Peter F. Lamb

On the use of continuous relative phase: Review of current approaches and outline for a new standard

BACKGROUND In this paper we review applications of continuous relative phase and commonly reported methods for calculating the phase angle. Signals with known properties as well as empirical data were used to compare methods for calculating the phase angle. FINDINGS Our results suggest that the most valid, robust and intuitive results are obtained from the following steps: 1) centering the amplitude of the original signals around zero, 2) creating analytic signals from the original signals using the Hilbert transform, 3) calculating the phase angle using the analytic signal and 4) calculating the continuous relative phase. INTERPRETATIONS The resulting continuous relative phase values are free of frequency artifacts, a problem associated with most normalization techniques, and the interpretation remains intuitive. We propose these methods for future research using continuous relative phase in studies and analyses of human movement coordination.

Artificial neural networks for analyzing inter-limb coordination: The golf chip shot

Motor control research relies on theories, such as coordination dynamics, adapted from physical sciences to explain the emergence of coordinated movement in biological systems. Historically, many studies of coordination have involved inter-limb coordination of relatively few degrees of freedom. This study looked at the high-dimensional inter-limb coordination used to perform the golf chip shot toward six different target distances. This study also introduces a visualization of high-dimensional coordination relevant within the coordination dynamics theoretical framework. A specific type of Artificial Neural Network (ANN), the Self-Organizing Map (SOM), was used for the analysis. In this study, the trajectory of consecutive best-matching nodes on the output map was used as a collective variable and subsequently fed into a second SOM which was used to create visualization of coordination stability. The SOM trajectories showed changes in coordination between movement patterns used for short chip shots and movement patterns used for long chip shots. The attractor diagrams showed non-linear phase transitions for three out of four players. The methods used in this study may offer a solution for researchers from a coordination dynamics perspective who intend to use data obtained from discrete high-dimensional movements.

Multi-dimensional coordination in cross-country skiing analyzed using self-organizing maps.

This study sought to ascertain how multi-dimensional coordination patterns changed with five poling speeds for 12 National Standard cross-country skiers during roller skiing on a treadmill. Self-organizing maps (SOMs), a type of artificial neural network, were used to map the multi-dimensional time series data on to a two-dimensional output grid. The trajectories of the best-matching nodes of the output were then used as a collective variable to train a second SOM to produce attractor diagrams and attractor surfaces to study coordination stability. Although four skiers had uni-modal basins of attraction that evolved gradually with changing speed, the other eight had two or three basins of attraction as poling speed changed. Two skiers showed bi-modal basins of attraction at some speeds, an example of degeneracy. What was most clearly evident was that different skiers showed different coordination dynamics for this skill as poling speed changed: inter-skier variability was the rule rather than an exception. The SOM analysis showed that coordination was much more variable in response to changing speeds compared to outcome variables such as poling frequency and cycle length.

Visualizing changes in lower body coordination with different types of foot orthoses using self-organizing maps (SOM)

Human movement involves the coordination of individual segments controlled by the central nervous system and powered by the muscles. However, visualization of this high-dimensional coordination between kinematic and kinetic parameters is challenging. The purposes of this study were (a) to identify differences in lower extremity coordination between different types of foot orthoses using Kohonen self-organizing maps (SOM) and (b) to demonstrate the SOM visualization of high-dimensional coordination in gait. This study used gait data for twenty subjects while running in four different orthotic conditions (control, posted, molded, and posted-molded) from a previous study. Data for one exemplar participant was used to demonstrate the visualization technique. In this visualization, areas on an output map represent certain characteristics of the gait cycle. By comparing trials of gait in different orthotic conditions a visual analysis of high-dimensional coordination is possible. Posting orthoses were shown to reduce and molded orthoses were shown to increase ankle mobility, respectively. However, when posting and molding were combined, the effects of the molded orthoses over-rode those of the posted orthoses. In fact, trials using posted-molded orthoses enhanced the effects of molded orthoses. SOMs may contribute to a better understanding of changes in the coordination of kinematic and kinetic variables at certain phases of the gait cycle under different conditions.

The application of self-organising maps to performance analysis data in rugby union

With the advent of professionalism in rugby union greater volumes of information are collected about player and team performance. A typical OptaTM, Sports CodeTM timeline, for a single rugby match, can have more than 2000 instances and labels of information. Unless there is a prior understanding of an opponent much time can be spent identifying irrelevant trends and information which may not fairly represent the performance of the match. Kohonen Self-organising Maps (SOMs) are a form of artificial neural network developed for clustering and visualising high-dimensional data by reducing the output to a low-dimensional output map. These visualisations may help the analyst quickly identify important relationships among the key performance indicators describing a match. In this paper we report on the application of SOMs to discrete data summarising matches in New Zealand’s ITM Cup rugby competition. The input variables were frequencies of common performance indicators. The SOM approach was used to narrow down the input variables to those that discriminate between successful and unsuccessful outcomes as well as map regions associated with various levels of success. Since map regions indicate game patterns or styles, further analysis showed that multiple game styles tended to lead to wins and multiple different styles tended to lead to losses. SOMs represent an important method for characterising game play in rugby union, we suggest continued use of SOMs will help make coaches and analysts more familiar with their interpretation and anticipate further streamlining of key performance indicator selection.

