Inferring motor plan complexity using a modified principal component analysis

Author

Atsma, W.J. ; Hodgson, A.J.

Author_Institution

Dept. of Mech. Eng., British Columbia Univ., Vancouver, BC, Canada

Volume

1

fYear

1999

fDate

1999

Abstract

While compliant behavior of the human arm is clearly an important contributor to stable interactions with the environment, the extent to which these properties of the arm are represented or ignored by the central nervous system in formulating motor plans remains unclear. Since model-based analyses (such as the various equilibrium-point models require a priori (and difficult to verify) assumptions about the internal structure of the neuromuscular system in order to test their predictions, we propose a model-independent approach to measuring the complexity of motor plans

Keywords

biocontrol; identification; inference mechanisms; muscle; neurophysiology; physiological models; principal component analysis; compliant behavior; feedback; generic adaptive nonlinear functions; human arm; identification; inference network; inverse controller; kinematic controller; linear dynamic model; model-independent approach; modified principal component analysis; motor commands; motor control algorithms; motor plan complexity inference; multi-port system; neuromuscular system; output trajectories; Central nervous system; Control systems; Humans; Inference algorithms; Motor drives; Neuromuscular; Performance evaluation; Predictive models; Principal component analysis; Surface reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint

Conference_Location

Atlanta, GA

ISSN

1094-687X

Print_ISBN

0-7803-5674-8

Type

conf

DOI

10.1109/IEMBS.1999.802611

Filename

802611