Clustering vertical ground reaction force curves produced during countermovement jumps.
The aim of this study is to assess and compare the performance of commonly 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 performance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred and twenty two vertical 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 identify 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.
Insight Centre Data Analytics, Dublin, Ireland; CLARITY: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland; Applied Sports Performance Research, School of Health and Human Performance, Dublin City University, Dublin, Ireland; Sports Surgery Clinic, Santry Demense, Dublin 9, Ireland. Electronic address: chris.richter@dcu.ie.
Chris
C
Richter
Insight Centre Data Analytics, Dublin, Ireland; CLARITY: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland.
Noel E
NE
O'Connor
Insight Centre Data Analytics, Dublin, Ireland; Applied Sports Performance Research, School of Health and Human Performance, Dublin City University, Dublin, Ireland; Sports Surgery Clinic, Santry Demense, Dublin 9, Ireland.
Brendan
B
Marshall
Insight Centre Data Analytics, Dublin, Ireland; Applied Sports Performance Research, School of Health and Human Performance, Dublin City University, Dublin, Ireland.
Kieran
K
Moran
D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D016000:Cluster Analysis; D006801:Humans; D008297:Male; D016011:Normal Distribution; D059385:Plyometric Exercise; D025341:Principal Component Analysis; D011336:Probability; D012044:Regression Analysis; D015203:Reproducibility of Results; D013177:Sports; D055815:Young Adult
Analysis of characterizing phases; Clustering; Countermovement jump; Vertical ground reaction force