Design of a Tissue Resonator Indenter Device for Measurement of Soft Tissue Viscoelastic Properties Using Parametric Identification

2009 ◽  
Author(s):  
Alireza Hariri ◽  
Jean W. Zu
Keyword(s):  
Soft Tissue ◽  
Viscoelastic Properties ◽  
Degrees Of Freedom ◽  
Mechanical Vibration ◽  
State Space Model ◽  
Parametric Identification ◽  
Model Parameters ◽  
Kelvin Model ◽  
Tissue Properties ◽  
New Device

The design of a new device called Tissue Resonator Indenter Device (TRID) for measuring soft tissue viscoelastic properties is presented. The two degrees-of-freedom device works based on mechanical vibration principles. When TRID comes into contact with a soft tissue, it can identify the tissue’s viscoelastic properties through the change of the device’s natural frequencies and damping ratios. In this paper, the deign of TRID is presented assuming Kelvin model for tissues. By working in the linear viscoelastic domain, TRID is designed to identify tissue properties in the range of 0–100 Hz. Assuming Kelvin model for tissues, the current paper develops a method for determining unknown tissue parameters using input-output data from TRID. Moreover, it is proved that the TRID’s parameters as well as the Kelvin tissue model parameters are globally identifiable. A parametric identification method using the prediction error approach is proposed for identifying the unknown tissue parameters in a grey-box state-space model. The reliability and effectiveness of the method for measuring soft tissue’s viscoelastic properties is demonstrated through simulation in the presence of considerable input and output noises.

Prediction of TKR Function Using Specimen Specific Robotically Calibrated Knee Models

2012 ◽  
Author(s):  
Eik Siggelkow ◽  
Iris Sauerberg ◽  
Francesco Benazzo ◽  
Marc Bandi
Keyword(s):  
Soft Tissue ◽  
Degrees Of Freedom ◽  
Knee Kinematics ◽  
Computer Models ◽  
Industrial Robots ◽  
Experimental Investigations ◽  
Tissue Properties ◽  
Tissue Structures ◽  
Kinematics And Kinetics

Passive knee kinematics and kinetics following total knee replacement (TKR) are dependent on the topology of the component joint surfaces as well as the properties of the passive soft tissue structures (ligaments and capsule). Recently, explicit computer models have been used for the prediction of knee joint kinematics based on experimental investigations [1]. However, most of these models replicate experimental knee simulators [2], which simulate soft tissue structures using springs or elastomeric structures. New generations of experimental setups deploy industrial robots for measuring kinematics and kinetics in six degrees of freedom as well as the contribution of soft tissue structures. Based on these experiments, accurate soft tissue properties are available for use in computer models to aid more realistic predictions of kinematics. Final evidence of the quality of the kinematic predictions from these computer models can be provided by direct validation of the models against experimental data. Therefore, the objective of this study was to use in vitro robotic test data to develop, verify, and validate specimen specific virtual models suitable for predicting laxity and kinematics of the reconstructed knee.

scholarly journals Developing a Quantifying Device for Soft Tissue Material Prop-Erties around Lumbar Spines

Biosensors ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 67
Author(s):  
Song Joo Lee ◽  
Yong-Eun Cho ◽  
Kyung-Hyun Kim ◽  
Deukhee Lee
Keyword(s):  
Lumbar Spine ◽  
Soft Tissue ◽  
Young’S Modulus ◽  
Viscoelastic Properties ◽  
Soft Tissues ◽  
Young's Modulus ◽  
Strain Curve ◽  
Stress And Strain ◽  
Commercial Device ◽  
Tissue Material

Knowing the material properties of the musculoskeletal soft tissue could be important to develop rehabilitation therapy and surgical procedures. However, there is a lack of devices and information on the viscoelastic properties of soft tissues around the lumbar spine. The goal of this study was to develop a portable quantifying device for providing strain and stress curves of muscles and ligaments around the lumbar spine at various stretching speeds. Each sample was conditioned and applied for 20 repeatable cyclic 5 mm stretch-and-relax trials in the direction and perpendicular direction of the fiber at 2, 3 and 5 mm/s. Our device successfully provided the stress and strain curve of the samples and our results showed that there were significant effects of speed on the young’s modulus of the samples (p < 0.05). Compared to the expensive commercial device, our lower-cost device provided comparable stress and strain curves of the sample. Based on our device and findings, various sizes of samples can be measured and viscoelastic properties of the soft tissues can be obtained. Our portable device and approach can help to investigate young’s modulus of musculoskeletal soft tissues conveniently, and can be a basis for developing a material testing device in a surgical room or various lab environments.

scholarly journals Software Sensor to Enhance Online Parametric Identification for Nonlinear Closed-Loop Systems for Robotic Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3653
Author(s):  
Lilia Sidhom ◽  
Ines Chihi ◽  
Ernest Nlandu Kamavuako
Keyword(s):  
Degrees Of Freedom ◽  
Closed Loop ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Parametric Identification ◽  
Recursive Least Squares ◽  
Unknown Parameters ◽  
Closed Loop Identification ◽  
Input Variables ◽  
Robotic Applications

This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differentiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algorithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

Motion Model of Two-Wheel Differential Drive Mobile Robot under Variable Load

2014 ◽  
Vol 668-669 ◽  
pp. 352-356 ◽  
Author(s):  
Zhi Hu Ruan ◽  
Niu Wang ◽  
Bing Xin Ran
Keyword(s):  
Mobile Robot ◽  
State Space Model ◽  
Response Characteristic ◽  
Model Parameters ◽  
Variable Load ◽  
Motion Model ◽  
Actual System ◽  
Wheel Load ◽  
Differential Drive ◽  
Entire Vehicle

