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EMG Decoder - kalarickal - 10-04-2017 EMG Decoder
A Project Report by Sarath S.Nair M105114 Department of Computer Science & Engineering College of Engineering Trivandrum Kerala - 695016 2010-11 [attachment=8622] Abstract Many physically disabled individuals are deterred from using computers due to their inability to utilize a hand-controlled mouse. However, if clicking or dragging of an icon can be achieved, these individuals would be able to take on the functions of a mouse without the use of hands. We propose to design and build an electromyogram (EMG) biopotential decoder in order to decode a bio signal due to hand muscle movements and to use this signal to generate the mouse click as well as drag actions. This design can also be used as a model for future advancements in human-computer interactions. The EMG biopotential ampli er should be capable of detecting frequencies between dc-10 Hz, the range at which most ocular movements operate. The EMG signal is in the microvolt range (100-4500 V). Therefore, when the DC o set is removed, it will be challenging to obtain a strong, usable signal given the minute nature of the recorded signal. Our choice of an EMG over other possible methods was selected based on the ease of usage and the low cost of production. 1 Introduction With the ever increasing role of computerized machines in society, Human Computer Interac- tion (HCI) system has become an increasingly important part of our daily lives. HCI determines the e ective utilization of the available information ow of the computing, communication, and display technologies. In recent years, there has been a tremendous interest in introducing intu- itive interfaces that can recognize the user's body movements and translate them into machine commands. For the neural linkage with computers, various biomedical signals (bio signals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Elec- tromyogram (EMG).Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various directions including the development of continuous EMG signal classi cation for graphical controller, that enables the physically disabled to use word processing programs and other personal computer software, internet. It also enable manipulation of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipments etc. The EMG controller can be programmed to perform gesture recognition based on signal analysis of groups of muscles action potential. Many physically disabled individuals are deterred from using computers due to their inability to utilize a hand-controlled mouse. The EMG signals generated in the human body can be used to implement the mouse functions like click, drag, scroll etc. In fact, an input device developed using EMGs is a natural means of HCI because the electrical activity induced by the human's arm muscle movements can be interpreted and transformed into computer's control commands. Furthermore, EMGs can be easily acquired on the surface of human skin through conveniently attachable electrodes.EMG signals can be measured more conveniently and safely than other neural signals. Furthermore, this non invasive monitoring method produces a good signal to noise ratio. Hence, an EMG-based HCI is the most practical with current technology. EMG measures electrical currents that are generated in a muscle during its contraction and represent neuromuscular activities. EMG signals can be used for a variety of applications including clinical applications, HCI and interactive computer gaming. Moreover, EMG can be used to sense isometric muscular activity which does not translate into movement. This makes it possible to classify subtle motionless gestures and to control interfaces without being noticed and without disrupting the surrounding environment. On the other hand, one of the main di culties in analyzing the EMG signal is due to its noisy characteristics. Compared to other bio signals, EMG contains complicated types of noise that are caused by, for example, inherent equipment noise, electromagnetic radiation, motion artifacts, and the interaction of di erent tissues. Hence, pre-processing is needed to lter out the unwanted noises in EMG. 2 Methodology 2.1 Gesture selection For controlling a mouse, four di erent gestures are required. These gestures should be equally easy to perform and form patterns in the EMG signal which are as discriminative as possible. Before the start and after the nish of each gesture, the hand should be situated in a posture called the home position, in which hardly any signal amplitude occurs. The rst gesture, hold (gesture 1), is a short and very light pressing of the st. Gesture Left (gesture 2) is performed by a quick left motion of the wrist towards the inside of the forearm, followed by a straight return into the home position. The motion directed towards the outside of the arm is called gesture Right (gesture 3) and is supposed to be slow and smooth. A st should be formed in this case. The fourth gesture, Circling (gesture 4), is a smooth circling movement of the wrist starting with a swinging movement into the direction of the inside arm. Depending on the way the executive user performs the motion, either a single or two consecutive circle movements are possible. For the home position, a loose, relaxed st has been proven to be the best suited position. The choice of the performing hand is not an issue since the experiments showed that both hands generate extremely similar EMG waveforms, except for unremarkable di erences in over- all amplitude. As most users opt for the right hand, the names and descriptions of the gestures refer to the right hand and have to be mirror-inverted for a left handed usage. 