Correlation between maximum in-shoe plantar pressures and clubhead speed in amateur golfers

Disagreements exist in the literature regarding the manner in which weight should be dynamically shared during the golf swing, both within-feet and between the back- and target-foot, to generate maximal clubhead speed. The purpose of this study was to determine whether preferential foot-loading locations underlie weight sharing by examining the correlation between clubhead speed and maximum plantar pressure (PP) distributions. Thirty-two amateur golfers with handicap indexes ranging from 2.7 to 25 performed 10 driver swings on artificial turf following a warm-up. PP distributions were recorded at 100 Hz, and clubhead speed was recorded using a ball-tracking Doppler radar system. Maximum PPs were extracted from a 2-s window approximately centred on ball contact and were regressed against clubhead speed. Significance was assessed over the entire foot surface using statistical parametric mapping (SPM), a spatially continuous technique. SPM revealed, at relatively high anatomical resolution, significant positive correlations between clubhead speed and PPs in the lateral target-foot (P < 0.05). This suggests that not only weight transfer but also weight-transfer location may be an important determinant of clubhead speed in amateur golfers.ArticleJOURNAL OF SPORTS SCIENCES. 33(2):192-197 (2015)journal articl

Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power.

One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, that a priori ROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-sample t tests to MANOVA

Statistical Parametric Mapping (SPM) for alpha-based statistical analyses of multi-muscle EMG time-series.

Multi-muscle EMG time-series are highly correlated and time dependent yet traditional statistical analysis of scalars from an EMG time-series fails to account for such dependencies. This paper promotes the use of SPM vector-field analysis for the generalised analysis of EMG time-series. We reanalysed a publicly available dataset of Young versus Adult EMG gait data to contrast scalar and SPM vector-field analysis. Independent scalar analyses of EMG data between 35% and 45% stance phase showed no statistical differences between the Young and Adult groups. SPM vector-field analysis did however identify statistical differences within this time period. As scalar analysis failed to consider the multi-muscle and time dependence of the EMG time-series it exhibited Type II error. SPM vector-field analysis on the other hand accounts for both dependencies whilst tightly controlling for Type I and Type II error making it highly applicable to EMG data analysis. Additionally SPM vector-field analysis is generalizable to linear and non-linear parametric and non-parametric statistical models, allowing its use under constraints that are common to electromyography and kinesiology

Bayesian inverse kinematics vs. least-squares inverse kinematics in estimates of planar postures and rotations in the absence of soft tissue artifact.

A variety of inverse kinematics (IK) algorithms exist for estimating postures and displacements from a set of noisy marker positions, typically aiming to minimize IK errors by distributing errors amongst all markers in a least-squares (LS) sense. This paper describes how Bayesian inference can contrastingly be used to maximize the probability that a given stochastic kinematic model would produce the observed marker positions. We developed Bayesian IK for two planar IK applications: (1) kinematic chain posture estimates using an explicit forward kinematics model, and (2) rigid body rotation estimates using implicit kinematic modeling through marker displacements. We then tested and compared Bayesian IK results to LS results in Monte Carlo simulations in which random marker error was introduced using Gaussian noise amplitudes ranging uniformly between 0.2 mm and 2.0 mm. Results showed that Bayesian IK was more accurate than LS-IK in over 92% of simulations, with the exception of one center-of-rotation coordinate planar rotation, for which Bayesian IK was more accurate in only 68% of simulations. Moreover, while LS errors increased with marker noise, Bayesian errors were comparatively unaffected by noise amplitude. Nevertheless, whereas the LS solutions required average computational durations of less than 0.5 s, average Bayesian IK durations ranged from 11.6 s for planar rotation to over 2000 s for kinematic chain postures. These results suggest that Bayesian IK can yield order-of-magnitude IK improvements for simple planar IK, but also that its computational demands may make it impractical for some applications

Smoothing can systematically bias small samples of one-dimensional biomechanical continua.

