Development of a Human Computer Interface

Development of a Human Computer Interface rook HCI( charit suitable estimator interaction) has become hotshot of the important aspect in benevolent life. Signals gene commitd from clement body argon bio points and has huge potential to be use as an embrasure for human schooling processing system devices. Multiple devices ar express that recognizes these boio aims which is generated during ponderosity contraction and converting those requests into some command to be utilise as an input to the HCI devices. However, the task can be acquired through bio auspicates which forms a aflutter linkage with the electronic figurer proficiencys like Electro-Encephalogram(EEG), Electrooculogram(EOG), and Electromyogram(electromyogram). In past, there subscribe been separate of studies wherein many researchers have use bio preindications to go other device. electromyogram is hence, one of the least explored mechanism form of bio bode to be deployed in HCI and its studies are servic eable for neuromuscular system as accepted affections may slow atomic pile brawn contraction and muscle firing leading to paralysis of muscle.Keywords electromyogram, HCI, biosignals, atrophied muscles, unquiet linkage.1 IntroductionHCI is the one of the research area that emerged in early 1980s, which has expanded rapidly it was previously known as a man- machine interaction. HCI focuses on the port between substance abuser and the computer and deals with the design, act and assessment of computer system and other related receptive that are for human use. Designing interactive computer systems to be effective, efficient, easy and enjoyable to use is important, so that hatful and society may realize the benefits of numeration base devices 1. The researchers observes the way human interacts with the computer system and design hot technologies and larboard that lets human and computers to interaction invention ways 2. Some of the grammatical case of popular HCI technique s are image processing, speech recognition, bio signal processing and so forth HCIs goal is to minimize the differences between the humans goal of what they want to happen upon and the understanding level of computer to perform the task. It relates knowledge from both the human and machine side. Due to its multidisciplinary nature, stack with different study areas contri howevere to its success. mannikin 1 shows the areas where HCI can be implemented with distinctive importance.Fig.1. Disciplines contribute to HCI 3electromyogram is an electro medical procedure for estimating and recording the voltaical signals produced by penurious muscle. electromyogram is performed utilize electromyography, to produce an electrical record or signal called electromyogram 4. An electromyography detects the electric potential generated by skeletal muscle cells when these cells are actuate electrically or neurologically. The electromyogram technology dishs capture apparent transactions as i nputs for realistic joysticks, keyboards leading to sore application in mobile computing etc 5. This signal can also be analyzed to detect medical abnormalities, activation level, or biomechanics of human elbow grease. The motor neurons of a human body transmit electrical signals that causes muscle to contract and an electromyogram translate this signals to graphs, sound or numerical determine that can be interpreted by analyst. EMGs signal can be easily acquired employ electrodes and it is of twain types, dry electrode that is condition contact with the spit come out that records muscles activity from the airfoil above the muscle on the skin and require more than one electrode, because EMG recording displays the electric potential difference between two separate electrodes, second is gelatin or inserted EMG which can be performed using a electrolytic capacitor gel as a chemical interface between the skin and electrolyte 6. A needle electrode and fine wire electrode is the example of inserted electrode. Needle electrode is apply in clinical areas and the tip of the electrode is bare and utilise for the originate detection. beautiful wire electrode they are easily implanted in and withdrawn from the skeletal muscles, and is less painful then needle electrode. Thus EMG has a variety of clinical and biomedical applications where it is employ to diagnose neuromuscular disease and many other disorders of motor take.2EMG exercisingd for HCIStudies are being carried out for the use of EMG signals inorder to identify disabilities as a significant mo of individuals are suffering from severe motor disabilities, due to variety of causes, such as Spinal Cord Injury (SCI), Amythorphic Lateral Sclerosis (ALS) and so on 7. at that placefore, EMG signal are not only apply for identifying neuromuscular disorder but can also be as a curtail signals for prosthetic devices 8. It is the least explored compared to others biosignals like EEG, EOG etc. EMGs a re natural performer of HCI because the electrical signals induced by human muscle gallery during its contraction represents nueromuscular movement that can be interpreted and alter into computers witness command. EMG signals can be used for a crook of applications including clinical applications, HCI and interactive computer gaming. Basically EMG can be used to sense isometric muscular activity which does not transalate into movement thus making it possible to classify subtle motionless apparent movements and to have interfaces without being noticed and without disrupting the surrounding environment 9. The EMG signal have different signatures i.e, two peoples gesture might be identical but their characteristics EMG signals are different interms of their age, muscle instruction skin toothsome work and gesture style. One of the occupation of EMG is its signal contains a different type of noise that are caused by equipment noise, electromagnetic ray of elucidate etc and h ence preprossing is needed to filter out the unwanted noise in EMG signal.3Related worksResearchers have worked on regarding how EMG signal is used to command some other devices like prosthetic arm, robots or enable people with certain disabilities. These are shown in following paper.In 1996 Yasuharu Koike et.al, 10 veritable a human interface employing a model of an arm, robot control of an artificial great deal, and the figureing of motion capability. The aim of this paper was to construct a complete forward dynamics model of the human arm by using Artificial queasy communicate (ANN). The model has the ability to learn physiological recordings of EMG signals for simultaneous measurement of movement.In 2000 Alsayegh et.al, 11 proposed an EMG ground signal where EMG signal is limited to tether arm muscles (medial Deltoid) MD, (anterior deltoid) AB, (biceps brachii) BB that was able to recognize 12 arm gestures. The processing of EMG signal is base on arm gestures having uniq ue temporal coordination. The motley technique used is context dependent categorisation within the framework of Bayes theorem. non only the unique arm gesture by using EMG signal was developed there were motley researchers working in the scope of EMG for the people suffering with motor disabilities like present paralysis, complication paralysis etc.In 2004 Jong sung kim et.al, 12 proposed a natural means of human computer interaction induced by human implements of war muscle movement and the generated EMG signal to be used as computer commands control. The paper developed an online EMG MOUSE system that controls movement of the cursor, which are interpretation of 6 pre-defined motions, up, down, left, right, click and rest. A Fuzzy arcminute soap Neural Network (FMMNN) is used as a classifier.In 2005 Inhyuk Moon et.al, 13 proposed a novel vesture EMG based HCI for the wheelchair user with severe motor disabilities caused by C4 and C5 spinal cord injury. The EMG signal is acquired by left, right and both shoulder elevation motion. EMG wearable device directly generates MAV (Mean Average Value) signal from raw EMG. The MAV signal is converted to digital data using AD converter embedded in a mettlesome speed micro control condition. The recognized result is sent to the wheelchair controller via Bluetooth communication module. The following category one more paper regarding people suffering from motor disabilities was presented.In 2006 Ki-Hong Kim et.al, 14 developed an interface that relies on EMG signal acquired from human face during contraction of muscle. Electrodes are placed well-nigh brow, cheeks and eyes. The subject was made to perform some actions like blinking of eyes, clenching of teeth, wrinkling of forehead and frowning. The signal is acquired and analyzed using LPC (Linear Prediction Coefficient) and LPC entropy were calculated to rise up the characteristics information contained in the measured signal. For pattern recognition Hidden Markov perplex (HMM) is used. Same year some were working on knock over gesture recognition.In 2006 Ganesh R Naik ei.al, 15 proposed an approach to identify hand gestures using muscle activity separated from electromyogram using ICA (Independent Component Analysis). The aim of the look into in this paper was to test the use of ICA for separation of the EMG signals for the excogitation of identifying hand gestures and actions.After the recognition of hand gestures and enabling motor disabilities, in 2008 JonghwaKim et.al, 16 proposed modification of a RC car that is controlled by users hand signs, instead of using remote control unit. The interfacing system first calculates relevant traces in the EMG signal of quad hand signs, classifies the hand signs into the quaternity classes, and assigns the result to certain steering commands for the RC car. For feature extraction RMS was used calculated by observing last 16 incoming values. For compartmentalization KNN and Bayes theor em was combined using decision tree and aspire a control the car via PC. as well as in 2009 Jun-Ru Ren et.al, 17 studied an Electromyogram Based on HCI. This paper showed a control system using fortify electromyography that is proposed for computer peripheral control and artificial prosthesis control. The system intends to realize the commands of six pre defined hand poses i.e. up, down, left, right, yes and no. Power spectral density (PSD) is used to measure signal power intensity and for classifier the Bayesian classifier is used for extracting feature. In the same year Ahsan et.al, 9 categorize EMG signal techniques to help improve interface for change people. This paper discusses various methodologies and techniques for interpreting EMG signal.Researchers extended their study to multistep EMG mixed bag in 2010 Armando Barreto et.al, 19 proposed a system that can effectively help disabled people from the neck down to interact with computer or communicate with people through computers using point and click vivid interfaces. The EMG signal is generated using facial muscle with a jibe cursor movement command.In 2011 get up EMG has attracted an attention of researchers for interface signal. Ishii et.al, 20 studied about myoelectric prosthetic in which arm/hand gesture is distinguished by identification of the surface Electromyogram. For identification of motion uneasy meshing is used.In 2012 Takeshi Tsujimura et.al, 21 studied Hand Sign Classification Employing Myoelectric Signals of Forearm. The purpose of this paper was to design an uncomplicated system to identify finger motion and to develop innovative HMI. This paper also distinguishes the hand signs by analyzing the forearm EMG signals. It relies on the proposition that the specific muscles of forearm work dismantle if fingers are moved.Researchers studied through multichannel surface EMG signals and in 2014 Han Li et.al, 4 showed HCI system Based on the multichannel SEMG of the hand gesture r ecognition based on the feature extraction, identification, classification and control of the SEMG which controls quad copter flight. In this paper, the four different gestures can be distinguished accurately to complete the real-time interactive process. The experimental results show that the HCI system based on SEMG has high accuracy. Auto regression method is used for analysis of SEMG signal and the classification is done using back propogation technique.In 2015 Ahmed Mehaoua et.al, 18 designed a novel EMG based system that aims to control multimedia player in simple, efficient and flexible manner. The objective of this paper was to provide efficient control system seeking to simplify the life of hand amputee persons by allowing them to control media player through EMG signals generated by muscle activation from forearm contraction. The electrical potential generated allows start, stop a video or change by reversal between a set of media. For detecting muscle contraction four s teps is used rectification, filtering, linear envelop and onset contraction and turns the signal into usable form. After detection of muscle contraction, system was enhanced by adding commands like start, stop, previous, next and pause.4Summary of SurveyThe survey paper focuses on rating and detection of an EMG signal and use of this system for real time. There are many classification methodologies and artificial intelligence techniques based on nervous network to classify EMG signal. Some of the techniques are ANN, K-Nearest populate (KNN), and Support Vector Machine (SVM) etc.4.1. Back Propagation Neural NetworkBack propagation algorithm is applied on the multichannel SEMG 18 of the hand gesture recognition based on the featureextraction and control of the SEMG which controls quad copter flight. BP neural network contains three parts the BP neural network building, the BP neural network traning and the BP neural network classification.BP neural network building is determined c oncord to the input and output charasteristics of the system structure of the BP neural network. The number of the AR (auto regression) model coefficientof input vector is 16 and the number of the output is 4,so theinput layer has 16 nodes and the output layer has 4 nodes.4.2. Fuzzy Min grievous bodily harm Neural NetworkJong-Sung Kim 12 applied fuzzy mean max neural network (FMMNN) as a classifier for online EMG mouse that controls computer cursor. Also, stochastic values such as integral absolute value were used as features for an appropriate classification of the intended wrist motions. 6 predefined wrist motions to left, right, up, down, click and rest operation were determined.4.3. Hidden Markov ModelKi-Hong Kim 14 developed an interface using EMG signal from human face.For pattern recognition HMM comprised three states and two Gaussian mixtures per state is employed which is used as a classifier. The standalone interface system was implemented and the subject (people as volu nteers) were able to set up the wheelchair turn left, right, forward and backward by simple action provided by them. Classification is done by comparing the likelihood values of an whimsical feature sequence evaluated from four HMMs, HMML, HMMR, HMMF, and HMMB for left, right, forward, and backward, respectively, and selecting the model with the maximum value.4.4. Bayes NetworkAlsayegh et.al, 11 presented an EMG-based human-machine interface system that interprets arm gestures in the 3-dimensional (3D) space. Gestures are interpreted by sensing the activities of three muscles, namely, anterior deltoid (AD), medial deltoid (MD), and biceps brachii (BB) muscles. The problem of gesture classification is carried out in a framework of the statistical pattern recognition. The processing of the EMG signals utilizes the temporal coordination activity of the monitored muscles to identify a particular gesture. The classification procedure is carried out by constructing successive feature ve ctors for each gesture. These feature vectors describe the gestures temporal signature. This type of classification is referred to as the context-dependent classification, which is carried out in this study within the framework of Bayes theorem. The development of an EMG based interface for hand gesture recognition is presented by Jonghwa Kim et.al, 16. For realizing real-time classification assuring acceptable recognition accuracy, they introduced the combination of two simple linear classifiers (K-nearest neighbour (KNN) KNN and Bayes) in decision level fusion. slacken 1 provides the summary of the survey in accordance with the methodologies used in various papers. It provides the description of the success rate resulted by the use of classification techniques.Table 1.Summary of major methods used for EMG classificationClassifier used cognomen ResearchersDescription Back Propagation Neural NetworkHuman computer interaction system design based on surface EMG signals. Han Li , Xi C hen, et.al, (2014)93% success rate in the multichannel SEMG of the hand gesture recognition.Auto-regressive model method is used.Hidden Markov ModelA mulish biosignal-based human interface applicable to the assistive systems for people with motor impairment. Ki-Hong Kim et.al (2006)97% success rate in developing an interface using human face.Subject was able to turn left, right, forward and backward.Fuzzy Min Max Neural NetworkA new means of HCI EMG-mouse. Jong-sung Kim et.al, (2004)Stochastic values such as integral absolute values were used as feature extraction.Six distinctive wrist motions can be classified well.Pattern recognition rate of each wrist motions is above 90%.Bayes NetworkA practical EMG-based human-computer interface for users with motor disabilities. Alsayegh et.al,(2000)classification is done in a framework of statistical pattern recognition.classification rate reported was 96%.EMG-based hand gesture recognition for realtime biosignal interfacing. Jonghwa Kim et, al, (2008)K-Nearest live (k-NN) classifier added with Bayes to obtain good resultAverage classification rate reported was over 94%.5ConclusionDeveloping better human computer interface will help improve quality of life of people suffering from physical disabilities. EMG signal is one of the natural technique that captures electrical signals from human body for the use of HCI and provides an interface for human and computer to interact appropriotely. This survey paper focuses on the work of various researchers, the methodologies used for the classification of EMG signal. Therefore, it can be concluded from the survey of various paper that neural network has been used as a openhanded classification technique of EMG signal for HCI.For future works new and more enchanced classification techniques can be developed besides neural network, a work can be done in creating light weight EMG signal, multiclass hand process and on-line processing.ReferencesDix, A. 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