second, the PCAtrial approach is better suited for detecting deviations from a mean motion trajectory and its results are easier to interpret; the PCAposture approach reveals coordination patterns and facilitates comparisons of postural speeds and accelerations.

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. ‘Syndromics’ refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

This study compared whole body kinematics of the clean movement when lifting three different loads, implementing two data analysis approaches based on principal component analysis (PCA). Nine weightlifters were equipped with 39 markers and their motion captured with 8 Vicon cameras at 100 Hz. Lifts of 60, 85, and 95% of the one repetition maximum were analyzed. The first PCA (PCAtrial) analyzed variance among time-normed waveforms compiled from subjects and trials; the second PCA (PCAposture) analyzed postural positions compiled over time, subjects and trials. Load effects were identified through repeated measures ANOVAs with Bonferroni-corrected post-hocs and through Cousineau-Morey confidence intervals. PCAtrial scores differed in the first (p < 0.016, η p 2 = 0.694) and fifth (p < 0.006, η p 2 = 0.768) principal component, suggesting that increased barbell load produced higher initial elevation, lower squat position, wider feet position after squatting, and less inclined arms. PCAposture revealed significant timing differences in all components. We conclude, first, barbell load affects specific aspects of the movement pattern of the clean; second, the PCAtrial approach is better suited for detecting deviations from a mean motion trajectory and its results are easier to interpret; the PCAposture approach reveals coordination patterns and facilitates comparisons of postural speeds and accelerations. [ABSTRACT FROM AUTHOR]

One criterion when selecting the number of principal components (PCs) to be considered in a principal component analysis (PCA) is the fraction of overall variance that each PC represents. When applying a PCA to kinematic marker data in postural control research, this criterion relates to the amplitude of postural changes, recently often called “principal (postural) positions” (PPs). However, in the assessment of postural control, important aspects are also how fast posture changes and the acceleration of postural changes, i.e., “principal accelerations” (PAs). The current study compared how much of the total position variance each PP explained (PP_rVAR) and how much of the total acceleration variance each PA explained (PA_rVAR). Furthermore, the frequency content of PP and PA signals were evaluated. Postural movements of 26 participants standing on stable ground or balancing on a multiaxial balance board were analyzed by applying a PCA on 90 marker coordinates. For each PC, PP_rVAR, PA_rVAR, and the Fourier transformations of the PP and PA time series were calculated. The PP_rVAR and the PA_rVAR-distributions differed substantially. The PP-frequency domain was observed well below 5 Hz, the PA-frequency domain up to 5 Hz for stable standing and up to 10 Hz on the balance board. These results confirm that small-amplitude but fast movement components can have a higher impact on postural accelerations—and thus on the forces active in the system—than large-amplitude but slow lower-order movement components. Thus, PA variance and its dependence on filter frequencies should be considered in dimensionality reduction decisions.

Motion analysis is used to study the functionality or dysfunctionality of the neuromuscular system, as human movements are the direct outcome of neuromuscular control. However, motion analysis often relies on measures that quantify simplified aspects of a motion, such as specific joint angles, despite the well-known complexity of segment interactions. In contrast, analyzing whole-body movement patterns may offer a new understanding of movement coordination and movement performance. Clinical research and sports technique evaluations suggest that principal component analysis (PCA) provides novel and valuable insights into control aspects of the neuromuscular system and how they relate to coordinative patterns. However, the implementation of PCA computations are time consuming, and require mathematical knowledge and programming skills, drastically limiting its application in current research. Therefore, the aim of this study is to present the Matlab software tool “PManalyzer” to facilitate and encourage the application of state-of-the-art PCA concepts in human movement science. The generalized PCA concepts implemented in the PManalyzer allow users to apply a variety of marker set independent PCA-variables on any kinematic data and to visualize the results with customizable plots. In addition, the extracted movement patterns can be explored with video options that may help testing hypotheses related to the interplay of segments. Furthermore, the software can be easily modified and adapted to any specific application.

