Prosthetic hand movement using artificial neural network (original) (raw)
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EMG (Electromyography) is a biological signal derived from the summation of electrical signals produced by muscular actions. This EMG can be integrated with external hardware and control prosthetics in rehabilitation. Pattern recognition plays an important role in developing myo-electric control based interfaces with prosthetics and Artificial Neural Networks (ANN) are widely used for such tasks. The main purpose of this paper is to classify different predefined hand gestured EMG signals (wrist up and finger flexion) using ANN and to compare the performances of four different Back propagation training algorithms used to train the network. The EMG patterns are extracted from the signals for each movement and then ANN is utilized to classify the EMG signals based on their features. The four different training algorithms used were SCG, LM, GD and GDM with different number of hidden layers and neurons. The ANNs were trained with those algorithms using the available experimental data as the training set. It was found that LM outperformed the others in giving the best performance within short time elapse. This classification can further be used to control devices based on EMG pattern recognition.
Identification of motion from multi-channel EMG signals for control of prosthetic hand
Australasian physical & engineering sciences in medicine / supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine, 2011
The authors in this paper propose an effective and efficient pattern recognition technique from four channel electromyogram (EMG) signals for control of multifunction prosthetic hand. Time domain features such as mean absolute value, number of zero crossings, number of slope sign changes and waveform length are considered for pattern recognition. The patterns are classified using simple logistic regression (SLR) technique and decision tree (DT) using J48 algorithm. In this study six specific hand and wrist motions are identified from the EMG signals obtained from ten different able-bodied. By considering relevant dominant features for pattern recognition, the processing time as well as memory space of the SLR and DT classifiers is found to be less in comparison with neural network (NN), k-nearest neighbour model 1 (kNN-Model-1), k-nearest neighbour model 2 (kNN-Model-2) and linear discriminant analysis. The classification accuracy of SLR classifier is found to be 91 ± 1.9%.
Development of an Algorithm for the EMG Control of Prosthetic Hand
Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing, 1139, chapter 15, Springer, 2019
This work presents the development of a new algorithm for the control of robotic and prosthetic hands: the proposed architecture is made of an EMG wearable sensor and a personalized Graphical User Interface (GUI). The proposed system inherits and processes eight EMG signals which are locally amplified and rectified within the wearable device: then a signal classifier allows piloting a 2 degree of freedom cursor on the GUI in order to reach a provided target in the Cartesian space. The aim of this study is to finally provide a user-friendly interface for training human subjects on reaching movements with the EMG signals of their forearm muscles. This approach as a twofold objectives: (1) to maintain, train and support the muscular tone and (2) to provide an interface for the physiotherapy and preparation of prosthetic use in daily life.
An implementation of movement classification for prosthesis control using custom-made EMG system
Serbian Journal of Electrical Engineering, 2017
Electromyography (EMG) is a well known technique used for recording electrical activity produced by human muscles. In the last few decades, EMG signals are used as a control input for prosthetic hands. There are several multifunctional myoelectric prosthetic hands for amputees on the market, but so forth, none of these devices permits the natural control of more than two degrees of freedom. In this paper we present our implementation of the pattern classification using custom made components (electrodes and an embedded EMG amplifier). The components were evaluated in offline and online tests, in able bodied as well as amputee subjects. This type of control is based on computing the time domain features of the EMG signals recorded from the forearm and using these features as input for a Linear Discriminant Analysis (LDA) classifier estimating the intention of the prosthetic user.
Implementation of a neural network of low computational cost for its application in arm prostheses
Revista de ingenieria tecnológica, 2022
A prostheses implementation represents a design challenge in its different stages. The control systems and the total system cost play a very important role. In this work, a control proposal is presented using artificial neural networks (ANN) for pattern recognition using electromyographic (EMG) signals, which are obtained from the arm muscle (biceps). A single channel EMG surface sensor is used to acquire the EMG signals and by means of adjacent windows the feature extraction is carried out in order to reduce the input values to the neural network. The neural network is trained with the features extracted from the EMG signals, using a method of muscle tension thresholds for activation and a labeling technique for the output called One Hot Encode. The resulting ANN was embedded in a low-cost microcontroller and an accuracy of approximately 93% was achieved.
Hand Motion Recognition from EMG using Artificial Neural Network
2019
Hand motion recognition has become an active research due to its numerous applications such as its use in human-computer interface. The motivation for this work is to help the disabled people by improving their quality of life. This paper aims to recognize and replicate four hand gestures fist, spread, wave in, and wave out on 3D printed prosthetic hand. Electromyography (EMG) signals are recorded for these gestures using Myo armband consisting of eight electrodes from which five statistical parameters of EMG signals are extracted and employed for classification. These parameters for each electrode accumulate to form feature vector inserted to Artificial Neural Network (ANN) which classifies it into its target classes (gestures). The performance of ANN classifier is assessed over Scaled Conjugate Gradient (SCG) in comparison of different algorithms. Our simulation results are also supported with experimental results run over 3D printed prosthetic hand.
A Method for Controlling of Hand Prosthesis Based on Neural Network
akademik.unsri.ac.id
The people are differed by their capabilities, skills and mental agilities. The evolution of human from childhood when they are completely dependent up to adultness the time they gradually set the dependency free is too complicated, by considering they have all started from almost one point but some become cleverer and some less.
Implementation of Prosthetic Hand through EMG Signals
Prosthetic device replicate the function of original human anatomy hand and is considered as a useful invention after facing many challenges. Electromyography (EMG) is detection of electrical potential or signals according to the contraction or expansion of Muscles. Contraction/Expansion of muscle generates the electrical potential of few micro volts. EMG controlled prosthetic hand are hardly ever available in Pakistan. The reason of this unavailability is its high price and lack of research. This paper presents the implementation of EMG controlled prosthetic hand. The purpose of this research is to design a low cost multifunctional microcontroller based EMG prosthetic hand. Functional prototype is working according to the design. More research work should be supported and carried out, if this prototype has to be launched commercially.
Journal of Intelligent Information Systems, 2003
One of the major difficulties faced by those who are fitted with prosthetic devices is the great mental effort needed during the first stages of training. When working with myoelectric prosthesis, that effort increases dramatically. In this sense, the authors decided to devise a mechanism to help patients during the learning stages, without actually having to wear the prosthesis. The system is based on a real hardware and software for detecting and processing electromyografic (EMG) signals. The association of autoregressive (AR) models and a neural network is used for EMG pattern discrimination. The outputs of the neural network are then used to control the movements of a virtual prosthesis that mimics what the real limb should be doing. This strategy resulted in rates of success of 100% when discriminating EMG signals collected from the upper arm muscle groups. The results show a very easy-to-use system that can greatly reduce the duration of the training stages.
EMG pattern recognition by neural networks for multi fingers control
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1992
The cybernetic interface through which users can communicate with computers "as we may think" is the dream of human-computer interactions_ Aiming at interfaces where machines adapt themselves to users' intention instead of users' adaptation to machines, we have been applying neural networks to realize electromyographic(EMG)-controlled prosthetic members-a historical heritage of the cybernetics. This paper proposes that EMG patterns can be analyzed and classified by neural networks. Through experiments and simulations, it is demonstrated that recognition of not only finger movement and torque but also joint angles in dynamic finger movement, based on EMG patterns, can be successfully accomplished.