Human Hand Prosthesis Based On Surface EMG Signals for Lower Arm Amputees

Transcription

1 Human Hand Prosthesis Based On Surface EMG Signals for Lower Arm Amputees J. Senthil Kumar 1, M. Bharath Kannan 2, S. Sankaranarayanan 3, A. Venkata Krishnan 4 1 Assistant Professor, 2, 3, 4 Final Year Students, Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu Abstract The main aim of the project is to create a cost effective prosthetic arm controlled by the surface EMG signals which would facilitate the differently-abled with arms that they would love to get. To serve differently abled fellow beings by harnessing technology is our motto. The designed prosthesis is based on the real-time learning method and the proposed real-time scheme consists of four basic units. They are signal acquisition and pre-processing unit, feature vector extraction Unit, Pattern classifier unit and the real-time trainer unit. The semg signals which originate from the residual muscles have low amplitude of 100 μv for moderately contracted muscles. The semg signals are amplified and then band limited to bring it in the amplitude range of 1-10v, the signals are finally rectified to convert it into a dc signal. The process of acquisition and processing is done by the preprocessing unit. The Feature vector unit processes the signal and the feature vectors are extracted. Once the feature vector set is formed, it is fed to the pattern classifier unit, where the neural network is employed for the non-linear mapping of the feature vector to a control command. For learning the user characteristics, neural network uses the back propagation algorithm. In order to get the exact mappings, the weights of the neural networks are modified by real-time trainer unit which employs real-time learning. The process is repeated until the control command generated is similar to the teacher vector from the user or the RMS value of the error is reduced below the threshold value. Once the neural network is trained, it outputs the control command; normally binary values are used to control the Prosthesis. The arm is driven through a set of motors (either stepper or servo motors). The control commands are fed from the processing unit which controls the motors which in-turn controls the movement of the arm. Our design of prosthetic arm has one degree of freedom and hence two actions can be carried out. The former grabbing an object and the latter releasing the object. The clockwise rotation of the motor will perform an action and the anticlockwise rotation will perform the second action. In this work, one degree of freedom is considered. This design will serve the amputees and the same design can be extended to higher degrees of freedom similar to the human hand. Keywords Prosthetic arm, Real-time learning method, human hand prosthesis, EMG signal processing, Electromyography, Myoelectric prosthetic arm. I. INTRODUCTION In order to give rehabilitation to amputees, Prosthesis is one of the best solutions. Prosthesis means the replacement of a missing body part with an artificial substitute. It consists of mechanical parts and a processor to control it. The controller processes the semg signals from the residual muscles and controls the movement of the mechanical parts by employing real-time learning. The prosthesis is designed with basic movement and grabbing features, the design has been done with care so that it can be extended to other hand-movements. II. ELECTROMYOGRAPHY Electromyography (EMG) deals with the detection, analysis and utilization of electrical signals which originates from skeletal muscles. Electromyography is studied in the field of Biomedical Engineering and Bio mechatronics deals with the prosthesis using electromyography. Myoelectric signal is a kind of electric signal produced during muscle activation. It is produced due to small electrical currents which are generated by the exchange of ions within the muscle membranes. The EMG signal is detected with the help of electrodes. Electrical activity produced by the muscles of the human body can be evaluated using Electromyography. Electromyography is the instrument which is used to obtain the EMG signal and the resultant record which obtained is known as electromyogram. The functioning of human body is an fascinating and intriguing activity. Perfect integration of the brain, nervous system and muscles is required for the motion of the human body. It is a well-organized effort of the brain and 28 major muscles to control the joints in the limb to create forces needed for counter gravity actions and propel the body using a minimum amount of energy. In the recent times, EMG uses robotic prosthesis for rehabilitation of patients with amputations. EMG is a valuable tool because of its natural way of sensing and classifying different movements of the muscles. Imitation of the motion of the human limb is effectively done using multi- degree of freedom robotic mechanism. 199

