Discrete vs. functional based data to analyze countermovement jump performance

While discrete point analysis (DPA) (e.g. peak power) is by far the most common method of analyzing movement data, it may have significant limitations because it ignores the vast majority of a signal’s data. In response, there has been a small but growing use of methods, such as functional data analysis (FDA), which allow an investigation of the underlying structure of the continuous signal and may therefore provide a more powerful analysis. However, a direct comparison between DPA and FDA has not been previously reported

Suma

Se presenta una propuesta de trabajo en el aula a partir de la lectura de el libro El teorema, de Adam Fawer. Así se estudian conceptos como las probabilidades, la criptografía o los juegos de azar, mediante problemas a resolver en las clases.Universitat de Barcelona. Biblioteca de Ciències de l'Educació; Passeig de la Vall d'hebron, 171; 08035 Barcelona; +34934021035; +34934021034;ES

COS-Weak: Probing the CGM using analogs of weak Mg II absorbers at z < 0.3

We present a sample of 34 weak metal line absorbers at $z< 0.3$ complied via the simultaneous detections ($3\sigma$) of the SiII$\lambda1260$ and CII$\lambda1334$ absorption lines, with $W_{r}$(SiII)$<0.2$ \AA\ and $W_{r}$(CII)$<0.3$ \AA, in archival HST/COS spectra. Our sample increases the number of known low-$z$ "weak absorbers" by a factor of $>5$. The column densities of HI and low-ionization metal lines obtained from Voigt profile fitting are used to build simple photoionization models using CLOUDY. The inferred densities and total hydrogen column densities are in the ranges of $-3.3 < \log n_{\rm H}/{\rm cm^{-3}} < -2.4$ and $16.0 < \log N_{\rm H}/{\rm cm^{-2}}<20.3$, respectively. The line of sight thicknesses of the absorbers have a wide range of $\sim$1 pc$-$50 kpc with a median value of $\sim$500 pc. The high-ionization OVI absorption, detected in 12/18 cases, always stems from a different gas-phase. Most importantly, 85% (50%) of these absorbers show a metallicity of $\rm [Si/H] > -1.0$ (0.0). The fraction of systems showing high metallicity (i.e., $\rm [Si/H]>-1.0$) in our sample is significantly higher than the HI-selected sample (Wotta et al. 2016) and the galaxy-selected sample (Prochaska et al. 2017) of absorbers probing the circum-galactic medium (CGM) at similar redshift. A search for galaxies has revealed a significant galaxy-overdensity around these weak absorbers compared to random places with a median impact parameter of 166 kpc to the nearest galaxy. Moreover, we find the presence of multiple galaxies in $\sim80$% of the cases, suggesting group environments. The observed $d\mathcal{N}/dz$ of $0.8\pm0.2$ indicates that such metal-enriched, compact, dense structures are ubiquitous in the halos of low-$z$ galaxies that are in groups. We suggest that these are transient structures that are related to outflows and/or stripping of metal-rich gas from galaxies.Comment: Published (2018MNRAS.476.4965M) after minor revision. Appendix A is newly added

Validity of wearable technology to measure peak impact during high-intensity treadmill running

The purpose of this study was to identify the validity of an upper-body mounted accelerometer to measure peak acceleration during high-intensity treadmill running. A twelve camera motion analysis (MA) system was used as the criterion measure with markers placed on and close to the accelerometer. Ten peak impacts per participant were compared (n = 390). All accelerometer values were significantly different between the MA unit and T6 reflective marker’s acceleration data. Smoothing accelerometer data at 8 and 6 Hz provides an acceptable indirect measure of peak impact acceleration performed during high-intensity running. Therefore, smoothing algorithms should be incorporated into the commercially available software that the devices are supplied with

Automatic detection, extraction and analysis of unrestrained gait using a wearable sensor system