Motion analysis is used to study the functionality or dysfunctionality of the neuromuscular system, as human movements are the direct outcome of neuromuscular control. However, motion analysis often relies on measures that quantify simplified aspects of a motion, such as specific joint angles, despite the well-known complexity of segment interactions. In contrast, analyzing whole-body movement patterns may offer a new understanding of movement coordination and movement performance. Clinical research and sports technique evaluations suggest that principal component analysis (PCA) provides novel and valuable insights into control aspects of the neuromuscular system and how they relate to coordinative patterns. However, the implementation of PCA computations are time consuming, and require mathematical knowledge and programming skills, drastically limiting its application in current research. Therefore, the aim of this study is to present the Matlab software tool "PManalyzer" to facilitate and encourage the application of state-of-the-art PCA concepts in human movement science. The generalized PCA concepts implemented in the PManalyzer allow users to apply a variety of marker set independent PCA-variables on any kinematic data and to visualize the results with customizable plots. In addition, the extracted movement patterns can be explored with video options that may help testing hypotheses related to the interplay of segments. Furthermore, the software can be easily modified and adapted to any specific application. [ABSTRACT FROM AUTHOR]

Abstract Human movements, recorded through kinematic data, can be described by means of principal component analysis (PCA) through a small set of variables representing correlated segment movements. The PC-eigenvectors then form a basis in the associated vector space of postural changes. Similar to 3D movements, the kinematics in this posture space can be quantified through ' principal' positions (PPs), velocities (PVs) and accelerations (PAs). The PAs represent a novel set of variables characterizing neuro-muscular control. The aim of the current technical note was to (i) compare the variance explained by PAs with the variance explained by PPs; (ii) clarify the relationship between PAs and segment accelerations; and (iii) compare variability of the first principal acceleration (PA 1) with the local dynamic stability (largest Lyapunov exponent, LyE) of the first principal position (PP 1). A PCA was applied on 3D upper-body positions collected by an Xsens inertial sensor system as nineteen volunteers performed a bimanual repetitive tapping task. The main finding revealed that the PP-explained variance considerably differed from the PA-explained variance, indicating that the latter should be considered when reducing the dimensionality in postural movement analysis through a PCA. Further, the current study formally established that the acceleration curves obtained from differentiating segment positions and from linear combinations of PAs are identical. Finally, a strong correlation, r (17) = 0.92, p < 0.001, was observed between the cycle-to-cycle variability in PA 1 and the LyE calculated for PP 1 , supporting the notion that PA variability and LyE share some of the information they provide about movement control. [ABSTRACT FROM AUTHOR]

Nigg and colleagues propose two new paradigms, the Muscle Tuning and the Preferred Movement Path concepts. The purpose of this commentary is to discuss plausibility and challenges of these two concepts. Both concepts are highly plausible from a mechanical point of view and they also go in line with every-day observations. The main challenges for the muscle tuning paradigm are that (a) this mechanism is only one of several mechanisms in how the body adapts to impacts, and (b) it is very difficult to develop testable predictions from this paradigm since the mechanical (vibrational) properties of the leg are highly subject-specific and complex. The main open questions regarding the preferred movement path paradigm relate to (a) its integration with the concepts for movement variability, and (b) to the circumstances under which the preferred movement path might change. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive investigation on the impact of increasing NaCl concentrations on hydroponically grown Stevia rebaudiana cultivars (Shoutian-2 and Fengtian). Growth parameters including plant height, biomass and physiological responses including osmotic potential were measured. In addition, the levels of steviol glycosides, elements and primary metabolites were measured and statistically evaluated. The cultivar Fengtian grew faster, accumulated less Na + and compatible organic solutes, and more K + in the leaves, as compared to the cv. Shoutian-2. Metabolite analysis identified 81 differentially accumulated metabolites, indicating an alteration in the metabolite phenotype of both cultivars upon exposure to salinity A general increase in many amino acids, amines, sugars and sugar phosphates with a concurrent decrease in most organic acids; including tricarboxylic acid (TCA) cycle intermediates, was observed. In the more salt tolerant cv. Fengtian, the levels of hexose phosphates and metabolites involved in cellular protection increased in response to salinity. These metabolites remained unchanged in the sensitive cv. Shoutian-2. Interestingly, salt treatment notably increased the rebaudioside A concentration by 53% while at the same time stevioside decreased by 38% in Fengtian which has important implications for controlling the relative amounts of reboudioside A and stevioside. The findings of this study leads to the conclusion that mild salinity stress can increase the yield of sweetener compounds, which is dependent on the cultivar and the level of salinity stress. [ABSTRACT FROM AUTHOR]

