Construct Validity of the Upper-Limb Interlimb Coordination Test in Stroke

Molad, Roni; Levin, Mindy F.

doi:10.1177/15459683211058092

Cited by 2 publications

(1 citation statement)

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“…Definition Cross Correlation is a signal processing method used to measure similarity of two series as a function of the displacement of one relative to the other (sliding dot product). When cross-correlation is used to measure coordination, usually only a zero time lag is considered [ 73 ], however recent studies have begun to explore the result of cross-correlation with different lags [ 74 ]. Cross-correlation is a metric that can be used on different types of temporal signals, for example [ 75 ] have computed cross-correlation based on CRP signals.…”

Section: Metrics Literature Reviewmentioning

confidence: 99%

A guide to inter-joint coordination characterization for discrete movements: a comparative study

Dubois,

Roby-Brami,

Parry

et al. 2023

J NeuroEngineering Rehabil

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Characterizing human movement is essential for understanding movement disorders, evaluating progress in rehabilitation, or even analyzing how a person adapts to the use of assistive devices. Thanks to the improvement of motion capture technology, recording human movement has become increasingly accessible and easier to conduct. Over the last few years, multiple methods have been proposed for characterizing inter-joint coordination. Despite this, there is no real consensus regarding how these different inter-joint coordination metrics should be applied when analyzing the coordination of discrete movement from kinematic data. In this work, we consider 12 coordination metrics identified from the literature and apply them to a simulated dataset based on reaching movements using two degrees of freedom. Each metric is evaluated according to eight criteria based on current understanding of human motor control physiology, i.e, each metric is graded on how well it fulfills each of these criteria. This comparative analysis highlights that no single inter-joint coordination metric can be considered as ideal. Depending on the movement characteristics that one seeks to understand, one or several metrics among those reviewed here may be pertinent in data analysis. We propose four main factors when choosing a metric (or a group of metrics): the importance of temporal vs. spatial coordination, the need for result explainability, the size of the dataset, and the computational resources. As a result, this study shows that extracting the relevant characteristics of inter-joint coordination is a scientific challenge and requires a methodical choice. As this preliminary study is conducted on a limited dataset, a more comprehensive analysis, introducing more variability, could be complementary to these results.

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Section: Metrics Literature Reviewmentioning

confidence: 99%