Analysis of Characterizing Phases on Waveforms: An Application to Vertical Jumps

Abstract

Abstract: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-tomaximum 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.

Two-way ANOVA for scalar trajectories, with experimental evidence of non-phasic interactions

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Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session

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Letter to the editor regarding “Application of principal component analysis in clinical gait research” by Federolf and colleagues

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