An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning

BACKGROUND: The information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers. The iPCA pre-processing implies an optimization stage which has not yet been deeply explored. METHODS: The present study considers two factors in the iPCA stage: namely A (the fitness function), and B (the search algorithm). The A factor comprises two levels, namely A(1) (the classification error) and A(2) (the correlation factor). Otherwise, the B factor has four levels, specifically B(1) (the Sequential Forward Selection, SFS), B(2) (the Sequential Floating Forward Selection, SFFS), B(3) (Artificial Bee Colony, ABC), and B(4) (Particle Swarm Optimization, PSO). This work evaluates the incidence of each one of the eight possible combinations between A and B factors over the classification error of the MCS. RESULTS: A two factor ANOVA was performed on the computed classification errors and determined that: (1) the interactive effects over the classification error are not significative (F(0.01,3,72) = 4.0659 > f( AB ) = 0.09), (2) the levels of factor A have significative effects on the classification error (F(0.02,1,72) = 5.0162 < f( A ) = 6.56), and (3) the levels of factor B over the classification error are not significative (F(0.01,3,72) = 4.0659 > f( B ) = 0.08). CONCLUSIONS: Considering the classification performance we found a superiority of using the factor A(2) in combination with any of the levels of factor B. With respect to the time performance the analysis suggests that the PSO algorithm is at least 14 percent better than its best competitor. The latter behavior has been observed for a particular configuration set of parameters in the search algorithms. Future works will investigate the effect of these parameters in the classification performance, such as length of the reduced size vector, number of particles and bees used during optimal search, the cognitive parameters in the PSO algorithm as well as the limit of cycles to improve a solution in the ABC algorithm

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

In recent years, researchers have conducted many studies on the design and control of prosthesis devices that take the place of a missing limb. Functional ability of prosthesis hands that mimic biological hand functions increases depending on the number of independent finger movements possible. From this perspective, in this study, six different finger movements were given to a prosthesis hand via bioelectrical signals, and the functionality of the prosthesis hand was increased. Bioelectrical signals were recorded by surface electromyography for four muscles with the help of surface electrodes. The recorded bioelectrical signals were subjected to a series of preprocessing and feature extraction processes. In order to create meaningful patterns of motion and an effective cognitive interaction network between the human and the prosthetic hand, fuzzy logic classification algorithms were developed. A five-fingered and 15-jointed prosthetic hand was designed via SolidWorks, and a prosthetic prototype was produced by a 3D printer. In addition, prosthetic hand simulator was designed in Matlab/SimMechanics. Pattern control of both the simulator and the prototype hand in real time was achieved. Position control of motors connected to each joint of the prosthetic hand was provided by a PID controller. Thus, an effective cognitive communication network established between the user, and the real-time pattern control of the prosthesis was provided by bioelectrical signals

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer

Optimizing User Integration for Individualized Rehabilitation

User integration with assistive devices or rehabilitation protocols to improve movement function is a key principle to consider for developers to truly optimize performance gains. Better integration may entail customizing operation of devices and training programs according to several user characteristics during execution of functional tasks. These characteristics may be physical dimensions, residual capabilities, restored sensory feedback, cognitive perception, or stereotypical actions

Automatic hand phantom map generation and detection using decomposition support vector machines

