Inter-joint coupling and joint angle synergies of human catching movements

Bockemühl T, Troje NF, Dürr V (2010)
Hum.Movement Sci. 29(1): 73-93.

Download
Es wurde kein Volltext hochgeladen. Nur Publikationsnachweis!
Zeitschriftenaufsatz | Veröffentlicht | Englisch
Autor
; ;
Abstract / Bemerkung
A central question in motor control is how the central nervous system (CNS) deals with redundant degrees of freedom (DoFs) inherent in the musculoskeletal system. One way to simplify control of a redundant system is to combine several DoFs into synergies. In reaching movements of the human arm, redundancy occurs at the kinematic level because there is an unlimited number of arm postures for each position of the hand. Redundancy also occurs at the level of muscle forces because each arm posture can be maintained by a set of muscle activation patterns. Both postural and force-related motor synergies may contribute to simplify the control problem. The present study analyzes the kinematic complexity of natural, unrestrained human arm movements, and detects the amount of kinematic synergy in a vast variety of arm postures. We have measured inter-joint coupling of the human arm and shoulder girdle during fast, unrestrained, and untrained catching movements. Participants were asked to catch a ball launched towards them on 16 different trajectories. These had to be reached from two different initial positions. Movement of the right arm was recorded using optical motion capture and was transformed into 10 joint angle time courses, corresponding to 3 DoFs of the shoulder girdle and 7 of the arm. The resulting time series of the arm postures were analyzed by principal components analysis (PCA). We found that the first three principal components (PCs) always captured more than 97% of the variance. Furthermore, subspaces spanned by PC sets associated with different catching positions varied smoothly across the arm's workspace. When we pooled complete sets of movements, three PCs, the theoretical minimum for reaching in 3D space, were sufficient to explain 80% of the data's variance. We assumed that the linearly correlated DoFs of each significant PC represent cardinal joint angle synergies, and showed that catching movements towards a multitude of targets in the arm's workspace can be generated efficiently by linear combinations of three of such synergies. The contribution of each synergy changed during a single catching movement and often varied systematically with target location. We conclude that unrestrained, one-handed catching movements are dominated by strong kinematic couplings between the joints that reduce the kinematic complexity of the human arm and shoulder girdle to three non-redundant DoFs.
Erscheinungsjahr
Zeitschriftentitel
Hum.Movement Sci.
Band
29
Zeitschriftennummer
1
Seite
73-93
ISSN
PUB-ID

Zitieren

Bockemühl T, Troje NF, Dürr V. Inter-joint coupling and joint angle synergies of human catching movements. Hum.Movement Sci. 2010;29(1):73-93.
Bockemühl, T., Troje, N. F., & Dürr, V. (2010). Inter-joint coupling and joint angle synergies of human catching movements. Hum.Movement Sci., 29(1), 73-93. doi:10.1016/j.humov.2009.03.003
Bockemühl, T., Troje, N. F., and Dürr, V. (2010). Inter-joint coupling and joint angle synergies of human catching movements. Hum.Movement Sci. 29, 73-93.
Bockemühl, T., Troje, N.F., & Dürr, V., 2010. Inter-joint coupling and joint angle synergies of human catching movements. Hum.Movement Sci., 29(1), p 73-93.
T. Bockemühl, N.F. Troje, and V. Dürr, “Inter-joint coupling and joint angle synergies of human catching movements”, Hum.Movement Sci., vol. 29, 2010, pp. 73-93.
Bockemühl, T., Troje, N.F., Dürr, V.: Inter-joint coupling and joint angle synergies of human catching movements. Hum.Movement Sci. 29, 73-93 (2010).
Bockemühl, Till, Troje, Nikolaus F., and Dürr, Volker. “Inter-joint coupling and joint angle synergies of human catching movements”. Hum.Movement Sci. 29.1 (2010): 73-93.

13 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Can We Achieve Intuitive Prosthetic Elbow Control Based on Healthy Upper Limb Motor Strategies?
Merad M, de Montalivet É, Touillet A, Martinet N, Roby-Brami A, Jarrassé N., Front Neurorobot 12(), 2018
PMID: 29456499
Modifying upper-limb inter-joint coordination in healthy subjects by training with a robotic exoskeleton.
Proietti T, Guigon E, Roby-Brami A, Jarrassé N., J Neuroeng Rehabil 14(1), 2017
PMID: 28606179
A simple approach to guide factor retention decisions when applying principal component analysis to biomechanical data.
Fischer SL, Hampton RH, Albert WJ., Comput Methods Biomech Biomed Engin 17(3), 2014
PMID: 22519512
Upper limb joint space modeling of stroke induced synergies using isolated and voluntary arm perturbations.
Simkins M, Al-Refai AH, Rosen J., IEEE Trans Neural Syst Rehabil Eng 22(3), 2014
PMID: 23912501
Principal component modeling of isokinetic moment curves for discriminating between the injured and healthy knees of unilateral ACL deficient patients.
Almosnino S, Brandon SC, Day AG, Stevenson JM, Dvir Z, Bardana DD., J Electromyogr Kinesiol 24(1), 2014
PMID: 24280243
Motor primitives of pointing movements in a three-dimensional workspace.
Schütz C, Schack T., Exp Brain Res 227(3), 2013
PMID: 23604576
Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics.
Maurer C, Federolf P, von Tscharner V, Stirling L, Nigg BM., Gait Posture 36(1), 2012
PMID: 22304784
Constraining upper limb synergies of hemiparetic patients using a robotic exoskeleton in the perspective of neuro-rehabilitation.
Crocher V, Sahbani A, Robertson J, Roby-Brami A, Morel G., IEEE Trans Neural Syst Rehabil Eng 20(3), 2012
PMID: 22481836

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 19945187
PubMed | Europe PMC

Suchen in

Google Scholar