Identification of time-varying biological systems from ensemble data (joint dynamics application)

The theory underlying a new method for the identification of time-varying systems is described. The method uses singular value decomposition to obtain least-squares estimates of time-varying impulse response functions from an ensemble of input-output realizations. No a priori assumptions regarding t...

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Bibliographic Details
Published inIEEE transactions on biomedical engineering Vol. 39; no. 12; pp. 1213 - 1225
Main Authors MacNeil, J.B., Kearney, R.E., Hunter, I.W.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.12.1992
Institute of Electrical and Electronics Engineers
Subjects
Adult
Algorithms
Autoregressive processes
Biological and medical sciences
Biological systems
Biomedical engineering
Councils
Frequency
Fundamental and applied biological sciences. Psychology
Humans
Joints - physiology
Mathematics
Miscellaneous
Models, Biological
Muscles - physiology
Nervous System Physiological Phenomena
Noise robustness
Singular value decomposition
System identification
Systems engineering and theory
Systems Theory
Time Factors
Time varying systems
Vertebrates: osteoarticular system, musculoskeletal system
Human
Ankle joint
Simulation
Theoretical study
Biophysics
Dynamical system
Experimental study
Mathematical model
Algorithm
Time varying circuit
Biological system
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Summary:The theory underlying a new method for the identification of time-varying systems is described. The method uses singular value decomposition to obtain least-squares estimates of time-varying impulse response functions from an ensemble of input-output realizations. No a priori assumptions regarding the system structure or form of the time-variation are required and there are few restrictions on the input signal. Simulation studies, using a model of time-varying joint dynamics, show that the method can track rapid changes in system dynamics accurately and is robust in the presence of output noise. An application of the method is demonstrated by using it to track dynamic ankle stiffness during a rapid, voluntary, isometric contraction. During the transient phase of the contraction, low-frequency ankle stiffness gain decreased in a manner which could not be described with the second-order model of joint dynamics often used under stationary conditions.< >
Bibliography:ObjectType-Article-1
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ISSN:0018-9294
1558-2531
DOI:10.1109/10.184697