Background: There is a need for providing sensory feedback for myoelectric prosthesis users. Providing tactile feedback can improve object manipulation abilities, enhance the perceptual embodiment of myoelectric prostheses and help reduce phantom limb pain. Many amputees have referred sensation from their missing hand on their residual limbs (phantom maps). This skin area can serve as a target for providing amputees with non-invasive tactile sensory feedback. One of the challenges of providing sensory feedback on the phantom map is to define the accurate boundary of each phantom digit because the phantom map distribution varies from person to person. Methods: In this paper, automatic phantom map detection methods based on four decomposition support vector machine algorithms and three sampling methods are proposed, complemented by fuzzy logic and active learning strategies. The algorithms and methods are tested on two databases: the first one includes 400 generated phantom maps, whereby the phantom map generation algorithm was based on our observation of the phantom maps to ensure smooth phantom digit edges, variety, and representativeness. The second database includes five reported phantom map images and transformations thereof. The accuracy and training/ classification time of each algorithm using a dense stimulation array (with 100 $$\times$$ × 100 actuators) and two coarse stimulation arrays (with 3 $$\times$$ × 5 and 4 $$\times$$ × 6 actuators) are presented and compared. Results: Both generated and reported phantom map images share the same trends. Majority-pooling sampling effectively increases the training size, albeit introducing some noise, and thus produces the smallest error rates among the three proposed sampling methods. For different decomposition architectures, one-vs-one reduces unclassified regions and in general has higher classification accuracy than the other architectures. By introducing fuzzy logic to bias the penalty parameter, the influence of pooling-induced noise is reduced. Moreover, active learning with different strategies was also tested and shown to improve the accuracy by introducing more representative training samples. Overall, dense arrays employing one-vs-one fuzzy support vector machines with majority-pooling sampling have the smallest average absolute error rate (8.78% for generated phantom maps and 11.5% for reported and transformed phantom map images). The detection accuracy of coarse arrays was found to be significantly lower than for dense array. Conclusions: The results demonstrate the effectiveness of support vector machines using a dense array in detecting refined phantom map shapes, whereas coarse arrays are unsuitable for this task. We therefore propose a two-step approach, using first a non-wearable dense array to detect an accurate phantom map shape, then to apply a wearable coarse stimulation array customized according to the detection results. The proposed methodology can be used as a tool for helping haptic feedback designers and for tracking the evolvement of phantom maps

Classification of EMG Signals using Wavelet Features and Fuzzy Logic Classifiers

Master'sMASTER OF ENGINEERIN

Development of threshold based EMG prosthetic hand

There is a real need of EMG (Electromyogram) based prosthetic hand for the amputee which should be economical as well as reliable. The cheap prosthetic hand available in market works passively. In those cases the patient does not feel the feeling of natural human hand. EMG based prosthetic hand provides the amputee feeling of natural human hand. The work that has been discussed here is to develop a prosthetic hand with one degree of freedom. The two motions developed were open and close. Most of the work is done at electronic level. The main work was to acquire the noiseless EMG signal and further to convert it into control signal for prosthetic hand, after suitable processing. For classification a threshold based technique has used rather than any classification technique like Artificial Neural Network (ANN), Fuzzy Logic and Genetic Algorithm (GA). It was tried to use the minimum hardware, without making any compromise with performance. It was done so, to achieve the target of developing a economical and reliable prosthetic hand. The threshold value used was variable and was controllable from outside by just varying the knob of potentiometer. This adds an additional dimension for tuning the device and scope to adjust the threshold according to muscle activity of subject. So the same prosthetic hand can be used by different amputees by just changing the threshold values only. The mechanical hand was having only two fingers to grasp the objects. The work was also extended to develop the frequency based Prosthetic hand. The scheme was to find out the frequency bands where the amplitude of open and close motions is different. The FFTs (Fast Fourier Transform) of EMG signal were calculated in MATLAB. The DSO (Digital Storage oscilloscope) was also having the facility of displaying the FFT of signal. It was found that there is certain possible frequency band which classifies the open and close motion of han

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

This book aims to provide state-of-the-art information on computer architecture and simulation in industry, engineering, and clinical scenarios. Accepted submissions are high in scientific value and provide a significant contribution to computer architecture. Each submission expands upon novel and innovative research where the methods, analysis, and conclusions are robust and of the highest standard. This book is a valuable resource for researchers, students, non-governmental organizations, and key decision-makers involved in earthquake disaster management systems at the national, regional, and local levels

Intelligent Controller Design For Multifunctional Prosthetics Hand

Prosthetics hand is replacement of original hands that lose or damage because of war, trauma, accident or congenital anomalies. However, problems often occur on a prosthetics hand when dealing with the control capabilities and devising functional. Thus, an advanced mechanical design with control approach is required to improve the performance in terms of quality control in prosthetics hand and also enhance existing capabilities to the optimum level. This paper aims to develop a functional prosthetics hand at upper limb, which will focus on position of human hand particularly using the movement of finger instructions. In this paper, an intelligent controller, Fuzzy with Proportional-Integral-Derivative (Fuzzy-PID) controller is proposed to realize accurate force control with high performance. The performance of prosthetics hand model controlled by Fuzzy-PID controller is outperform the conventional PID controller and Fuzzy controller, where the improvement of the transient response and steady state error is achieved. Performance comparison of three different controllers has been presented through these evaluation process