3 System structure In this section the single processing steps of the system, from the recording of a gesture to mouse click control, are described. An overview is given in Figure 2. Figure 2.General procedure of a biosignal based recognition system. The EMG signal of the performing arm muscles is detected by electrodes connected to a sensor. In order to qualify the incoming raw signal for further processing the signal is prepro- cessed rst. Next, the incoming patterns, which represent a gesture movement in the signal, are matched. To be able to distinguish patterns, the signi cant features of each pattern are rst extracted. The resulting feature vector is used for the classi cation of the movement order. 3.1 Signal acquisition For recording of the EMG signal, only one pair of pre-gelled single Ag/AgCl electrodes was xed on the skin of the system user's inside forearm (Fig. 3). Usually, each pair of electrodes is used to examine mainly one single muscle. Signal interferences of adjacent muscles, known as crosstalk, are normally undesirable. Since we designed like only one channel sensor for signal acquisition it was necessary to examine several muscles simultaneously with one pair of electrodes. These observed muscles were mainly the exor carpi radialis and the palmaris longus, both of which are responsible for wrist movements, as well as the exor digitorum super cialis, which is used for nger movements. All three electrodes are situated in a line in the middle of the forearm parallel to the length of the forearm muscle bers. By placing the rst electrode near the wrist, it is possible to examine the muscles of the forearm between their tendon insertions and their motor points, which seems to be the best location for a constant measurement. The reference electrode is placed in the middle. The sampling rate of the EMG signal in the system was set at 125 samples per second. Figure 3. Position of the electrodes according to the muscle structure 3.2 Preprocessing Fortunately, in this project, the common noises found in EMG signals, such as inherent equip- ment noise, electromagnetic radiation, or moving artifacts, are hard to nd. However there will be another problem hindering the raw signal from being subsequently processed. The incoming signal will be extremely unstable baseline which is di cult to calculate reasonable values for statistical and frequency features. As a consequence, the signal needs to be preprocessed rst to be suitable for further processing. For this purpose, the raw signal values x should be detrended with the following simple detrending function D: 3.3 Pattern extraction To capture only relevant patterns in EMG waveforms, we need to design an adaptive thresh- olding. Although the system should provide a default setting for the concrete threshold values which normally provides comparatively accurate pattern boundaries, the best results are to be achieved when the threshold values are individually adjusted to the user of the system. This is due to the fact that the EMG signal of each person is slightly di erent. An incoming pre- processed value is marked as the beginning of a pattern if a certain de ned threshold value is reached. This boundary is found to be quite unproblematic, as there exists a considerable di erence in the signal amplitude between staying in the home position and starting to perform a gesture. The upper bound will be limited by the Press gesture which generally generates the lowest amplitude and absolutely needed to be identi ed, while the lower bound will be limited by noise, for example resulting from small unintended movements which must not be recognized as patterns. Detecting the ending of a pattern is more complicated. Here, a trade-o between two di erent problems is to be found: First, the ending boundary should not be too high, otherwise the pattern might not be caught completely. Second, the ending boundary should not be too low, otherwise the end of a pattern might be detected much too late. A further complication may results from the fact that some gestures can cause a reverberation e ect in the signal amplitude, long after the motion had already stopped. If, because of the characteristic zigzag form of an EMG signal, the examination of an incoming value does not make any sense, a couple of consecutive values have to be observed. The best results for the detection of a pattern ending in the system were achieved by observing the root mean square (RMS) of the last 16 incoming values. If this RMS value fell twice in a row below two (possibly di erent) boundary values, it was considered as being the end of a pattern. 3.4 Feature extraction To classify a performed gesture some distinctive features have to be found and taken from each matched pattern [19, 20]. Therefore several features were extracted, including common statistical features like maximum, minimum, mean value, variance, signal length and root mean square.In the frequency domain obtained by using typical fast Fourier transform (FFT), we can calculate fundamental frequency (F0) and Fourier variance of the spectrum. Given the spectrum of signal it can also be extracted the region length which is de ned as a partial length of the spectrum containing greater magnitude than the mean value of total Fourier coe cients. This feature should be an indicator for how periodic a signal is: the smaller the region the more periodic the signal. In the cases where there is more than one region in the spectrum, the lengths of these regions are added. First, the positions of the maximum and the minimum which are de ned by the relative position (as a percentage) of the max. and min. |