Determination of the Geometric Parameters of a Parallel-Serial Rehabilitation Robot Based on Clinical Data
20 Sep 2020-pp 556-566
TL;DR: Effective numerical methods and algorithms were developed and tested that made it possible to determine the minimum geometric parameters of the active parallel mechanism that ensure the movement of the passive orthosis within the workspace under clinical data when simulating walking.
Abstract: The article discusses the structure and model of a robotic system for the rehabilitation of the lower limbs based on a passive orthosis in the form of a serial RRRR mechanism and an active parallel 3-PRRR mechanism. Effective numerical methods and algorithms were developed and tested that made it possible to determine the minimum geometric parameters of the active parallel mechanism that ensure the movement of the passive orthosis within the workspace under clinical data when simulating walking. The structure is proposed. The basis parameters for the rehabilitation system design are investigated. To implement the developed methods, an effective algorithm, software package, and visualization system for exported three-dimensional workspaces in STL format were synthesized. The results of mathematical modeling and analysis of the results are given.
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25 Mar 2022
TL;DR: A mathematical model of the robotic system was obtained, graphs were constructed demonstrating the accuracy of working out a given trajectory due to the anthropometric parameters of the patient.
Abstract: The paper presents structural diagrams of the control system of a robotic system with feedback on the patient’s effort and without it, illustrates the conditions for using each of them; a method is prepared to increase the reliability of data on the applied efforts, associated with the use of strain gauges in the absence of actions of the patient’s motor organs; an example of the patient’s motivation to perform ”correct” movements, ”helping” the mechanism, consisting in changing the speed of movement to form the patient’s feeling that the mechanism moves due to his efforts; a mathematical model of the robotic system was obtained, graphs were constructed demonstrating the accuracy of working out a given trajectory due to the anthropometric parameters of the patient.
TL;DR: In this paper , an optimized model of rehabilitation systems based on a cable-actuated robot for rehabilitation of patients with impaired motor function of upper and lower limbs is discussed, and the positions of cable-driven actuators are determined such that the cables do not touch the patient's body.
Abstract: The paper discusses an optimized model of rehabilitation systems based on a cable-actuated robot designed for rehabilitation of patients with impaired motor function of upper and lower limbs. Tensile strength and cable lengths depending on joint angles are calculated to determine optimal positions of coils, with due account of the effects elasticity and gravity forces produce during rehabilitation. Based on the calculations, the positions of cable-driven actuators are determined such that the cables do not touch the patient’s body and the optimal forces of the actuators are ensured.
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Book•
01 Jan 1996TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Abstract: From the Publisher:
"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.
-- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
9,933 citations
Book•
30 Aug 2001TL;DR: In this paper, the authors present a set-based approach for estimating the length of a set with respect to a set of set operators and the number of sets in the set.
