Separating timing, movement conditions and individual differences in the analysis of human movement

TL;DR

This paper introduces a nonlinear mixed-effects model for analyzing human movement data, effectively separating timing and spatial variations, and revealing new insights into movement behavior under different task conditions.

Contribution

The paper presents a novel nonlinear mixed-effects model that overcomes identifiability issues in separating timing and path variations in human movement analysis.

Findings

Model successfully classifies movements by participant.

Uncovers low-dimensional structure of movement variations.

Reveals effects of obstacle placement on movement paths.

Abstract

A central task in the analysis of human movement behavior is to determine systematic patterns and differences across experimental conditions, participants and repetitions. This is possible because human movement is highly regular, being constrained by invariance principles. Movement timing and movement path, in particular, are linked through scaling laws. Separating variations of movement timing from the spatial variations of movements is a well-known challenge that is addressed in current approaches only through forms of preprocessing that bias analysis. Here we propose a novel nonlinear mixed-effects model for analyzing temporally continuous signals that contain systematic effects in both timing and path. Identifiability issues of path relative to timing are overcome by using maximum likelihood estimation in which the most likely separation of space and time is chosen given the variation found in data. The model is applied to analyze experimental data of human arm movements in which participants move a hand-held object to a target location while avoiding an obstacle. The model is used to classify movement data according to participant. Comparison to alternative approaches establishes nonlinear mixed-effects models as viable alternatives to conventional analysis frameworks. The model is then combined with a novel factor-analysis model that estimates the low-dimensional subspace within which movements vary when the task demands vary. Our framework enables us to visualize different dimensions of movement variation and to test hypotheses about the effect of obstacle placement and height on the movement path. We demonstrate that the approach can be used to uncover new properties of human movement.