Based on kinematics characteristic of two-wheeled differential drive mobile robot (WMR) and response characteristic of fact motor drive system, this paper presents the analysis method of the equivalent rotation inertia, and the entire vehicle load is assigned to each wheel, and then the wheel load is converted into the corresponding equivalent rotation inertia of the motor shaft of each wheel, and motion model of WMR are obtained for combining with quasi-equivalent (QE) state space model of double-loop direct current motor systems under variable load and kinematics model of WMR under the load changes. By using speed response data of the actual system and combining with genetic algorithm to accurately identify the model parameters. Finally, through experiments results of the WMR motion model and the second order model respectively comparing with the actual system which demonstrates the effectiveness of the proposing method and model.

scholarly journals Robust and Optimal Output-Feedback Control for Interval State-Space Model: Application to a Two-Degrees-of-Freedom Piezoelectric Tube Actuator

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Mounir Hammouche ◽  
Philippe Lutz ◽  
Micky Rakotondrabe
Keyword(s):  
State Space ◽  
Output Feedback ◽  
Degrees Of Freedom ◽  
Closed Loop ◽  
State Space Model ◽  
Output Feedback Control ◽  
Optimal Solution ◽  
Loop System ◽  
Closed Loop System ◽  
Optimal Output

The problem of robust and optimal output feedback design for interval state-space systems is addressed in this paper. Indeed, an algorithm based on set inversion via interval analysis (SIVIA) combined with interval eigenvalues computation and eigenvalues clustering techniques is proposed to seek for a set of robust gains. This recursive SIVIA-based algorithm allows to approximate with subpaving the set solutions [K] that satisfy the inclusion of the eigenvalues of the closed-loop system in a desired region in the complex plane. Moreover, the LQ tracker design is employed to find from the set solutions [K] the optimal solution that minimizes the inputs/outputs energy and ensures the best behaviors of the closed-loop system. Finally, the effectiveness of the algorithm is illustrated by a real experimentation on a piezoelectric tube actuator.

scholarly journals Bayesian Analysis of Interleaved Learning and Response Bias in Behavioral Experiments

2007 ◽  
Vol 97 (3) ◽  
pp. 2516-2524 ◽  
Author(s):  
Anne C. Smith ◽  
Sylvia Wirth ◽  
Wendy A. Suzuki ◽  
Emery N. Brown
Keyword(s):  
Bayesian Analysis ◽  
State Space ◽  
State Space Model ◽  
Estimation Procedure ◽  
Ideal Observer ◽  
Model Parameters ◽  
Behavioral Experiments ◽  
Actual Object ◽  
Space Model ◽  
Multiple Tasks

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood–based approaches in the analysis of a simulated conditioned T-maze task and of an actual object–place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

scholarly journals Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Gergely Takács ◽  
Tomáš Polóni ◽  
Boris Rohal’-Ilkiv
Keyword(s):  
Control System ◽  
Vibration Control ◽  
State Space ◽  
Real Time ◽  
Cantilever Beam ◽  
State Space Model ◽  
Model Parameters ◽  
Extended Kalman Filtering ◽  
Space Model ◽  
Continuous State

This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

scholarly journals Effect of time complexities and variation of mass spring model parameters on surgical simulation

2014 ◽  
Vol 9 (4) ◽  
Author(s):  
Salina Sulaiman ◽  
Tan Sing Yee ◽  
Abdullah Bade
Keyword(s):  
Soft Tissue ◽  
Human Liver ◽  
Soft Tissues ◽  
Surgical Simulation ◽  
Model Parameters ◽  
Spring Model ◽  
Surgical Simulator ◽  
Tissue Model ◽  
Mass Spring Model ◽  
Mass Spring

Physically based models assimilate organ-specific material properties, thus they are suitable in developing a surgical simulation. This study uses mass spring model (MSM) to represent the human liver because MSM is a discrete model that is potentially more realistic than the finite element model (FEM). For a high-end computer aided medical technology such as the surgical simulator, the most important issues are to fulfil the basic requirement of a surgical simulator. Novice and experienced surgeons use surgical simulator for surgery training and planning. Therefore, surgical simulation must provide a realistic and fast responding virtual environment. This study focuses on fulfilling the time complexity and realistic of the surgical simulator. In order to have a fast responding simulation, the choice of numerical integration method is crucial. This study shows that MATLAB ode45 is the fastest method compared to 2nd ordered Euler, MATLAB ode113, MATLAB ode23s and MATLAB ode23t. However, the major issue is human liver consists of soft tissues. In modelling a soft tissue model, we need to understand the mechanical response of soft tissues to surgical manipulation. Any interaction between haptic device and the liver model may causes large deformation and topology change in the soft tissue model. Thus, this study investigates and presents the effect of varying mass, damping, stiffness coefficient on the nonlinear liver mass spring model. MATLAB performs and shows simulation results for each of the experiment. Additionally, the observed optimal dataset of liver behaviour is applied in SOFA (Simulation Open Framework Architecture) to visualize the major effect.

scholarly journals Applying biophysical models to understand the role of white matter in cognitive development

2018 ◽  
Author(s):  
Elizabeth Huber ◽  
Rafael Neto Henriques ◽  
Julia P. Owen ◽  
Ariel Rokem ◽  
Jason D. Yeatman
Keyword(s):  
White Matter ◽  
Cognitive Development ◽  
Diffusion Mri ◽  
Reading Skill ◽  
Great Promise ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Tissue Properties ◽  
Biophysical Models ◽  
Specific Tissue

AbstractDiffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular biophysical models to characterize axonal properties. We first assess the scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences in white matter that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon density in poor readers. Both models support this interpretation, highlighting the utility of biophysical models for testing specific hypotheses about cognitive development.

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