Abstract: I. Introduction.- 1. Introduction.- 1.1 What Are the Key Concepts?.- 1.2 How Did the Story Start?.- 1.3 What About Complexity?.- 1.4 How is the Book Organized?.- II. Tools.- 2. Interval Analysis.- 2.1 Introduction.- 2.2 Operations on Sets.- 2.2.1 Purely set-theoretic operations.- 2.2.2 Extended operations.- 2.2.3 Properties of set operators.- 2.2.4 Wrappers.- 2.3 Interval Analysis.- 2.3.1 Intervals.- 2.3.2 Interval computation.- 2.3.3 Closed intervals.- 2.3.4 Interval vectors.- 2.3.5 Interval matrices.- 2.4 Inclusion Functions.- 2.4.1 Definitions.- 2.4.2 Natural inclusion functions.- 2.4.3 Centred inclusion functions.- 2.4.4 Mixed centred inclusion functions.- 2.4.5 Taylor inclusion functions.- 2.4.6 Comparison.- 2.5 Inclusion Tests.- 2.5.1 Interval Booleans.- 2.5.2 Tests.- 2.5.3 Inclusion tests for sets.- 2.6 Conclusions.- 3. Subpavings.- 3.1 Introduction.- 3.2 Set Topology.- 3.2.1 Distances between compact sets.- 3.2.2 Enclosure of compact sets between subpavings.- 3.3 Regular Subpavings.- 3.3.1 Pavings and subpavings.- 3.3.2 Representing a regular subpaving as a binary tree.- 3.3.3 Basic operations on regular subpavings.- 3.4 Implementation of Set Computation.- 3.4.1 Set inversion.- 3.4.2 Image evaluation.- 3.5 Conclusions.- 4. Contractors.- 4.1 Introduction.- 4.2 Basic Contractors.- 4.2.1 Finite subsolvers.- 4.2.2 Intervalization of finite subsolvers.- 4.2.3 Fixed-point methods.- 4.2.4 Forward-backward propagation.- 4.2.5 Linear programming approach.- 4.3 External Approximation.- 4.3.1 Principle.- 4.3.2 Preconditioning.- 4.3.3 Newton contractor.- 4.3.4 Parallel linearization.- 4.3.5 Using formal transformations.- 4.4 Collaboration Between Contractors.- 4.4.1 Principle.- 4.4.2 Contractors and inclusion functions.- 4.5 Contractors for Sets.- 4.5.1 Definitions.- 4.5.2 Sets defined by equality and inequality constraints.- 4.5.3 Improving contractors using local search.- 4.6 Conclusions.- 5. Solvers.- 5.1 Introduction.- 5.2 Solving Square Systems of Non-linear Equations.- 5.3 Characterizing Sets Defined by Inequalities.- 5.4 Interval Hull of a Set Defined by Inequalities.- 5.4.1 First approach.- 5.4.2 Second approach.- 5.5 Global Optimization.- 5.5.1 The Moore-Skelboe algorithm.- 5.5.2 Hansen's algorithm.- 5.5.3 Using interval constraint propagation.- 5.6 Minimax Optimization.- 5.6.1 Unconstrained case.- 5.6.2 Constrained case.- 5.6.3 Dealing with quantifiers.- 5.7 Cost Contours.- 5.8 Conclusions.- III. Applications.- 6. Estimation.- 6.1 Introduction.- 6.2 Parameter Estimation Via Optimization.- 6.2.1 Least-square parameter estimation in compartmental modelling.- 6.2.2 Minimax parameter estimation.- 6.3 Parameter Bounding.- 6.3.1 Introduction.- 6.3.2 The values of the independent variables are known.- 6.3.3 Robustification against outliers.- 6.3.4 The values of the independent variables are uncertain.- 6.3.5 Computation of the interval hull of the posterior feasible set.- 6.4 State Bounding.- 6.4.1 Introduction.- 6.4.2 Bounding the initial state.- 6.4.3 Bounding all variables.- 6.4.4 Bounding by constraint propagation.- 6.5 Conclusions.- 7. Robust Control.- 7.1 Introduction.- 7.2 Stability of Deterministic Linear Systems.- 7.2.1 Characteristic polynomial.- 7.2.2 Routh criterion.- 7.2.3 Stability degree.- 7.3 Basic Tests for Robust Stability.- 7.3.1 Interval polynomials.- 7.3.2 Polytope polynomials.- 7.3.3 Image-set polynomials.- 7.3.4 Conclusion.- 7.4 Robust Stability Analysis.- 7.4.1 Stability domains.- 7.4.2 Stability degree.- 7.4.3 Value-set approach.- 7.4.4 Robust stability margins.- 7.4.5 Stability radius.- 7.5 Controller Design.- 7.6 Conclusions.- 8. Robotics.- 8.1 Introduction.- 8.2 Forward Kinematics Problem for Stewart-Gough Platforms.- 8.2.1 Stewart-Gough platforms.- 8.2.2 From the frame of the mobile plate to that of the base.- 8.2.3 Equations to be solved.- 8.2.4 Solution.- 8.3 Path Planning.- 8.3.1 Graph discretization of configuration space.- 8.3.2 Algorithms for finding a feasible path.- 8.3.3 Test case.- 8.4 Localization and Tracking of a Mobile Robot.- 8.4.1 Formulation of the static localization problem.- 8.4.2 Model of the measurement process.- 8.4.3 Set inversion.- 8.4.4 Dealing with outliers.- 8.4.5 Static localization example.- 8.4.6 Tracking.- 8.4.7 Example.- 8.5 Conclusions.- IV. Implementation.- 9. Automatic Differentiation.- 9.1 Introduction.- 9.2 Forward and Backward Differentiations.- 9.2.1 Forward differentiation.- 9.2.2 Backward differentiation.- 9.3 Differentiation of Algorithms.- 9.3.1 First assumption.- 9.3.2 Second assumption.- 9.3.3 Third assumption.- 9.4 Examples.- 9.4.1 Example 1.- 9.4.2 Example 2.- 9.5 Conclusions.- 10. Guaranteed Computation with Floating-point Numbers.- 10.1 Introduction.- 10.2 Floating-point Numbers and IEEE 754.- 10.2.1 Representation.- 10.2.2 Rounding.- 10.2.3 Special quantities.- 10.3 Intervals and IEEE 754.- 10.3.1 Machine intervals.- 10.3.2 Closed interval arithmetic.- 10.3.3 Handling elementary functions.- 10.3.4 Improvements.- 10.4 Interval Resources.- 10.5 Conclusions.- 11. Do It Yourself.- 11.1 Introduction.- 11.2 Notions of C++.- 11.2.1 Program structure.- 11.2.2 Standard types.- 11.2.3 Pointers.- 11.2.4 Passing parameters to a function.- 11.3 INTERVAL Class.- 11.3.1 Constructors and destructor.- 11.3.2 Other member functions.- 11.3.3 Mathematical functions.- 11.4 Intervals with PROFIL/BIAS.- 11.4.1 BIAS.- 11.4.2 PROFIL.- 11.4.3 Getting started.- 11.5 Exercises on Intervals.- 11.6 Interval Vectors.- 11.6.1 INTERVAL_VECTOR class.- 11.6.2 Constructors, assignment and function call operators.- 11.6.3 Friend functions.- 11.6.4 Utilities.- 11.7 Vectors with PROFIL/BIAS.- 11.8 Exercises on Interval Vectors.- 11.9 Interval Matrices.- 11.10 Matrices with PROFIL/BIAS.- 11.11 Exercises on Interval Matrices.- 11.12 Regular Subpavings with PROFIL/BIAS.- 11.12.1 NODE class.- 11.12.2 Set inversion with subpavings.- 11.12.3 Image evaluation with subpavings.- 11.12.4 System simulation and state estimation with subpavings.- 11.13 Error Handling.- 11.13.1 Using exit.- 11.13.2 Exception handling.- 11.13.3 Mathematical errors.- References.
736 citations
TL;DR: Stoke patients who received physiotherapy treatment in combination with robotic devices, such as Lokomat or Gait Trainer, were more likely to reach better results, compared to patients who receive conventional gait training alone.
135 citations
TL;DR: Ekso™ gait training seems promising in gait rehabilitation for post-stroke patients, besides OGT, and the study proposes a putative neurophysiological basis supporting Ekso™ after-effects.
Abstract: The use of neurorobotic devices may improve gait recovery by entraining specific brain plasticity mechanisms, which may be a key issue for successful rehabilitation using such approach. We assessed whether the wearable exoskeleton, Ekso™, could get higher gait performance than conventional overground gait training (OGT) in patients with hemiparesis due to stroke in a chronic phase, and foster the recovery of specific brain plasticity mechanisms. We enrolled forty patients in a prospective, pre-post, randomized clinical study. Twenty patients underwent Ekso™ gait training (EGT) (45-min/session, five times/week), in addition to overground gait therapy, whilst 20 patients practiced an OGT of the same duration. All individuals were evaluated about gait performance (10 m walking test), gait cycle, muscle activation pattern (by recording surface electromyography from lower limb muscles), frontoparietal effective connectivity (FPEC) by using EEG, cortico-spinal excitability (CSE), and sensory-motor integration (SMI) from both primary motor areas by using Transcranial Magnetic Stimulation paradigm before and after the gait training. A significant effect size was found in the EGT-induced improvement in the 10 m walking test (d = 0.9, p < 0.001), CSE in the affected side (d = 0.7, p = 0.001), SMI in the affected side (d = 0.5, p = 0.03), overall gait quality (d = 0.8, p = 0.001), hip and knee muscle activation (d = 0.8, p = 0.001), and FPEC (d = 0.8, p = 0.001). The strengthening of FPEC (r = 0.601, p < 0.001), the increase of SMI in the affected side (r = 0.554, p < 0.001), and the decrease of SMI in the unaffected side (r = − 0.540, p < 0.001) were the most important factors correlated with the clinical improvement. Ekso™ gait training seems promising in gait rehabilitation for post-stroke patients, besides OGT. Our study proposes a putative neurophysiological basis supporting Ekso™ after-effects. This knowledge may be useful to plan highly patient-tailored gait rehabilitation protocols. ClinicalTrials.gov
, NCT03162263
.
111 citations