A robust ensemble data method for identification of human joint mechanical properties during movement

This paper describes a perturbation method for the identification of linear time-varying systems with an unknown input (voluntary joint input) using ensemble data. The method separates the unknown input and the perturbation through high-pass filtering and recasts the multi-input single-output system...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 1999-04, Vol.46 (4), p.409-419
Main Authors: Yangming Xu, Hollerbach, J.M.
Format: Article
Language:English
Subjects:
Adult
Analytical models
Biological and medical sciences
Cities and towns
Computer aided analysis
Computer simulation
Elasticity
Elbow
Elbow Joint - physiology
Female
Filtering
Forearm - physiology
Fundamental and applied biological sciences. Psychology
Humans
Identification (control systems)
Joints (anatomy)
Least-Squares Analysis
Linear Models
Male
Mechanical factors
Models, Biological
Movement - physiology
Perturbation methods
Perturbation techniques
Reference Values
Robustness
Signal Processing, Computer-Assisted
System identification
Time varying control systems
Time varying systems
Vertebrates: body movement. Posture. Locomotion. Flight. Swimming. Physical exercise. Rest. Sports
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Summary:This paper describes a perturbation method for the identification of linear time-varying systems with an unknown input (voluntary joint input) using ensemble data. The method separates the unknown input and the perturbation through high-pass filtering and recasts the multi-input single-output system identification into single-input single-output system identification. The method is robust to intertrial variation, and can track changes of system dynamics up to 5 Hz. Analysis and simulation are given for the conditions similar to those for the human arm experiments. Experiments show that mechanical properties of the human elbow joint change with voluntary movement speed and that the mean stiffness with voluntary movement is in the range of the posture and is higher than reported before.
ISSN:0018-9294
1558-2531
DOI:10.1109/10.752938