Design an System for Hand Gesture Recognition with Emg Signal by Neural Network (original) (raw)
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Design a System for Hand Gesture Recognition with Neural Network
The intellectual computing of an effective humancomputer interaction (HCI) or human alternative and augmentative communication (HAAC) is vital in our lives in today's technological environment. One of the most essential approaches for developing a gesture-based interface system for HCI or HAAC applications is hand gesture recognition. As a result, in order to create an advanced hand gesture recognition system with successful applications, it is required to establish an appropriate gesture recognition technique. Human activity and gesture detection are crucial components of the rapidly expanding area of ambient intelligence, which includes applications such as robots, smart homes, assistive systems, virtual reality, and so on. We proposed a method for recognizing hand movements using surface electromyography based on an ANN. The CapgMyo dataset based on the Myo wristband (an eight-channel sEMG device) is utilized to assess participants' forearm sEMG signals in our technique. The original sEMG signal is preprocessed to remove noise and detect muscle activity areas, then signals are subjected to time and frequency-based domain feature extraction. We used an ANN classification model to predict various gesture output classes for categorization. Finally, we put the suggested model to the test to see if it could recognize these movements, and it did so with an accuracy of 87.32 percent.
Development of Machine Learning Models to Determine Hand Gestures using EMG Signals
DAAAM Proceedings, 2020
The connection between the human body and science is still growing exponentially. The human body has many mysteries. The more we learn about them, the better we improve our scientific perspectives. In this case, the analysis of EMG (Electromyographic) signals gives the possibility the use the EMG data to perform classification tasks. Machine Learning models and Neural Networks are the best tools to classify different hand gestures using the dataset. This work aims to analyse the characteristics of EMG signals and use the EMG dataset to perform different ML models. The results will be used in robotic fields and control systems as future work. In this project, the Python programming language is used. The dataset was recorded using an MYO Thalmic bracelet. The number of instances is about 40000-50000 recordings in each column (channels). There are six different hand gestures tasks recorded in the dataset that are; hand clenched in a fist, wrist extension, wrist flexion, hand radial deviations, hand ulnar deviations, hand extended palm. The study of ML models and using gestures to control robotic devices could be useful in industrial spheres. A person may use a forearm bracelet to use it in industrial operations. Another purpose of this paper is; helping people who lost their hands. With the help of robotic arm plugged-in to their forearm and EMG device, they might be able to perform several hand-gestures.
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.
Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures
This paper presents an electromyography (EMG) signal dataset for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial deviation of the wrist, grip, abduction of all fingers, adduction of all fingers, supination, and pronation. Data were collected from 4 forearm muscles when simulating 10 unique hand gestures and recorded with the BIOPAC MP36 device using Ag/AgCl surface bipolar electrodes. Each participant's data contains five repetitive cycles of ten hand gestures. A demographic survey was applied to the participants before the signal recording process. This data can be utilized for recognition, classification, and prediction studies in order to develop EMG-based hand movement controller systems. The dataset can also be useful as a reference to create an artificial intelligence model (especially a deep learning model) to detect gesture-related EMG signals. Additionally, it is encouraged to * Corresponding author.
Real-Time Hand Gesture Recognition Based on Artificial Feed-Forward Neural Networks and EMG
2018 26th European Signal Processing Conference (EUSIPCO), 2018
In this paper, we propose a real-time hand gesture recognition model. This model is based on both a shallow feedforward neural network with 3 layers and an electromyography (EMG) of the forearm. The structure of the proposed model is composed of 5 modules: data acquisition using the commercial device Myo armband and a sliding window approach, preprocessing, automatic feature extraction, classification, and postprocessing. The proposed model has an accuracy of 90.1% at recognizing 5 categories of gestures (fist, wave-in, wave-out, open, and pinch), and an average time response of 11 ms in a personal computer. The main contributions of this work include (1) a hand gesture recognition model that responds quickly and with relative good accuracy, (2) an automatic method for feature extraction from time series of varying length, and (3) the code and the dataset used for this work, which are made publicly available.
Classification of EMG signal for multiple hand gestures based on neural network
Indonesian Journal of Electrical Engineering and Computer Science, 2020
This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.
Applied Sciences, 2020
Hand gesture recognition systems have several applications including medicine and engineering. A gesture recognition system should identify the class, time, and duration of a gesture executed by a user. Gesture recognition systems based on electromyographies (EMGs) produce good results when the EMG sensor is placed on the same orientation for training and testing. However, when the orientation of the sensor changes between training and testing, which is very common in practice, the classification and recognition accuracies degrade significantly. In this work, we propose a system for recognizing, in real time, five gestures of the right hand. These gestures are the same ones recognized by the proprietary system of the Myo armband. The proposed system is based on the use of a shallow artificial feed-forward neural network. This network takes as input the covariances between the channels of an EMG and the result of a bag of five functions applied to each channel of an EMG. To correct t...
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.
Neural Network Classifier for Hand Motion Detection from EMG Signal
2010
EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. Additionally, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This paper represents the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). A backpropagation (BP) network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The conventional and most effective time and timefrequency based feature set is utilized for the training of neural network. The obtained results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%. Furthermore, when the trained network tested on unknown data set, it successfully identify the movement types.
ICONIET PROCEEDING
The goal of this research is to detect Surface Electromyography (SEMG) signal froma person’s arm using Myo Armband and classify his / her performed finger ges-tures based onthe corresponding signal. Artificial Neural Network (based on the machine learning approach)and Principal Component Analysis (based on the feature extraction approach) with and withoutFast Fourier Transform (FFT) were selected as the methods utilized in this research. Analysisresults show that ANN has achieved 62.14% gesture classifying accuracy, while PCA withoutFFT has achieved 30.43% and PCA without FFT has achieved 48.15% accuracy. The threeclassifiers are tested using SEMG data from a set of six recorded custom gestures. Thecomparison results show that the ANN classifier shows higher classifying accuracy and morerobust rather than the PCA classifier’s classi-fying accuracy. Therefore, ANN classifier is moresuited to be implemented in classifying SEMG signals as hand gestures.