AI Techniques to Estimate Muscle Force and Joint Torque for Upper Extremity

2011 ◽  
Vol 467-469 ◽  
pp. 788-793 ◽  
Author(s):  
S. Parasuraman ◽  
Arif Wicaksono Oyong ◽  
Veronica Lestari Jauw
Keyword(s):  
Experimental Data ◽  
Nonlinear Regression ◽  
Stroke Rehabilitation ◽  
Muscle Force ◽  
Joint Torque ◽  
Robot Assisted ◽  
Unknown Parameters ◽  
Regression Methods ◽  
Voluntary Contractions ◽  
Best Fit

This paper is motivated by works done in the area of robot-assisted stroke rehabilitation. To map the EMG to joint torque, evolutionary techniques with suitable mathematical models are proposed. These models have unknown adjustable parameters, and the values of these parameters are obtained using nonlinear regression methods such as GA and SA. Five subjects took part in the experiments and they were asked to perform non-fatiguing and variable force maximal voluntary contractions and sub-maximal voluntary contractions. The recorded EMG signals data of various joints are entered to the model, to estimate the best fit values for the unknown parameters. Once these values of the parameters are obtained they are applied into the model and thus the joint torque is estimated. Predictions made by the proposed techniques are well correlated with experimental data.

ROBOT-ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING GA PROCESS

2011 ◽  
Vol 11 (04) ◽  
pp. 827-843 ◽  
Author(s):  
S. PARASURAMAN ◽  
ARIF WICAKSONO OYONG
Keyword(s):  
Genetic Algorithm ◽  
Basic Principle ◽  
Stroke Rehabilitation ◽  
Real Life ◽  
Joint Torque ◽  
Robot Assisted ◽  
Emg Signal ◽  
Conversion Algorithm

This project focuses on the development of robot-assisted stroke rehabilitation by implementing electromyography (EMG) as the interface between robot and user communication. The key issue in the implementation of EMG in this application is the conversion of EMG signal into torque data. This article presents a methodology of EMG signal to estimated joint torque conversion by using genetic algorithm (GA). The basic principle of GA, formulation, and implementation to the problem are discussed in this article. Experimentation with real-life EMG data has been carried out to assess the feasibility of the methodology in robot-assisted stroke rehabilitation problem. Preliminary investigations show that the methodology can be used in EMG to joint torque conversion algorithm.

Bifurcation of Transversal Relaxation Rate in Emulsions

1997 ◽  
Vol 51 (9) ◽  
pp. 1281-1286 ◽  
Author(s):  
I. Scheurmann ◽  
M. Martín-Landrove ◽  
R. Martín ◽  
J. Espidel
Keyword(s):  
Experimental Data ◽  
Laplace Transform ◽  
Relaxation Rate ◽  
Nonlinear Regression ◽  
Inverse Laplace Transform ◽  
New Approach ◽  
Regression Methods ◽  
Exchange Processes ◽  
Branched Structure ◽  
Novel Algorithm

We have performed Carr—Purcell—Meiboom—Gill (CPMG) experiments on xylene/water emulsions to understand the different molecular exchange processes present in the system. The experimental data were analyzed with a combination of nonlinear regression methods and a novel algorithm of inverse Laplace transform. The experimental results showed not only a great dispersion in the transversal relaxation rate but also a very rich branched structure of its bifurcation map. This result introduces a completely new approach and methodology to understand molecular exchange processes.

ROBOT ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING SA PROCESS

2011 ◽  
Vol 11 (03) ◽  
pp. 691-704 ◽  
Author(s):  
S. PARASURAMAN ◽  
ARIF WICAKSONO OYONG ◽  
VERONICA LESTARI JAUW
Keyword(s):  
Simulated Annealing ◽  
Control System ◽  
Stroke Rehabilitation ◽  
Experimental Studies ◽  
Real Life ◽  
Joint Torque ◽  
Experimental Investigations ◽  
Robot Assisted ◽  
Signal Conversion ◽  
Emg Signal

This paper focuses on the implementation of robot-assisted stroke rehabilitation using electromyography (EMG) as the interface between the robot and subjects. The key issue in implementing EMG for this application is the conversion process of EMG signal into torque/force, which is used as a input to the control system. This paper presents a methodology of EMG signal conversion into estimated joint torque by using simulated annealing (SA) technique. Basic principle of SA, formulation, and implementation to the problem are discussed in this paper. Experimental studies with real life EMG data have been carried out for five subjects. These studies are used to evaluate the feasibility of the methodology proposed for robot-assisted stroke rehabilitation problem. Experimental investigations and results are discussed at the end of the paper.

scholarly journals The impact of calculation on the value of thermodynamic parameters of copper adsorption on poplar sawdust

2011 ◽  
pp. 167-174
Author(s):  
Mirjana Brdar ◽  
Marina Sciban ◽  
Aleksandar Takaci
Keyword(s):  
Experimental Data ◽  
Thermodynamic Parameters ◽  
Nonlinear Regression ◽  
Copper Ions ◽  
Least Squares Regression ◽  
Copper Adsorption ◽  
Regression Methods ◽  
Endothermic Process ◽  
The Impact ◽  
Poplar Sawdust