Within this paper we demonstrate thee ffectiveness of a novel body-worn gait monitoring and analysis framework to both accurately and automatically assess gait during ’freeliving’ conditions. Key features of the system include the ability to automatically identify individual steps within specific gait conditions, and the implementation of continuous waveform analysis within an automated system for the generation of temporally normalized data and their statistical comparison across subjects

Cross-comparison of the performance of discrete, phase and functional data analysis to describe a dependent variable

The aim of this study was to assess and contrast the ability of discrete point, functional principal component analysis (fPCA) and analysis of characterizing phases (ACP) to describe a dependent variable (jump height) from vertical ground reaction force curves captured during the propulsion phase of a countermovement jump. A stepwise multiple regression analysis was used to assess the ability of each data analysis technique. The order of effectiveness (high to low) was ACP, fPCA and discrete point analysis. Discrete point analysis was not able to generate strong predictors and detected also erroneous variables. FPCA and ACP detected similar factors to describe jump height. However, ACP performed better than fPCA because it considers the time and magnitude domain separately and in combination and it examines key-phases, without the influence of non-key-phases

Classification of continuous vertical ground reaction forces

The aim of this study is to assess and compare the performance of com- monly used hierarchical, partitional (k-means) and Gaussian model-based (Expectation-Maximization algorithm) clustering techniques to appropriately identify subgroup patterns within vertical ground reaction force data, using a continuous waveform analysis. In addition, we also compared the perfor- mance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred-and twenty two verti- cal jumps) were analyzed. The performance of each cluster technique was measured by assessing the ability to explain variances in jump height using a stepwise regression analysis. Only k-means (normalized scores; 82 %) and hierarchical clustering (normalized scores; 85 %) were able to extend the ability to describe variances in jump height beyond that achieved using the group analysis (i.e. one cluster; 78 %). Further, our findings strongly indicate the need to normalize the input data (similarity measure) when clustering. In contrast to the group analysis, the subgroup analysis was able to iden- tify cluster specific phases of variance, which improved the ability to explain variances in jump height, due to the identification of cluster specific predictor variables. Our findings therefore highlight the benefit of performing a subgroup analysis and may explain, at least in part, the contrasting findings between previous studies that used a single group level of analysis

Performance related factors in countermovement jumps: identified using a continuous subgroup analysis approach

The aim of this study was to examine the benefit of utilizing a subgroup analysis design over a single group analysis design, and determine if performance related factors differ across individuals in countermovement jumping. Joint kinematics and kinetics were used to cluster 122 individuals into four groups, based on their movement strategy. The ability to describe jump height across a single group and subgroup analysis design was assessed to measure the performance of both analysis designs, and performance related factors were identified across the generated clusters. Findings highlight a greater ability of the subgroup analysis design to describe jump height, indicating a benefit of utilizing a subgroup analysis. This is supported by the performance related factors identified, which differed across individuals

Analysis of characterizing phases on waveforms – an application to vertical jumps

The aim of this study is to propose a novel data analysis approach, ‘Analysis of Characterizing Phases’ (ACP), that detects and examines phases of variance within a sample of curves utilizing the time, magnitude and magnitude-time domain; and to compare the findings of ACP to discrete point analysis in identifying performance related factors in vertical jumps. Twenty five vertical jumps were analyzed. Discrete point analysis identified the initial-to-maximum rate of force development (p = .006) and the time from initial-to-maximum force (p = .047) as performance related factors. However, due to inter-subject variability in the shape of the force curves (i.e non-, uni- and bi-modal nature), these variables were judged to be functionally erroneous. In contrast, ACP identified the ability to: apply forces for longer (p < .038), generate higher forces (p < .027) and produce a greater rate of force development (p < .003) as performance related factors. Analysis of Characterizing Phases showed advantages over discrete point analysis in identifying performance related factors because it: (i) analyses only related phases, (ii) analyses the whole data set, (iii) can identify performance related factors that occur solely as a phase, (iv) identifies the specific phase over which differences occur, and (v) analyses the time, magnitude and combined magnitude-time domains