2 Advancement in electronics and microprocessor technology has made multiple control options for robotic mechanisms. III. EMG ELECTRODES AND ITS PLACEMENT EMG electrodes are used to detect the bioelectrical activates inside the muscles of the human body. The surface EMG electrodes provide a non-invasive technique for the detection and measurement of EMG signal. The EMG electrodes form a chemical equilibrium between the skin and the detecting surface by means of electrolytic conduction, so that current can flow into the electrode. Moreover Surface EMG electrodes are widely used to detect the muscle activity in order to control device to achieve prosthesis for physically disabled and amputated population. Proper skin preparation is required for the application of surface EMG electrodes. Skin impedance must be considerably reduced in order to obtain a good quality EMG signal. The dead cells on the skin e.g. hair must be completely removed from the location where the EMG electrodes are to be placed. The moisture level of the skin must be very low. Wetness or sweat on the skin can be eliminated by treating with alcohol. Two detecting surfaces (or EMG electrodes) are placed in bipolar configuration on the skin. The distance between the centers of the electrodes should only be cm. The longitudinal axis of the electrodes (which connects the detecting surfaces) should be in parallel to the length of the muscle fibers. The electrodes placed on the muscle have proved to be a more than on e acceptable location. Here, the target muscle fiber density is the highest. When the electrodes are arranged in this way, the detecting surfaces intersect most of the same muscle fibers, and as a result, an improved signal is observed. A reference point is used detect the EMG signal. An EMG electrode is used as reference. It acts as a ground for the EMG signal. It should be placed on an electrically neutral tissue far from the EMG detection surface. IV. PROPOSED EMG PATTERN CLASSIFICATION FOR HAND MOVEMENTS REAL TIME SCHEME EMG is a set of complicated signals influenced by various anatomical and physiological properties, differing for each and every person. Its characteristics were learned in an offline manner. It is not possible to vary the inner states correspond to the amputee s variation of the hand movements by the offline approach. Real-time training methods were introduced, to eliminate the dependence on individual subjects and to decrease effects of offline approach on EMG pattern recognition. 200 Due to the bulkier structure and its crude gripping functionality true operation as prosthesis hands cannot be obtained. To overcome these shortcomings, the degree of freedom of the prosthesis is reduced. The block diagram of the proposed real-time methodology is shown in Fig 1. Fig 1: Block diagram of the proposed real-time learning methodology A. Signal Acquisition and Pre-processing Unit: The signal acquisition unit is responsible for acquiring semg signals from the residual muscles of the amputees. This unit comprises of an instrumentation amplifier for signal amplification since the captured the signal is in the order of several microvolts, a high pass filter and a low pass filter to remove the artifacts in the semg signal. Many factors must be concerned in the proper detection of the EMG signal. The signal is accurately extracted only when the electrode is properly. The extracted EMG signal is hampered by the noise. In order to reduce the effect of the noise, the signal must be filtered even after the differential amplification. Both the high and low frequency noise contaminates the raw EMG signal. DC offsets, sensor drift on skin and temperature fluctuations causes the low frequency noise. Low frequency noise can be removed using the High pass filter. Nerve conduction and high frequency interference from radio broadcasts, computers, cellular phones, etc. causes the high frequency noise. High frequency noise can be deleted using a low pass filter. The process of differential amplification has enabled the measurement of EMG signals of low noise and high signal fidelity (i.e. high signal to noise ratio). Typical band pass frequency ranges are from between 10 and 20Hz (high pass filtering) to between 500 and 1000Hz (low-pass filtering). High-pass filtering is necessary because movement artifacts are comprised of low frequency components (typically <10Hz). Low pass filtering is desirable to remove highfrequency components to avoid signal aliasing. In the past, it was common to remove power-line (A/C) noise components (i.e. either 50 or 60Hz) by using a sharp notch filter. There are problems with notch filtering because EMG has large signal contributions at these and neighbouring frequencies. The result of notch filtering is the loss of important EMG signal information, so notch filtering should be avoided as a general rule.