This work deals with analysis and modelling of the radionuclides 210 Pb and 210 Po in the food-chain lichen-reindeer-man in addition to 210 Po and 137 Cs in top predators. By using the methods of Partial Least Square Regression (PLSR) the atmospheric deposition of 210 Pb and 210 Po is predicted at the sample locations. Dynamic modelling of the activity concentration with differential equations is fitted to the sample data. Reindeer lichen consumption, gastrointestinal absorption, organ distribution and elimination is derived from information in the literature. Dynamic modelling of transfer of 210 Pb and 210 Po to reindeer meat, liver and bone from lichen consumption, fitted well with data from Sweden and Finland from 1966 to 1971. The activity concentration of 210 Pb in the skeleton in man is modelled by using the results of studying the kinetics of lead in skeleton and blood in lead-workers after end of occupational exposure. The result of modelling 210 Pb and 210 Po activity in skeleton matched well with concentrations of 210 Pb and 210 Po in teeth from reindeer-breeders and autopsy bone samples in Finland. The results of 210 Po and 137 Cs in different tissues of wolf, wolverine and lynx previously published, are analysed with multivariate data processing methods such as Principal Component Analysis PCA, and modelled with the method of Projection to Latent Structures , PLS, or Partial Least Square Regression PLSR. [ABSTRACT FROM AUTHOR]

Background Movement variability in sustained repetitive tasks is an important factor in the context of work-related musculoskeletal disorders. While a popular hypothesis suggests that movement variability can prevent overuse injuries, pain evolving during task execution may also cause variability. The aim of the current study was to investigate, first, differences in movement behavior between volunteers with and without work-related pain and, second, the influence of emerging pain on movement variability. Methods Upper-body 3D kinematics were collected as 22 subjects with musculoskeletal disorders and 19 healthy volunteers performed a bimanual repetitive tapping task with a self-chosen and a given rhythm. Three subgroups were formed within the patient group according to the level of pain the participants experienced during the task. Principal component analysis was applied to 30 joint angle coordinates to characterize in a combined analysis the movement variability associated with reconfigurations of the volunteers' postures and the cycle-to-cycle variability that occurred during the execution of the task. Findings Patients with no task-related pain showed lower cycle-to-cycle variability compared to healthy controls. Findings also indicated an increase in movement variability as pain emerged, manifesting both as frequent postural changes and large cycle-to-cycle variability. Interpretation The findings suggested a relationship between work-related musculoskeletal disorders and movement variability but further investigation is needed on this issue. Additionally, the findings provided clear evidence that pain increased motor variability. Postural reconfigurations and cycle-to-cycle variability should be considered jointly when investigating movement variability and musculoskeletal disorders. [ABSTRACT FROM AUTHOR]