Understanding the relationship among launch variables in the golf drive using neural network visualisations

The aim of this study was to identify and characterise individual differences in launch conditions measured from the same hole during four rounds of a professional golf tournament. Launch data from the 18th tee at the 2009 Dubai World Championship were used for the analysis. Self-organising maps (SOMs) were chosen to visualise the potentially non-linear relationship among the launch variables. Several distinctly different types of drives were identified on the Output Map. Drives which carried the furthest were not necessarily associated with the highest rates of ball speed. As indicated by carry distance, the longest drives had backspin rates of roughly 2,700 rpm, a launch angle of 11°, a straight or slightly left-to-right curving ball flight (for right-handers), and reached an apex of about 36 m. These values are specific to the 18th hole at the Dubai World Championship and differ from the general launch recommendations found in the literature.

Visualizing Rugby Game Styles Using Self-Organizing Maps

Rugby coaches and analysts often use notational data describing match events to assess their teams performance and to devise strategic plans for upcoming matches. However, given the volume and complexity of the data available, it is difficult for them to recognize high-dimensional relationships among the available performance variables. A nonlinear approach using self-organizing maps (SOM) can help visualize the performance of a team and its opponents as well as the subsequent suitability of certain game styles, given the style of the opponent.

Using performance data to identify styles of play in netball: An alternative to performance indicators

Abstract The advent of sports technology has led to large, high-dimensional, performance data-sets, which pose decision-making challenges for coaches and performance analysts. If large data-sets are managed poorly inaccurate and biased decision-making may actually be enabled. This paper outlines a process for capturing, organising and analysing a large performance data-set in professional netball. Two hundred and fifty ANZ Championship matches, from the 2012 to 2015 seasons, where analysed. Self-organising maps and a k-means clustering algorithm were used to describe seven game styles, which were used in a case study to devise a strategy for an upcoming opponent. The team implemented a centre-pass (CP) defence strategy based on the opponent’s previous successful and unsuccessful performances. This strategy involved allowing the oppositions Wing-attack to receive the CP while allowing their Goal attack to take the second pass. The strategy was monitored live by the coaches on a tablet computer via a custom-built dashboard, which tracks each component of the strategy. The process provides an alternative to use of conventional performance indicators and demonstrates a method for handling large high-dimensional performance data-sets. Further work is needed to identify an ecologically valid method for variable selection.

Concepts and Methods for Strategy Building and Tactical Adherence: A Case Study in Football:

This investigation looked at the on-field strategic planning of a professional German soccer club, how the strategic plans were adhered to in competition and how they were adjusted following matches. The methods presented in this article combine qualitative and quantitative techniques and are central to being able to assess objectively strategic plans and playing tactics. The coaching philosophy is a main contributor to the strategic plans, aspects of the match which were directly related to the coaching philosophy were less susceptible to change throughout the season. Additionally, adherence to strategic plans increased as the season progressed and was higher for home matches compared to away. The methods presented in this article have led to a new model of strategic planning, assessment of the quality of strategic plans put in place and the teams adherence to those strategies so that plans for upcoming matches can be optimised. The methods presented and the model for strategic planning and assessment can be used for other sports.

Performance analysis in golf using the ISOPAR method

Performance analysis in golf has been hindered by a lack of valid performance indicators available. This paper presents an application of the ISOPAR method (Stöckl et al., 2011) to performance analysis in golf for two tournaments at the Golfclub Augsburg in Germany. The method involves characterising golf holes using ball locations and the number of shots required to hole out from each respective ball location. Using these data, ISOPAR maps can be created to visualise the difficulty of the hole. Areas on the greens which provide putts with the least difficulty were shown on the ISOPAR maps. Shots played to hole locations on elevated areas of the green may be considered more influential in the outcome (score) of the hole. A new performance indicator Shot Quality is also demonstrated in this paper. The quality of individual shots was highly correlated (Rd 1, ρ = -0.749; Rd 2, ρ = -0.441; Rd 3, ρ = -0.429) with hole score yet showed almost no relationship with round score. The correlation with tournament ranking was higher than expected (ρ = 0.221) considering the lack of correlation with Shot Quality and round score. In this study, only putting data were collected but future research will extend the method to include entire holes.