Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.

Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap$632+$and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to$sim 96$correct classification rates with less than 10% of the original features

Impulsive differential equations by using the Euler method

The theory of impulsive differential equations is emerging as an important area of investigation since such equations appear to represent a natural framework for mathematical modeling of several real phenomena. There have been intensive studies on the qualitative behavior of solutions of the impulsive differential equations. However, many impulsive differential equations cannot be solved analytically or their solving is complicated. In this paper, we represent the algorithm which follows the theory of impulsive differential equations to solve the impulsive differential equations by using the Euler methods. It is clearly shown the impulsive operators k I that acts at the moments k t influence the error. Finally, the better convergence result of the numerical solution is given by solving the numerical examples

Impulsive differential equations by using the Euler method

The theory of impulsive differential equations is emerging as an important area of investigation since such equations appear to represent a natural framework for mathematical modeling of several real phenomena. There have been intensive studies on the qualitative behavior of solutions of the impulsive differential equations. However, many impulsive differential equations cannot be solved analytically or their solving is complicated. In this paper, we represent the algorithm which follows the theory of impulsive differential equations to solve the impulsive differential equations by using the Euler methods. It is clearly shown the impulsive operators k I that acts at the moments k t influence the error. Finally, the better convergence result of the numerical solution is given by solving the numerical examples