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

Balance as a predictor towards independent cycling

Cycling is a milestone for children. Learning to ride a bike is an acquired skill, often obtained with difficulty. Fundamental movement skills (FMS) are generally developed in early childhood. Children have the developmental potential to progress to the mature stage of most FMS by the age of 6, at which point they are able to combine FMS to produce specialised skills in sports and recreational activities like cycling. Balance, a subset of FMS, has often thought to be essential in cycling; however, there is no empirical evidence to support this statement. Thus, this study investigates if balance is a contributing factor to learning to cycle. 72 children (3.7+/-0.5) were assessed pre and post a 5 week intervention. The children were assessed on ability to cycle independently and balance ability. Ability to cycle independently was measured using a traditional bike. If the child was able to cycle without assistance (tester holding onto bike) they were given a score of 1 and if they could not a score of 0. No children were able to cycle independently at pre-intervention. Balance ability was measured using the balance subset of the Movement Assessment Battery for Children, second edition (MABC-2). All children attended 10 cycling lessons over 5 weeks. Linear regressions were run to assess whether the balance at pre-intervention predicted if a child would be able the cycle independently post-intervention. Balance ability did not predict cycling independently (r^2=.002, p>.05). The current results would suggest that the FMS skill of balance is not a contributing factor to learning to cycle. This result, while in contrast to the general assumption, is not surprising as most children do not reach the mastery level of FMS till the age of 6. Therefore, between 3 and 5 years, when children generally learn to cycle, they are not yet at the phase of refining FMS to produce sport specific skills. Further research should investigate (i) if other FMS subsets or a combination of FMS contribute to learning to cycle and (ii) if cycling is an independent skill learnt at parallel to FMS

A virtual coaching environment for improving golf swing technique

As a proficient golf swing is a key element of success in golf, many golfers make significant effort improving their stroke mechanics. In order to help enhance golfing performance, it is important to identify the performance determining factors within the full golf swing. In addition, explicit instructions on specific features in stroke technique requiring alterations must be imparted to the player in an unambiguous and intuitive manner. However, these two objectives are difficult to achieve due to the subjective nature of traditional coaching techniques and the predominantly implicit knowledge players have of their movements. In this work, we have developed a set of visualisation and analysis tools for use in a virtual golf coaching environment. In this virtual coaching studio, the analysis tools allow for specific areas require improvement in a player's 3D stroke dynamics to be isolated. An interactive 3D virtual coaching environment then allows detailed and unambiguous coaching information to be visually imparted back to the player via the use of two virtual human avatars; the first mimics the movements performed by the player; the second takes the role of a virtual coach, performing ideal stroke movement dynamics. The potential of the coaching tool is highlighted in its use by sports science researchers in the evaluation of competing approaches for calculating the X-Factor, a significant performance determining factor for hitting distance in a golf swing

Biomechanical factors associated with jump height: a comparison of cross-sectional and pre-to-post training change findings

Previous studies investigating the biomechanical factors associated with maximal countermovement jump height have typically utilised cross-sectional data. An alternative but less common approach is to use pre-to-post training change data, where the relationship between an improvement in jump height and a change in a factor is examined more directly. Our study compared the findings of these approaches. Such an evaluation is necessary because cross-sectional studies are currently a primary source of information for coaches when examining what factors to train to enhance performance. The countermovement jump of forty four males was analysed before and after an eight week training intervention. Correlations with jump height were calculated using both cross-sectional (pre-training data only) and pre-to-post training change data. Eight factors identified in the cross-sectional analysis were not significantly correlated with a change in jump height in the pre-to-post analysis. Additionally, only six of eleven factors identified in the pre-to-post analysis were identified in the cross-sectional analysis. These findings imply that: (a) not all factors identified in a cross-sectional analysis may be critical to jump height improvement, and (b) cross-sectional analyses alone may not provide an insight into all of the potential factors to train to enhance jump height. Coaches must be aware of these limitations when examining cross-sectional studies to identify factors to train to enhance jump ability. Additional findings highlight that while exercises prescribed to improve jump height should aim to enhance concentric power production at all joints, a particular emphasis on enhancing hip joint peak power may be warranted

Analysis of the joint kinematics of the 5 iron golf swing

The purpose of this study was to identify the performance determining factors of the 5-iron golf swing. Joint kinematics were obtained from thirty male golfers using a twelve camera motion analysis system. Participants were divided into two groups, based on their ball launch speed (high vs. low). Those in the high ball speed group were deemed to be the more skillful group. Statistical analysis was used to identify the variables which differed significantly between the two groups, and could therefore be classified as the performance determining factors. The following factors were important to performance success: (i) the ability of the golfer to maintain a large X Factor angle and generate large X Factor angular velocity throughout the downswing, (ii) maintain the left arm as straight as possible throughout the swing, (iii) utilise greater movement of the hips in the direction of the target and a greater extension of the right hip during the downswing and (iv) greater flexion of both shoulders and less left shoulder internal rotation during the backswing

Epidemiology of injury in male Irish secondary school adolescents in one academic year.

BACKGROUND:Establishing the incidence of injury is the first step in initiating injury prevention strategies. There is a lack of research on injury in Irish adolescents and this study paves the way for further injury prevention research, by implementing a prospective cohort study. PURPOSE:To establish the epidemiology of injury in male adolescents in Irish secondary schools in one academic year. METHODS:452 male 4th and 5th year adolescents (aged 15.62±0.70 years) took part in a prospective epidemiology study for one academic year. Any injury sustained during training or competition resulting in restricted performance or time lost from play was assessed weekly by an athletic rehabilitation therapist. An injury report form was completed to ensure standardisation of the injury description. RESULTS:5.16 injuries per 1,000 hours were noted, with 35.6% at risk of injury and 27.9% of injured participants at risk of sustaining another injury that school year. Competition injuries (16.91 injuries per 1,000 hours) were more common than training injuries (2.63 injuries per 1,000 hours). Lower limb injuries predominated (73.1%) with knee (17.9%), ankle (13.5%) and hamstring (11.7%) injuries most common. Strains (29.4%), sprains (20.8%), fatigue-induced muscle disorder (14.5%) and contusions (13.1%) were frequent. Injuries were primarily minor (0–7 days) (41.6%), followed by severe (>22 days) (39.7%) and moderate (8–21 days) (18.7%). CONCLUSION:Injuries are common in adolescents in Irish secondary schools and the development and implementation of injury prevention strategies are required

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

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