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

PeerJ

article

Todd C. Pataky¹  Mark A. Robinson²  Jos Vanrenterghem³ 

[1] Institute for Fiber Engineering, Department of Bioengineering, Shinshu University;Research Institute for Sport and Exercise Sciences, Liverpool John Moores University;Department of Rehabilitation Sciences, Katholieke Universiteit Leuven

关键词: Time series analysis;    Kinematics;    Constrained hypotheses;    Statistical parametric mapping;    Dynamics;    Random field theory;    Hypothesis testing;    Biomechanics;    Human movement;   

DOI  :  10.7717/peerj.2652

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY