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This paper aims at the classification of hand gestures using electromyographic signals (EMG) obtained through a MyoTM armband, which has eight medical grade electrodes. Each electrode provides information regarding muscles contraction performed during the execution of the movement. From these electrodes signals are extracted seven features for each one of eight electrodes. After extraction of the...
In recent years, the development of EMG-based assistive technologies has received special attention of the researchers. This can be attributed to the advantages of EMG signal over other biosignals. The present study proposes the development of a dual-channel EMG biopotential amplifier. The biopotential amplifier was designed using INA128 instrumentation amplifier and was used for acquiring EMG signals...
Rapid disability patients increasing over time and need a solution in the future. Hand amputation is one form of disability that common in Indonesian society. A possible solution would be necessary at the moment is the development of prosthetic hand that has the ability as a human hand. The development of neuroscience has now reached the stage of the body's ability to use the signal as an input signal...
Electromyography (EMG) signal can be defined as a measure of electrical activity produced by skeletal muscles. It can be used in handling electronic devices or prosthesis. If we are able recognize the hand gesture captured using EMG signal with greater reliability and classification rate, it could serve a good purpose for handling the prosthesis and to provide the good quality of life to amputees...
This paper describes pattern recognition of electromyography (EMG) signal during load lifting using Artificial Neural Network (ANN). EMG is a method to measure and record the muscle activity when individuals perform certain operation and actions. This research will classify the EMG signal based on force apply to the arm due to the gravity act on it during load lifting. Recognizing pattern based on...
This paper aims at building a portable robotic hand for physically disabled people to perform basic hand movements. Surface Electromyography(EMG) signal is collected from muscles of human forearm to extract the subject's intentions of action, where six kinds of gestures are selected for discussion. An Artificial Neural Network(ANN) is trained and utilized to distinguish the desired movement according...
Detecting the stress of computer user in an office like environment will enable more development of computer and make it intelligent enough where it can interact with its user, taking users effective state in to account; known as affective computing. In this research work, we have analyzed physical and mental stress of a computer user in all day long working environment by analyzing variations in...
Communication and sign-language learning of the people with hearing disabilities in Thailand has been problematic due to limited number of sign-language experts. To facilitate the sign-language learning and communication between the hearing disability and ordinary people, the sign language-to-alphabet spelling conversion was developed based on electromyography (EMG) signal recorded from the forearm...
There has been wide range of expansion in hand gestures close in style, pattern, movement and feature as well as in meaning. From that aspect this following paper confabs an improved version of hand gesture recognition based on segmentation process and backpropagation algorithm through the analysis of myoelectric signals generates from the movement of brachioradialis muscle and antebrachial vein of...
This paper presents the design, optimization and performance evaluation of artificial neural network for the efficient classification of Electromyography (EMG) signals. The EMG signals are collected for different types of volunteer hand motion which are processed to extract some predefined features as inputs to the neural network. The time and time-frequency based extracted feature sets are used to...
Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based...
Today's advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction...
This paper presents a novel approach to optimize pattern recognition system using genetic algorithm (GA) to identify the type of hand motion employing artificial neural networks (ANNs) with high performance and accuracy suited for practical implementations. To achieve this approach, electromyographic (EMG) signals were obtained from sixteen locations on the forearm of six subjects in ten hand motion...
The present work reports the use of Support Vector Machines (SVMs) as classifier of myoelectric signals. This tool was recently used to analyze data and recognize patterns, but just a few studies report its use in myoelectric registers. The aim of this research is analyze and compare some classification schemes employing Artificial Neural Networks and Linear Discriminant Analysis in order to establish...
The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements are quite small. On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced...
With the aim to control a multiple degrees of freedom electromechanical devices, e.g., assistive robots, powered wheelchair, etc., this paper proposes a real-time multichannel surface electromyography classification scheme based on the coordination or synergies between a functional group of muscles: biceps brachii, triceps brachii, pronator teres, and brachioradialis. The muscular synergy is evaluated...
In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The electromyography (EMG) signals can be used as a control source of artificial arm after it has been processed. The objective of this work is to achieve better classification...
Myoelectric or electromyogram (EMG) signals can be useful in intelligently recognizing intended limb motion of a person. This paper presents an attempt to develop a four-channel EMG signal acquisition system as part of an ongoing research in the development of an active prosthetic device. The acquired signals are used for identification and classification of six unique movements of hand and wrist,...
In this paper, Hidden Markov Model of surface electromyography (EMG) algorithm that facilitates automatic SEMG feature extraction and artificial neural network (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of neuromuscle disorders. The investigated ANN were: the multilayer backpropagation algorithm. The percentage of correct classification reaches 90...
In this paper we describe a method to classify online sleep/wake states of humans based on cardiorespiratory signals for wearable applications. The method is designed to be embedded in a portable microcontroller device and to cope with the resulting tight power restrictions. The method uses a Fast Fourier Transform as the main feature extraction method and an adaptive feed-forward Artificial Neural...
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