The main purposes of the current study were to examine bilateral asymmetry in postural control during single-leg standing between the dominant and non-dominant legs using a novel analysis approach based on principal component analysis (PCA). It was hypothesized that the asymmetry might manifest as differences in the coordinative structure (control strategies), or as differences in the frequency or regularity of corrective interventions of the motor control system. The static and dynamic leg dominance of 26 active young adults (14 males and 12 females) was determined from their preferred leg for dynamic and for static tasks. Then postural movements during one-leg standing were recorded with a standard marker-based motion capture system and analyzed by a PCA. The coordinative structure of postural movements was quantified using the relative variance of the principal movement components (PMs). Then the PMs were differentiated to obtain postural accelerations, from which two variables characterizing the activity (frequency and regularity) of the postural control system were derived. There were no differences in the coordinative structure, neither for dynamic nor for static leg preference. However, both variables characterizing asymmetries in the postural accelerations showed significant differences in specific PMs. Dynamic leg dominance yielded more and larger effects than static leg dominance. In the opinion of the authors, the PM-specificity of limb dominance agrees with principles of movement control derived from optimal feedback control theory. In summary, the current study suggests that leg dominance should be considered in clinical testing; different effects in different movement components should be expected; and one-leg standing should be seen as a dynamic, rather than as a static task. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sample entropy (SaEn) applied on center-of-pressure (COP) data provides a measure for the regularity of human postural control. Two mechanisms could contribute to altered COP regularity: first, an altered temporal structure (temporal regularity) of postural movements (H1); or second, altered coordination between segment movements (coordinative complexity; H2). The current study used rapid, voluntary head-shaking to perturb the postural control system, thus producing changes in COP regularity, to then assess the two hypotheses. Sixteen healthy participants (age 26.5 ± 3.5; seven females), whose postural movements were tracked via 39 reflective markers, performed trials in which they first stood quietly on a force plate for 30 s, then shook their head for 10 s, finally stood quietly for another 90 s. A principal component analysis (PCA) performed on the kinematic data extracted the main postural movement components. Temporal regularity was determined by calculating SaEn on the time series of these movement components. Coordinative complexity was determined by assessing the relative explained variance of the first five components. H1 was supported, but H2 was not. These results suggest that moderate perturbations of the postural control system produce altered temporal structures of the main postural movement components, but do not necessarily change the coordinative structure of intersegment movements.

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.

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. [ABSTRACT FROM AUTHOR]

A growing number of studies apply Principal Component Analysis (PCA) on whole-body kinematic data to facilitate an analysis of posture changes in human movement. An unanswered question is, how much the PCA outcomes depend on the chosen measurement device. This study aimed to assess the internal consistency of PCA outcomes from treadmill walking motion capture data simultaneously collected through laboratory-grade optical motion capture and field-suitable inertial-based motion tracking. Data was simultaneously collected using VICON (whole-body plug-in gait marker positions) and Xsens (body segment positions) from 20 participants during 2-min treadmill walking. Using PCA, Principal Movements (PMs) were determined using two commonly used practices: on an individual and a grouped basis. For both, correlation matrices were used to determine internal consistency between outcomes from either measurement system for each PM. Both individual and grouped approach showed excellent internal consistency between outcomes from the two systems among the lower order PMs. For the individual analysis, high correlations were only found along the diagonal of the correlation matrix while the grouped analysis also showed high off-diagonal correlations. These results have important implications for future application of PCA in terms of the independence of the resulting PM data, the way group-differences are expressed in higher-order PMs and the interpretation of movement complexity. Concluding, while PCA-outcomes from the two systems start to deviate in the higher order PMs, excellent internal consistency was found in the lower order PMs which already represent about 98% of the variance in the dataset., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Van Andel, Mohr, Schmidt, Werner and Federolf.)

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• Increased support surface instabilities modulated bilateral muscle coordination. • Modulation of bilateral muscle coordination was seen in specific muscles. • Coordination behaviors differed between dorsiflexor and plantar flexor muscles. • For dorsiflexor muscle, coordination increased with increased instability. • For plantar flexor muscles, coordination decreased with increased instability. Balance exercises have proven effective in enhancing and regaining neuromuscular control. However, how the bilateral homonymous muscles are coordinated to achieve bipedal equilibrium remains unclear. In terms of increasingly difficult balance tasks, the current study focused on two levels of muscle coordination: individual homonymous muscles and groups of homonymous muscles. In 25 physically active young adults, a cross-correlation between the bilateral electromyographic (EMG) signals of both legs (i.e., bilateral EMG-EMG correlation) was conducted on seven muscles measured when performing bipedal balancing on three different support surface instabilities. Then, the patterns of bilateral EMG-EMG cross-correlation coefficients were determined through a principal component analysis (PCA). It was hypothesized that modulations of bilateral lower-limb muscle coordination should be observed in the specific relevant muscles or in the patterns of bilateral muscle coordination. The results showed that only the first hypothesis was supported as changes in the strength of bilateral EMG-EMG correlation (p ≤ 0.005) and in the time delays (p < 0.001) were mostly restricted in the lower-leg muscles. The dorsiflexor and plantar flexor muscles showed opposite coordination behaviors. Larger bilateral EMG-EMG correlation and shorter time delays appeared only in the tibialis anterior muscle, suggesting that bilateral dorsiflexor muscle coordination is needed for exercising on multiaxial-unstable platforms. [ABSTRACT FROM AUTHOR]

