» Articles » PMID: 14973321

Muscular and Postural Synergies of the Human Hand

Overview
Journal J Neurophysiol
Specialties Neurology
Physiology
Date 2004 Feb 20
PMID 14973321
Citations 98
Authors
Affiliations
Soon will be listed here.
Abstract

Because humans have limited ability to independently control the many joints of the hand, a wide variety of hand shapes can be characterized as a weighted combination of just two or three main patterns of covariation in joint rotations, or "postural synergies." The present study sought to align muscle synergies with these main postural synergies and to describe the form of membership of motor units in these postural/muscle synergies. Seventeen joint angles and the electromyographic (EMG) activities of several hand muscles (both intrinsic and extrinsic muscles) were recorded while human subjects held the hand statically in 52 specific shapes (i.e., shaping the hand around 26 commonly grasped objects or forming the 26 letter shapes of a manual alphabet). Principal-components analysis revealed several patterns of muscle synergy, some of which represented either coactivation of all hand muscles, or reciprocal patterns of activity (above and below average levels) in the intrinsic index finger and thumb muscles or (to a lesser extent) in the extrinsic four-tendoned extensor and flexor muscles. Single- and multiunit activity was generally a multimodal function of whole hand shape. This implies that motor-unit activation does not align with a single synergy; instead, motor units participate in multiple muscle synergies. Thus it appears that the organization of the global pattern of hand muscle activation is highly distributed. This organization mirrors the highly fractured somatotopy of cortical hand representations and may provide an ideal substrate for motor learning and recovery from injury.

Citing Articles

Variations in Clustering of Multielectrode Local Field Potentials in the Motor Cortex of Macaque Monkeys during a Reach-and-Grasp Task.

Chambellant F, Falaki A, Moreau-Debord I, French R, Serrano E, Quessy S eNeuro. 2024; 11(9).

PMID: 39288997 PMC: 11439563. DOI: 10.1523/ENEURO.0047-24.2024.


Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement.

Ting L, Gick B, Kesar T, Xu J Philos Trans R Soc Lond B Biol Sci. 2024; 379(1911):20230485.

PMID: 39155720 PMC: 11529631. DOI: 10.1098/rstb.2023.0485.


Biomimetic learning of hand gestures in a humanoid robot.

Olikkal P, Pei D, Karri B, Satyanarayana A, Kakoty N, Vinjamuri R Front Hum Neurosci. 2024; 18:1391531.

PMID: 39099602 PMC: 11295247. DOI: 10.3389/fnhum.2024.1391531.


Stretchable glove for accurate and robust hand pose reconstruction based on comprehensive motion data.

Park M, Park T, Park S, Yoon S, Koo S, Park Y Nat Commun. 2024; 15(1):5821.

PMID: 38987530 PMC: 11237015. DOI: 10.1038/s41467-024-50101-w.


Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance.

Hellara H, Barioul R, Sahnoun S, Fakhfakh A, Kanoun O Sensors (Basel). 2024; 24(11).

PMID: 38894429 PMC: 11175337. DOI: 10.3390/s24113638.