The quality with which smoothing algorithms perform is often assessed in simulation by starting with a known 1D datum, adding noise, smoothing the noisy data, then quantifying the difference between the smoothed data and known datum, often using mean-square error (MSE). While effectively summarizing overall difference, MSE fails to capture localized, one-sided errors. This paper describes how smoothing noisy 1D data using a variety of algorithms can introduce systematic bias, and quantifies this bias using the false positive rate (FPR): the probability that a smoothing algorithm will yield a dataset whose 1D mean differs significantly from its true 1D datum. A simulation study was conducted involving six 1D datum continua, and four smoothing algorithms whose parameters were systematically manipulated along with sample size and noise amplitude. Approximately ten million simulation iterations were evaluated. FPRs were calculated at α=0.05, based on the calculated smoothness of the resulting datasets. Results showed that FPRs were much higher than the expected value of α, and in many cases approached 100%. FPRs were highest with aggressive smoothing parameters, large sample sizes and small noise amplitudes, irrespective of both smoothing algorithm and the 1D datum. These results suggest that smoothing 1D biomechanical data can introduce statistical bias with relatively high probability. The implications are experiment-specific because the biomechanical meaning of 1D changes can vary vastly between datasets. Smoothing-induced bias should be a cause for general concern when small 1D changes have non-trivial biomechanical consequences

Laetoli's lost tracks: 3D generated mean shape and missing footprints.

The Laetoli site (Tanzania) contains the oldest known hominin footprints, and their interpretation remains open to debate, despite over 35 years of research. The two hominin trackways present are parallel to one another, one of which is a composite formed by at least two individuals walking in single file. Most researchers have focused on the single, clearly discernible G1 trackway while the G2/3 trackway has been largely dismissed due to its composite nature. Here we report the use of a new technique that allows us to decouple the G2 and G3 tracks for the first time. In so doing we are able to quantify the mean footprint topology of the G3 trackway and render it useable for subsequent data analyses. By restoring the effectively 'lost' G3 track, we have doubled the available data on some of the rarest traces directly associated with our Pliocene ancestors

Preserving the impossible: conservation of soft-sediment hominin footprint sites and strategies for three-dimensional digital data capture.

Human footprints provide some of the most publically emotive and tangible evidence of our ancestors. To the scientific community they provide evidence of stature, presence, behaviour and in the case of early hominins potential evidence with respect to the evolution of gait. While rare in the geological record the number of footprint sites has increased in recent years along with the analytical tools available for their study. Many of these sites are at risk from rapid erosion, including the Ileret footprints in northern Kenya which are second only in age to those at Laetoli (Tanzania). Unlithified, soft-sediment footprint sites such these pose a significant geoconservation challenge. In the first part of this paper conservation and preservation options are explored leading to the conclusion that to 'record and digitally rescue' provides the only viable approach. Key to such strategies is the increasing availability of three-dimensional data capture either via optical laser scanning and/or digital photogrammetry. Within the discipline there is a developing schism between those that favour one approach over the other and a requirement from geoconservationists and the scientific community for some form of objective appraisal of these alternatives is necessary. Consequently in the second part of this paper we evaluate these alternative approaches and the role they can play in a 'record and digitally rescue' conservation strategy. Using modern footprint data, digital models created via optical laser scanning are compared to those generated by state-of-the-art photogrammetry. Both methods give comparable although subtly different results. This data is evaluated alongside a review of field deployment issues to provide guidance to the community with respect to the factors which need to be considered in digital conservation of human/hominin footprints

Development of the infant foot as a load bearing structure : study protocol for a longitudinal evaluation (the Small Steps study)

Background An improved understanding of the structural and functional development of the paediatric foot is fundamental to a strong theoretical framework for health professionals and scientists. An infant’s transition from sitting, through crawling and cruising, to walking is when the structures and function of the foot must adapt to bearing load. The adaptation of skin and other hard and soft tissue, and foot and gait biomechanics, during this time is poorly understood. This is because data characterising the foot tissue and loading pre-walking onset does not exist. Of the existing kinematic and plantar pressure data, few studies have collected data which reflects the real-life activities of infants with modern equipment. Methods This is a longitudinal study and part of the Great Foundations Initiative, a collaborative project between the University of Brighton and the University of Salford, which is seeking to improve foot health in children. Two cohorts of 50 infants will be recruited at the two sites (University of Brighton, Eastbourne, UK and University of Salford, Salford, UK). Infants will be recruited when they first reach for their feet and attend four laboratory visits at milestones related to foot loading, with experienced independent walking being the final milestone. Data collection will include tissue characteristics (skin thickness, texture, elasticity, pH and tendon thickness and cross-sectional area), plantar pressures and kinematics captured during real world locomotion tasks. Discussion This study will provide a database characterising the development of the infant foot as it becomes a weight bearing structure. The data will allow effective comparison and quantification of changes in structure and function due to maturation and loading by measuring pre and post established walking. Additional variables which impact on the development of the foot (gender, ethnicity and body weight) will also be factored into our analysis. This will help us to advance understanding of the determinants of foot development in early childhood