This paper discusses principal component analysis (PCA) of integral transforms (spectra and autocovariance functions) of time-domain signals. It is illustrated using acoustic emissions from mechanical equipment. It was found that acoustic signals from different stages of operation appeared as distinct clusters in the PCA analysis. The clusters moved when machinery faults were present and the modelling errors also increased under fault conditions; thus, each type of fault had a distinctive signature and could be diagnosed. PCA using autocovariance functions that were derived from the full power spectrum had better performance than spectral PCA using averaged periodograms, and both gave a significant improvement over time-domain PCA. [ABSTRACT FROM AUTHOR]

One approach to investigating sensorimotor control is to assess the accelerations that produce changes in the kinematic state of the system. When assessing complex whole-body movements, structuring the multi-segmental accelerations is important. A useful structuring can be achieved through a principal component analysis (PCA) performed on segment positions followed by double-differentiation to obtain "principal accelerations" (PAs). In past research PAs have proven sensitive to altered motor control strategies, however, the interrelationship between PAs and muscle activation (surface electromyography, sEMG) have never been determined. The purpose of the current study was therefore to assess the relationship between PAs and sEMG signals recorded from muscles controlling the ankle joint during one-leg standing trials. It was hypothesized that medium correlation should be observed when accounting for neurophysiologic latencies (electro-mechanical delay). Unipedal balancing on a level-rigid ground was performed by 25 volunteers. sEMG activities were recorded from the tibialis anterior, peroneus longus, gastrocnemius medialis, and soleus muscles of the stance leg. The first eight PA-time series were determined from kinematic marker data. Then, a cross-correlation analysis was performed between sEMG and PA time series. We found that peak correlation coefficients for many participants aligned at time delays between 0.116 and 0.362 s and were typically in the range small to medium (|r| = 0.1 to 0.6). Thus, the current study confirmed a direct association between many principal accelerations PA(t) and muscle activation signals recorded from four muscles crossing the ankle joint complex. The combined analysis of PA and sEMG signals allowed exploring the neuromuscular function of each muscle in different postural movement components. [ABSTRACT FROM AUTHOR]

• An original method based on PCA is proposed to analyse QCM data. • PCA based analysis can improve sensitivity and limit of detection. • The PCA method is benchmarked in the case of biotin-streptavidin binding. • The PCA method is applied for detecting GFAP in serum solution. The quartz-crystal-microbalance (QCM) is a widespread analytical tool used in various fields such as chemistry, life-sciences, and soft-matter research. The QCM can detect mass and viscoelastic changes over its sensors surface. The sensor signal stems from the variation of the quartz resonant acoustic frequencies, called harmonics. To date, the operator selects and analyses one or few of these harmonics, therefore introducing an operator bias. This standard method also causes a massive loss of information owing to the discarding of the unanalyzed signals. Here, we develop a novel method for QCM data-analysis based on principal-component-analysis (PCA) that takes advantage from the whole harmonic dataset and avoids any operator bias. Such method is applied to two different molecular adsorption experiments. One is a standard benchmark experiment based on the streptavidin-biotin binding, widely used in the biosensing field. The other experiment is a case-study where an antigen-antibody binding is tested in presence of biological noise. The antigen of choice is the glial-fibrillary-acidic-protein (GFAP), recently discovered as a brain-damage biomarker. In both cases, we observed an improved sensitivity and limit-of-detection compared to the standard method. Our analysis further reveals a decreased, or nearly unvaried, noise-level. The proposed PCA-based approach standardizes the QCM data-analysis procedure and improves its performance. This method, besides being a useful tool for the QCM community, can be customized to other sensing technologies whose response is made of several variables. [ABSTRACT FROM AUTHOR]