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A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition

Yu Hu, Yongkang Wong, Wentao Wei, Yu Du, Mohan Kankanhalli, Weidong Geng, Huiguang He
2018 PLoS ONE  
sparse multi-channel electromyogram signal.  ...  Motivated by the sequential nature of electromyogram signal, we propose an attention-based hybrid CNN and RNN (CNN-RNN) architecture to better capture temporal properties of electromyogram signal for gesture  ...  Motivated by the idea of signal image [37], we present a new sEMG image representation method based on a feature vector for sparse multi-channel electromyogram signals.  ... 
doi:10.1371/journal.pone.0206049 fatcat:agbipjsujjdjric63h57lsueha

Hand Motion Recognition from Single Channel Surface EMG Using Wavelet & Artificial Neural Network

S.M. Mane, R.A. Kambli, F.S. Kazi, N.M. Singh
2015 Procedia Computer Science  
Analysis of surface Electromyogram (sEMG) is one of the standard procedures to identify posture, gesture and actions (i.e. control of prosthesis via learnt body actions). sEMG signals are usually complex  ...  The strength of the muscle contraction can be easily measured by the muscle activity extracted at the skin surface.  ...  Experiment Four volunteers (three males and one female) helped in taking readings of EMG signal from hand movement.  ... 
doi:10.1016/j.procs.2015.04.227 fatcat:sg4i6oo2frbzvfehsqtvonawaa

Research on EMG-based Classification of Hand Movements using Four Electrodes Arrangements on Forearm

Yusuke Nagata, National Institute of Technology, Kagoshima College, 899-5193 Kirishima-shi, Kagoshima, JAPAN, Souichirou Nagano, Kiyotaka Kamata, Yozo Tamari, Nariaki Imamura, Teruji Ide, National Institute of Technology, Kagoshima College, 899-5193 Kirishima-shi, Kagoshima, JAPAN, National Institute of Technology, Kagoshima College, 899-5193 Kirishima-shi, Kagoshima, JAPAN, National Institute of Technology, Kagoshima College, 899-5193 Kirishima-shi, Kagoshima, JAPAN, National Institute of Technology, Kagoshima College, 899-5193 Kirishima-shi, Kagoshima, JAPAN, National Institute of Technology, Kagoshima College, 899-5193 Kirishima-shi, Kagoshima, JAPAN
2022 International Journal of Integrated Engineering  
Although various types of myoelectric prosthetic hands using surface electromyogram have been developed, the placement of electrodes to acquire myoelectric potential needs to be adjusted by a specialist  ...  In this study, we report the effects of muscle potential measurements on the pattern discriminator and the estimation of the useful measurement points by fixing four electrodes arranged in the form of  ...  In this study, the parameters of , C  were decided using a trial and error fashion. There are "One-Versus-Rest" and "One-Versus-One" in the multi-class classification methods.  ... 
doi:10.30880/ijie.2022.14.02.004 fatcat:6chdzhgjzvfhzgbzn3d74m3uku

Classification of hand gestures from forearm electromyogram signatures from support vector machine

Diaa Albitar, R. Jailani, Megat Syahirul Amin Megat Ali, Anwar P. P. Abdul Majeed
2021 Indonesian Journal of Electrical Engineering and Computer Science  
A hand gesture classification model based on electromyogram signal has been successfully developed using support vector machine with overall accuracy of 97.4% for training, and 88.0% for testing.</p>  ...  Initially, a total of 248 time-and frequency-domain features are extracted from the eightchannel device. Neighborhood component analysis has reduced them to a total of fourteen features.  ...  ACKNOWLEDGEMENTS This work is funded in part by the Institute of Research Management and Innovation, Universiti Teknologi MARA.  ... 
doi:10.11591/ijeecs.v24.i1.pp260-268 fatcat:ipvhmxj42vdvncf2ucbropcire

Advances in Electromyogram Signal Classification to Improve the Quality of Life for the Disabled and Aged People

Ahsan
2010 Journal of Computer Science  
Conclusion: This study focused on the advances and improvements on different methodologies used for EMG signal classification with their efficiency, flexibility and applications.  ...  For the development of robust, flexible and efficient applications, this study opened a pathway to the researchers in performing future comparative studies between different EMG classification methods.  ...  It has been discovered that the classification performance of hand and finger movements depends significantly upon feature extraction.  ... 
doi:10.3844/jcssp.2010.706.715 fatcat:cd2cqqy4zvdnfd3ps5bsmhst7q

Enhancing Prediction of Prosthetic Fingers Movement Based on sEMG using Mixtures of Features and Random Forest

2019 International journal of recent technology and engineering  
Recently, researchers started focusing on providing feature extraction methods for both time domain and frequency domain for predicting either hand or finger movements.  ...  This sub-system is used to acquire electromyogram (EMG) signals from a person's muscles and convert it to movements to control prosthetic hands or fingers.  ...  ACKNOWLEDGMENT The authors would like to thank the owner of the sEMG signals dataset to make the data available online.  ... 
doi:10.35940/ijrte.d6801.118419 fatcat:vxne7crhfna4hldqvkvj4zaury