The adsorption of Cu(II) onto poplar sawdust as an adsorbent is analyzed. The experimental data were fitted by the Langmuir isotherm using four linearized forms at the isotherm along with the original one. The least squares regression method was applied. Using the obtained Langmuir constants by each at methods, the enthalpy, entropy and Gibbs free energy at adsoption were calculated. A comparison of the used linear and non-linear regression methods in view at the goodness of the fit is presented. The coefficient of correlation was adopted as a criterionn to select the best method. The impact of the choice at regression model on the resulting estimates of the thermodynamic parameters is discussed. The best fit of the experimental data is obtained by the nonlinear regression. Thus, it is recommended to use the Langmuir parameters calculated by the nonlinear regression for estimating the thermodynamic parameters of adsorptin. The differences in the values obtained by different models are not so large to change the basic conclusion that the adsorption of copper ions on poplar sawdust is a spontaneous endothermic process i.e. that tested adsorbent has an affinity for copper ions.

Comparative Analysis of the Adsorption of Methylene Blue Using Magnetised and Non-Magnetised Coconut Shell Biochar

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
A Musa
Keyword(s):  
Methylene Blue ◽  
Nonlinear Regression ◽  
Error Function ◽  
Coconut Shell ◽  
Isotherm Models ◽  
P Value ◽  
Regression Methods ◽  
Best Fit ◽  
Coconut Shell Biochar ◽  
Mb Adsorption

It is envisaged that magnetisation might alter the sorption behaviours of magnetised biochars due to some variation in the physicochemical properties from their precursor. This study evaluated the adsorption behaviours of a coconut shell biochar produced at 600 °C, CSB600, and its magnetised pair, MCSB600, in the adsorption of methylene blue (MB) from aqueous solutions. Langmuir, Freundlich and Redlich-Peterson isotherm models were used to describe the experimental isotherm using linear and nonlinear regression methods to determine the best fit for MB adsorption from the batch experiments conducted. The Langmuir model proved to be the best fit to explain the experimental data as it had the highest R2 (0.9684 and 0.9855) from linear regression and the lowest hybrid fractional error function, HYBRID (4.58, 1.145) and marquardt’s percent standard deviation, MPSD (10.61, 5.04) error function values from the nonlinear regression methods with maximum monolayer adsorption capacities of 5.590 and 5.229 mg/g for CSB600 and MCSB600 respectively. The magnetised biochar exhibited similar adsorption characteristics to what was observed for the non-magnetised biochar and only about 6.46% lower MB adsorption capacity was recorded. A p-value of 0.088 obtained suggested the isotherms were similar and therefore, magnetisation did not affect the adsorption of MB.

Feedback Reactor and Nonlinear Regression Methods

1992 ◽  
Vol 57 (10) ◽  
pp. 2053-2058
Author(s):  
Václav Dušek ◽  
František Skopal
Keyword(s):  
Nonlinear Regression ◽  
Kinetic Measurements ◽  
Regression Methods ◽  
Linear And Nonlinear

Linear and nonlinear regression methods are compared with respect to their application to the evaluation of chemico-kinetic measurements of a feedback reactor. Their assets and pitfalls are demonstrated.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

Three-dimensional asymmetric maximum weight lifting prediction considering dynamic joint strength

2021 ◽  
pp. 095441192098703
Author(s):  
Rahid Zaman ◽  
Yujiang Xiang ◽  
Jazmin Cruz ◽  
James Yang
Keyword(s):  
Experimental Data ◽  
Degrees Of Freedom ◽  
Inverse Dynamics ◽  
Three Dimensional ◽  
Optimization Method ◽  
Weight Lifting ◽  
Joint Torque ◽  
Maximum Weight ◽  
Angular Velocities ◽  
Asymmetric Lifting

In this study, the three-dimensional (3D) asymmetric maximum weight lifting is predicted using an inverse-dynamics-based optimization method considering dynamic joint torque limits. The dynamic joint torque limits are functions of joint angles and angular velocities, and imposed on the hip, knee, ankle, wrist, elbow, shoulder, and lumbar spine joints. The 3D model has 40 degrees of freedom (DOFs) including 34 physical revolute joints and 6 global joints. A multi-objective optimization (MOO) problem is solved by simultaneously maximizing box weight and minimizing the sum of joint torque squares. A total of 12 male subjects were recruited to conduct maximum weight box lifting using squat-lifting strategy. Finally, the predicted lifting motion, ground reaction forces, and maximum lifting weight are validated with the experimental data. The prediction results agree well with the experimental data and the model’s predictive capability is demonstrated. This is the first study that uses MOO to predict maximum lifting weight and 3D asymmetric lifting motion while considering dynamic joint torque limits. The proposed method has the potential to prevent individuals’ risk of injury for lifting.

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