3 B. Feature Extraction Unit Accurate feature extraction from EMG signals is the main requirement of classification system in both real-time and offline systems and is essential to the motion command identification. The non-stationary nature of semg signal makes it difficult to precisely extract feature parameters with stationary models like autoregressive (AR) model with the assumption EMG signal of small time period is stationary and linear. Time-frequency features present better results in EMG pattern recognition applications. This is due to the effect of combining time domain and frequency analyses which yields a potentially more revealing picture of the temporal localization of a signal's spectral characteristics. [1] In this work, a time domain window of 500ms is used for collecting semg signal. C. Pattern classifier unit EMG prosthetic hand controllers must generate the control commands from the extracted feature vector set. In other words, the controllers must learn operator s characteristics to output the control command adequately using nonlinear functions because some person s feature vectors differ from another person s and the mapping relationships are not linear. The detection of different predefined hand motions (left, right, up and down) can be done using artificial neural network (ANN). An artificial neural network (ANN) is a computational system inspired by the learning characteristics and structure of biological neural networks. A back propagation (BP) algorithm has been utilized for the classification of EMG signals. Sl(k) [2] Where k=0,1,..,n-1 Among all different types of TFR, short -time Fourier transform (STFT), are preferable, which are too computationally intense for real-time application. STFT is also known as Gabor transform. Gabor transform is a Fast Fourier transform with Gaussian as a window function. A Gaussian window allows a balanced time resolution and frequency resolution. Because of this Gaussian window is normally used for real time application. Now the generated spectra are very high dimension vector. ) [5] Too large number of the dimension of the feature vector causes space state explosion and too high sensitivity causes incorrect discrimination. It causes problems in the learning. Reduction of sensitivity is performed by interpolation and the reduction of the dimension is performed with the extraction from the generated vector. The constructed feature is fed to a classifier to discriminate the hand motions. To overcome the loss of temporal information of the signal and poor classification performance, segmentation is done on semg signal and desirable features are extracted from each segment. [3] [4] 201 ) [7] Where m=2,3 & i=1,2,.., The application of ANN could reduce the amount of user training. Moreover, a simple feed-forward neural network can assure high recognition rates. ANNs possess several attractive features that make them suitable for difficult signal processing problems. One of the simplest and most widely used ANN is the multilayer perceptron s (MPLP), which is characterized by a set of input units, a layer of output units, and a number of hidden layers (one or two, in general). Each input node is connected to each unit in the hidden layer. The connections between units have an associated weight W; each unit of the hidden layer is connected to the neurons in the following layer, be it hidden or output, in a similar way. Hidden and output nodes, however, have a transfer function. [9] [6] [8] [10] Many algorithms have been developed to train the network i.e., to adapt the network weights. In Back Propagation algorithm, the network is first initialized by setting up all its weights to be small random numbers say between 1 and +1. Next, the input pattern is applied and the output calculated (this is called the forward pass).

4 The calculation gives an output which is completely different to what you want (the Target), since all the weights are random. The error of each neuron is calculated, which is essentially: Target - Actual Output (i.e. what you want What you actually get). This error is then used mathematically to change the weights in such a way that the error will get smaller. In other words, the Output of each neuron will get closer to its Target (this part is called the reverse pass). The process is repeated again and again until the error is minimal. D. Real-time Trainer Unit EMG signal patterns differ among individuals. Moreover, electrical impedance of the skin; electrode locations; time variations caused by fatigue, sweat, and so on differ from user to user and from time to time. It is clear that the EMG processing unit should adapt itself to these changes in order to minimize ill-discriminations. The device should learn how the user behaves and adjust its internal parameters relative to the operator s variation in real time. There are three major learning paradigms, each corresponding to a particular abstract learning task. These are supervised learning, unsupervised learning and reinforcement learning. In supervised learning, a set of example pairs are given and the aim is to find a function in the allowed class of functions that matches the examples. In this prosthesis