PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements.

Thomas H Haid,Aude-Clémence M Doix,Arunee Promsri,Matteo Zago,Peter A Federolf

doi:10.3389/fninf.2019.00024

Thomas H Haid, Aude-Clémence M Doix + Show 3 more

Open Access

https://doi.org/10.3389/fninf.2019.00024

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Abstract

Motion analysis is used to study the functionality or dysfunctionality of the neuromuscular system, as human movements are the direct outcome of neuromuscular control. However, motion analysis often relies on measures that quantify simplified aspects of a motion, such as specific joint angles, despite the well-known complexity of segment interactions. In contrast, analyzing whole-body movement patterns may offer a new understanding of movement coordination and movement performance. Clinical research and sports technique evaluations suggest that principal component analysis (PCA) provides novel and valuable insights into control aspects of the neuromuscular system and how they relate to coordinative patterns. However, the implementation of PCA computations are time consuming, and require mathematical knowledge and programming skills, drastically limiting its application in current research. Therefore, the aim of this study is to present the Matlab software tool “PManalyzer” to facilitate and encourage the application of state-of-the-art PCA concepts in human movement science. The generalized PCA concepts implemented in the PManalyzer allow users to apply a variety of marker set independent PCA-variables on any kinematic data and to visualize the results with customizable plots. In addition, the extracted movement patterns can be explored with video options that may help testing hypotheses related to the interplay of segments. Furthermore, the software can be easily modified and adapted to any specific application.

Highlights

Sensorimotor control of movements is one of the most important functions of the nervous system
The PManalyzer can compute a range of principal component analysis (PCA) variables
Typical kinematic data consists of 3D positions in time obtained by tracking the motion of n anatomical landmarks; either utilizing a motion capture system or video-tracking (Figueroa et al, 2003)

Summary

Introduction

Sensorimotor control of movements is one of the most important functions of the nervous system It involves detecting the physical state which the biomechanical system is in; processing this information to determine which changes to the system are desired or need to be opposed; and activating the motor system to generate the forces that produce the required changes to the system. Conventional movement analyses often look into specific, pre-determined aspects of a motion Such analyses often neglect important information about segment interactions; and the complex nature of these interactions makes a priori variable determination prone to false identification of important aspects. That is why other approaches quantify whole body kinematics (Honegger et al, 2013; Boström et al, 2018) Most of these approaches still rely on predefined aspects of specific movements such as angles, torques, or segment trajectories

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