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

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.

Leg Dominance Effects on Postural Control When Performing Challenging Balance Exercises

“…Then, all marker data were centered by subtracting the mean posture vector [44,45,47], thus preventing differences in mean marker positioning in space from having an influence on the PCA outcome [22]. For each subject, subj, a mean posture vector was calculated:…”

“…The PCA was computed through a singular value decomposition of the input matrix's covariance matrix [44,45,47]. The PCA yielded a set of eigenvectors, − −− → PC k , which form a new basis for the vector space of marker positions, i.e., the eigenvectors provide a basis in which changes in posture ("postural movements") can be quantified [44,45,47].…”

Should the Minimal Intervention Principle Be Considered When Investigating Dual-Tasking Effects on Postural Control?

“…The kinematic data was analyzed using a principal component analysis (PCA) to evaluate postural movements, i.e., changes in the postural configuration [29]. All of the following PCA-based calculations were computed using a software package called "PManalyzer" [39], which is provided through open access. As a first analysis step, gaps in the marker trajectories were filled [40,41], then the data from each trial was normalized by subtracting the mean posture and dividing by the trial's mean Euclidean distance [29,32], finally, the marker coordinates were weighed according to the relative body mass, which they represent [29,42].…”

Adolescent Awkwardness: Alterations in Temporal Control Characteristics of Posture with Maturation and the Relation to Movement Exploration

“…MatLab R2019b (The Mathworks Inc., Natick, MA, USA) and the "PManalyzer" [40] were used to process the kinematic data. In all trials, the first 5 s were omitted to avoid settle-in effects, nine asymmetrical markers were removed, and gaps in marker-trajectories were reconstructed using a PCA-based procedure [41,42].…”