Document Type

Journal Article

Publication Title

Royal Society Open Science

Publisher

The Royal Society Publishing

School

Centre for Exercise and Sports Science Research / School of Medical and Health Sciences

RAS ID

32083

Funders

Edith Cowan University - Open Access Support Scheme 2020

Comments

Sahrom, S. B., Wilkie, J. C., Nosaka, K., & Blazevich, A. J. (2020). The use of yank-time signal as an alternative to identify kinematic events and define phases in human countermovement jumping. Royal Society Open Science, 7(8), Article 192093. https://doi.org/10.1098/rsos.192093

Abstract

Detailed examinations of both the movement and muscle activation patterns used by animals and humans to complete complex tasks are difficult to obtain in many environments. Therefore, the ability to infer movement and muscle activation patterns after capture of a single set of easily obtained data is highly sought after. One possible solution to this problem is to capture force-time data through the use of appropriate transducers, then interrogate the signal's derivative, the yank-time signal, which amplifies, and thus highlights, temporal force-time changes. Because the countermovement vertical jump (CMJ) is a complex movement that has been well studied in humans, it provides an excellent preliminary model to test the validity of this solution. The aim of the present study was therefore to explore the use of yank-time signal, derived from vertical ground reaction force-time data, to identify and describe important kinematic (captured using three-dimensional motion analysis) and kinetic events in the CMJ, and to relate these to possible muscle activation (electromyography) events that underpin them. It was found that the yank-time signal could be used to accurately identify several key events during the CMJ that are likely to be missed or misidentified when only force-time data are inspected, including the first instances of joint flexion and centre of mass movement. Four different jump profiles (i.e. kinematic patterns) were inferred from the yank-time data, which were linked to different patterns of muscle activation. Therefore, yank-time signal interrogation provides a viable method of estimating kinematic patterns and muscle activation strategies in complex human movements.

DOI

10.1098/rsos.192093

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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