design we used supervised learning technique. A commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network's output, and the target value. Supervised learning is pattern recognition (also known as classification) and regression (also known as function approximation). [11] The reduced feature set (p) with teaching data (t i ) in the form of (p i, t i ) are created by the real-time trainer and sends this teaching vector to pattern classifier unit. From the generated EMG signal, the real time trainer unit updates the state of pattern classifier unit in the interval that demands control command. Until the root mean square (RMS) comparison is within the acceptable range, this process is continued. [13] V. PROPOSED PROSTHETIC ARM The proposed and fabricated Prosthetic arm is shown in Fig 2. The proposed model consists of a DC gear motor which is used to control the fingers movement. When the fingers have to be flexed, the appropriate surface EMG signals are acquired from the muscle activity and they are mapped to the appropriate control commands. Once the commands are issued, the motor rotates in such a way that it causes the flexion of fingers. Similarly during the flexion of the fingers, the same occurs, but the motor rotates in the opposite direction which extends the fingers. The extension is achieved more appropriately with the spring actions. Similarly wrist actions can be interpreted as similar as the finder flexion and extension. [12] Operator s evaluation system performance is required for implementing real-time learning scheme in the pattern recognition applications. Consideration of the time as a cost function in the offline methods is not needed. The real-time method must be capable of evaluating the accuracy of system to recognize hand movement that is performed by the amputee. The Real time trainer unit develops the training data. The training data contains a reference signal from the amputee and reduced using the reduction unit. The training data is fed to the pattern recognition unit. While the real time trainer unit receives the reference signal from the operator, it creates the vector as, where Q is a desired hand movement and {Q = 1, 2...n} for n total hand motions. 202 Fig 2: FABRICATED PROSTHETIC ARM VI. CONCLUSION AND FUTURESCOPE The proposed model employs indigenously developed design methods to bliss the amputees with arms that they would love to get. As the product is user-independent there will not any need to have separate sessions with amputees to tune the prosthesis to them, amputees can buy it as a gadget and use it.

5 A main feature of the proposed model is that it is highly affordable and the cost can be highly brought down on mass-production. The designed prosthetic arm is having only one degree of freedom. That it is able to do only one action (grabbing action). Moreover it uses a single channel EMG processing Unit which is capable of capturing the EMG signals from the muscles which are responsible of opening and closing of hand. It is possible to have individual finger movements with the help of multi-channel EMG processing unit. Beside used for prosthetic application, EMG signal processing can also be used to analyze the muscle activity of a human. REFERENCES [1] Carlo J. De Luca (2006) Electromyography: Encyclopedia of Medical Devices and Instrumentation (John G. Webster Ed.), John Wiley Publisher. [2] Nuria Masso, Ferran Rey, Dani Romero, Gabriel Gual, Lluis Costa and Ana German (2010) Surface Electromyography and Applications in Sport Apunts Medicina De L Esport, Vol. 45: [3] Dr. Scott Day Important Factors in Surface EMG Measurement, Bortec Biomedical Incorporated [4] Bjorn Gerdle, Stefan Karlsson, Scott Day and Mats Djupsjobacka (1999) Acquisition, Processing and Analysis of the Surface Electromyogram. In: U. Windhorst, H. Johansson, editors. "Modern Techniques in Neuroscience Research", Springer. [5] Carlo J. De Luca (2002) Surface Electromyography: Detection and Recording, Delsys Incorporated. [6] Gianluca De Luca (2001) Fundamental Concepts in EMG Signal Acquisition, Delsys Incorporated. [7] P.R.S. Sanches, A.F. Müller, L. Carro, A.A. Susin, P. Nohama (2007) Analog Reconfigurable Technologies for EMG Signal Processing Journal of Biomedical Engineering, Vol. 23, pp [8] S.L. Pullman, D.S. Goodin, A.I. Marquinez, S. Tabbal and M. Rubin (2000) Clinical Utility of Surface EMG Report of the Therapeutics and Technology Assessment, Subcommittee of the American Academy of Neurology, Vol. 55: [9] Zecca M, Micera S, Carrozza MC, Dario P: Control of Multifunctional Prosthetic Hands by Processing the Electromyography Signal. Critical Reviews in Biomedical Engineering 2002, 30(4 6): [10] Hudgins B, Parker P, Scott RN: A new strategy for multifunction myoelectric control. IEEE Tran Biomed Eng 1993, 40(1): [11] Englehart K, Hudgins B: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 2003, 50(7): [12] Englehart K, Hudgins B, Parker PA: A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 2001, 48(3): [13] Graupe D, Salahi J, and Kohn KH: Multifunction prosthesis and orthosis control via micro-computer identification of temporal pattern differences in single-site myoelectric signals. J Biomed Eng 1982, 4: