In this thesis, the author proposes to estimate the clamp force using two types of Surface Electromyography (SEMG): rectified SEMG and integrated SEMG. The estimation result clearly shows that integrated SEMG performed 3.53 times better than rectified SEMG in the case of cross-correlation coefficient.
List of Abbreviation
INTRODUCTION 1.1 Prosthesis
- State of Art
- Objectives
- Contributions
- Potential Application of This Research
- Organization of this Thesis
- Hand
- Prehension
To evaluate the performance of the proposed method and compare with other techniques such as support vectors. However, not all grasping organs are used for tool use - for example, the giraffe's tongue is instead used for feeding and self-cleaning.
24 Hands as Tools
Anatomy of the Hand
Digits
- Hand bones
In human anatomy, the arm is the part of the upper limb between the shoulder and elbow joints. The ulna is one of the two long bones in the forearm, the other being the radius.
Muscle
- Superficial Muscles of Anterior Forearm Biceps brachii
The brachioradialis is the forearm muscle that flexes the forearm at the elbow. The pronator teres is a muscle of the human body (located mainly on the forearm) which, together with the pronator quadratus muscle, serves to pronate the forearm (it turns it so that the palm faces backwards (when the body is in an anatomical position)).
Electromyography (EMG)
Clinical use of surface EMG to treat more specific disorders began in the 1960s. The contraction of skeletal muscle is initiated by impulses in the neurons to the muscle and is usually under voluntary control.
Force predictions
Force-Sharing Problem
Reductionism
The result is a biomechanically underdetermined system in which there are fewer system equations than unknowns. This is due to the redundancy of the musculoskeletal system, in which more than one muscle often involves a given joint. A common strategy used to solve an undetermined system is to reduce the number of unknown forces until the number of equations and unknown forces are the same [57].
This is usually achieved by combining several muscles with similar functions into one muscle group and is called the reduction method. The approach has been used to estimate the muscle forces acting on the ankle joint [58] and the knee joint [59]. The major weakness of this method is that although it provides a mathematically unique solution for the forces within a muscle group, it does not determine the forces produced by individual muscles.
Direct Measurement of Tendon Force
Despite these limitations, direct measurement of tendon forces has contributed to our understanding of the control of the neuromuscular and musculoskeletal systems.
Optimization
- Static Optimization
- Dynamic Optimization
Linear objective functions have been criticized for their inability to predict the co-activation of synergistic muscles and that the solution is strongly related to the number of constraints imposed. Nonlinear objective functions can predict muscle coactivation, even without applying additional constraints. Nonlinear optimization improves muscle force predictions, but this method is usually more complex and less efficient than linear programming [66].
However, these methods offer limited insight into the underlying neuromuscular control strategy because the performance criterion is only resolved at a given moment in time. Dynamic optimization refers to the process of minimizing and maximizing the costs/benefits of some objective function over a period of time; it is sometimes referred to as optimal control. The dynamic optimization process involves using a model that includes the dynamics of a system to find the inputs (eg, muscle excitation signals) and all outputs (eg, forces) that maximize the performance of a task.
The advantage of the dynamic optimization approach is that it parallelizes the problem that must be solved by the CNS during voluntary goal-directed movement Dynamic optimization methods predict the muscle excitation signals that produce a given movement.
Electromyography (EMG)
Artificial Neural Networks
- Human Brain
- Model of an Artificial Neuron
- Artificial Neural Network Architecture
- Characteristics of Neural Networks
- Learning Methods of Neural Networks
- Artificial Neural Networks Using EMG
The human brain is one of the most complex things that is generally poorly understood. However, the concepts of neurons as the basic components of the brain are attributed to Ramon Y. It is the acceleration or delay of input signals that is modeled by weights.
Thus, the weights here are multiplicative factors of the inputs that account for synapse strength. The vertices of the graph can represent neurons (input/output) and the edges represent synaptic connections. The computational units of the hidden layer are known as hidden neurons or hidden units.
In this, each input pattern used to train the network is associated with an output pattern, which is the target of the desired pattern.
Support Vector Machine
- SVM Kernel Function
- Support Vector Regression
The formulation defines a boundary separating two classes in the form of a linear hyperplane in the data space, where the distance between the boundaries of the two classes and the hyperplane is known as the edge of the hyperplane [78]. Maximizing the edge of a hyperplane in both spaces is equivalent to maximizing the distance between class boundaries. An explicit definition of the nonlinear mapping 0(x) is avoided by using a kernel function defined as .
For a > 0, we can see a as a scaling parameter of the input data, and r as a shift parameter [79] that controls the threshold of mapping. Analogous to the "soft margin" loss function adapted to SV machines by Cortes and Vapnik, one can introduce slack variables i, to deal with otherwise infeasible constraints of the optimization problem (3.17). Hence we arrive at the ) formulation started in (3.12). In a sense, the complexity of a function's representation by SVs is independent of the dimensionality of the input space X, and depends only on the number of SVs.
