Measurement of Dynamic Joint Stiffness from Multiple Short Data Segments

被引:11
作者
Jalaleddini, Kian [1 ]
Golkar, Mahsa A. [2 ]
Kearney, Robert E. [2 ]
机构
[1] Univ Southern Calif, Div Biokinesiol & Phys Therapy, Los Angeles, CA 90033 USA
[2] McGill Univ, Dept Biomed Engn, Montreal, PQ H3A 2B4, Canada
基金
加拿大健康研究院; 加拿大自然科学与工程研究理事会;
关键词
Biological system modeling; biomechanics; biomedical signal processing; nonlinear dynamical systems; state-space methods; system identification; HUMAN ANKLE STIFFNESS; SYSTEM-IDENTIFICATION; REFLEX CONTRIBUTIONS; MECHANICAL IMPEDANCE; HAMMERSTEIN SYSTEMS; HEMIPARETIC STROKE; CEREBRAL-PALSY; MUSCLES; SPASTICITY; MODELS;
D O I
10.1109/TNSRE.2017.2659749
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
This paper presents our new method, Short Segment-Structural Decomposition SubSpace (SS-SDSS), for the estimation of dynamic joint stiffness from short data segments. The main application is for data sets that are only piecewise stationary. Our approach is to: 1) derive a data-driven, mathematical model for dynamic stiffness for short data segments; 2) bin the non-stationary data into a number of short, stationary data segments; and 3) estimate the model parameters from subsets of segments with the same properties. This method extends our previous state-spacework by recognizing that initial conditions have important effects for short data segments; consequently, initial conditions are incorporated into the stiffness model and estimated for each segment. A simulation study that faithfully replicated experimental conditions delineated the range of experimental conditions for which the method can successfully identify stiffness. An experimental study on the ankle of a healthy subject during a torque matching tasks demonstrated the successful estimation of dynamic stiffness in a slow, time-varying experiment. Together, the simulation and experimental studies demonstrate that the SS-SDSS method is a valuable tool to measure stiffness in functionally important tasks.
引用
收藏
页码:925 / 934
页数:10
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