It states that at the point of solution the product between dual variables and constraints must vanish.
Necessary tools
Experimental Setup
Subjects were asked to sit comfortably in an armed chair and their forearms were positioned on one arm of the chair (Figure 4.1).
Data Acquisition
The subjects were asked to fully relax their forearm muscles to avoid any other forces except the clamping force. We only shaved one subject's arm because other subjects only had tinny and thin hair on their arm that did not significantly interfere with the conductivity of the SEMG measurements. We used BIOPAC gel 101, formulated with 0.5% saline in a neutral base to ensure better conductivity between muscle and the electrode.
Three SEMG electrodes were placed above the middle of the muscle without adhesive tape (Figure 4.2). Subjects were asked to generate a dynamic force and SEMG was simultaneously recorded using the experimental setup. The minimum and maximum values are represented by "whiskers", the mean value is the red line in the box, and the box itself represents 50 percent of the force (Figure 4.4), SEMG (Figure 4.5) and integrated SEMG (Figure 4.6) data.
Pre-processing
Hidden layer: According to the function [82], the signal appearing at the output of neuron j is calculated as. Where is the local number of inputs applied to neuron j and wji is the synaptic weight connecting neuron I to neuron j, andy1 (n) is the input signal from neuron j or equivalently the functional signal appearing on output neuron i. If neuronj is the first hidden network layer, then m= mO and the index I refers to the index ih input terminal of the network, for which we are writing.
A Sigmoid nonlinearity is the hyperbolic tangent function, the most general form, and is defined by (4.5). The original data may be too large or too small in range, so we can rescale it to the correct range so that training and predictions will be faster. The reason for this is usually because the input data for the problem we were trying to solve contains a lot of 'features', or say 'attributes', so the input will be a set (or vector/array for example).
After preparing the specified format for libsvm and scaling, use the command prompt and go to the directory where there is an executable file such as svmtrain.exe, svmscale.exe, svmpredict.exe. Then run svmtrain.exe and svmpredict.exe.
Performance Evaluation Mean Square Error (MSE)
Wi-weight: set the parameter C of class i to weight*C, for C-SVC (default 1) -v n: n-fold cross-validation mode. In statistics and probability theory, standard deviation (represented by the symbol sigma, ) shows how much variation or "spread" there is from the mean (mean or expected value). A low standard deviation indicates that the data points tend to be very close to the mean, while high standard deviation indicates that the data points are spread over a large range of values.
An alternative measure of performance can be evaluated by correlation between the estimated force signal, F.n) and the original force signal, F(n). Where p, and o are the mean and standard deviation of F(n), and ,u and c are the mean and standard deviation of p(n).
CHAPTER 5
Results and Discussions 5.1 Database
Result and Discussion
After Training, the performance of our proposed method was evaluated using MSE and correlation coefficient respectively. At first, the optimal number of hidden neurons, which is 5, is found in Figure 5.1 and Figure 5.2. SEMG AND TARGET STRENGTH AND DATA POINTS RELATED TO INTEGRATED INPUT SEMG VALUES.
The experiment was conducted using two types of SEMG, such as (i) rectified SEMG and (ii) integrated SEMG. The dashed red line represents the force measured by the sensor (hand dynamometer) and the solid blue line represents the force estimated by the ANN estimator in Figure 5.3 and Figure 5.4.
MSE CORR MSE CORR MSE CORR MSE CORR
We can recall from Figure 4.3 that three electrodes' positions are marked with x, y and z and each of these electrodes has four individual positions marked as 1, 2, 3 and 4.
MSE CORR MSE CORR
Future Works
Wininger, “Forearm pressure signature as a predictor of grip strength,” Journal of Rehabilitation Research & Development, vol. Chao "Biomechanical analysis of static forces in the thumb during hand function", Journal of Bone and Joint Surgery, 59 A(1), pp. Napier, "The grasping movements of the human hand", Journal of Bone and Joint Surgery, 38B, p.
Zuelzer, “On the Construction, Circuitry, and Properties of Liquid Metal Strain Gauges,” Journal of Biomechanics 21, pp. Savelberg, “Dynamic muscle force predictions from EMG: an artificial neural network approach,” Journal of Electromyography and Kinesiology 9, pp. Stacey, “Artificial Neural Network Model for Generating Muscle Activation Patterns for Human Locomotion,” Journal of Electromyography and Kinesiology 11, pp.
Wing, “Age-related changes in grip strength and hand movement dynamics,” Journal of Motor Behavior , vol.
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Department of Biomedical Engineering