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Chengcheng Li, Gongfa Li, Guozhang Jiang, Disi Chen, Honghai Liu
2018 Neural computing & applications (Print)  
This paper designs 9 kinds of actions that can react effectively to the function of the hand and extracts the original EMG signals, which are based on the sEMG of the forearm muscles of human hand movement  ...  the accurate and effective control of the rehabilitation equipment or intelligent prosthesis, and the current research is based on data process and pattern recognition.  ...  Foundation of Wuhan University of Science and Technology (GF201705).  ... 
doi:10.1007/s00521-018-3909-z fatcat:awl3vtnyxng6dl3fkg32kmur6e

Myoelectric control of prosthetic hands: state-of-the-art review

Purushothaman Geethanjali
2016 Medical Devices : Evidence and Research  
All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand.  ...  Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features.  ...  Pattern recognition-based myoelectric control Pattern recognition-based myoelectric control typically consists of feature extraction and feature classification of segmented data in signal processing to  ... 
doi:10.2147/mder.s91102 pmid:27555799 pmcid:PMC4968852 fatcat:dp2kffj4vzcfhklopmlplxttbq

Simple and Computationally Efficient Movement Classification Approach for EMG-controlled Prosthetic Hand: ANFIS vs. Artificial Neural Network

Hessam Jahani Fariman, Siti A. Ahmad, M. Hamiruce Marhaban, M. Ali Jan Ghasab, Paul H. Chappell
2015 Intelligent Automation and Soft Computing  
These signals were segmented and the features extracted using a 22 new combined time-domain method of feature extraction.  ...  The aim of this paper is to propose an exploratory study on simple, accurate and 19 computationally efficient movement classification technique for prosthetic hand application.  ...  Pattern Classification of Surface Electromyography Based on AR Model 464 and High-order Neural Network.  ... 
doi:10.1080/10798587.2015.1008735 fatcat:bc54hzag65glropicoyqn22ifa

Design and Implementation of Myoelectric Controlled Arm

Tariq M. Younes, Mohammad A. AlKhedher, Abdel-Hamid Soliman, Aiman Al Alawin
2019 Journal of Mechatronics and Robotics  
The Neural Network can discriminate 4 performances of the EMG signals simultaneously. The digital signal processing was realized using MATLAB and LabVIEW software.  ...  In this system, the Artificial Neural Network (ANN) is used to learn the relation between the power spectrum of EMG signal analysed by Fast Fourier Transform (FFT) and the performance desired by handicapped  ...  Abdel-Hamid Soliman: Provided data-analysis and contributed to the writing of the manuscript. Dr. Aiman Al-Alawin: Designed the mechanical system of myoelectric arm.  ... 
doi:10.3844/jmrsp.2019.552.562 fatcat:wcdh4q2pzzhapnpx4qnxjnaj3i

Real-time intelligent pattern recognition algorithm for surface EMG signals

Mahdi Khezri, Mehran Jahed
2007 BioMedical Engineering OnLine  
Results: In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal are time and time-frequency domain.  ...  EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis  ...  , where the sensitivity and specificity may be defined as number of correct classification of multi-labeled movements per total number of confused cross-class movements, and number of correct classification  ... 
doi:10.1186/1475-925x-6-45 pmid:18053184 pmcid:PMC2222669 fatcat:btfgaoryenbwffc7xztnq2ffc4

D-S evidential theory on sEMG signal recognition

Honghai Liu, Ying Sun, Jianyi Kong, Guozhang Jiang, Gongfa Li, Weiliang Ding
2017 International Journal of Computing Science and Mathematics  
D-S evidential theory gets information based on information fusion of multi feature sets and multi classifiers.  ...  In order to promote the accuracy and complexity in the recognition of sEMG signals by classifiers, this paper tells a method based on fused D-S evidential theory.  ...  Acknowledgements This work was supported by grants of National Natural Science Foundation of China (Grant Nos. 51575407, 51575338, 1575412, 61273106).  ... 
doi:10.1504/ijcsm.2017.10004702 fatcat:2cpaaq35ejd6jnnovbldp3zcg4

Correlation Analysis of Electromyogram Signals for Multiuser Myoelectric Interfaces

Rami N. Khushaba
2014 IEEE transactions on neural systems and rehabilitation engineering  
The proposed method has been validated on a set of eight intact-limbed subjects, left-and-right handed, performing ten classes of bilateral synchronous fingers movements with four electrodes on each forearm  ...  The proposed idea is summarized into three steps, to: 1) train a myoelectric pattern classifier on the set of style-independent features extracted from multiple users using the proposed CCA-based mapping  ...  ACKNOWLEDGMENT The authors would like to acknowledge the support of Dr. Ali Al-Timemy from Plymouth University, UK for providing the second group of EMG datasets.  ... 
doi:10.1109/tnsre.2014.2304470 pmid:24760933 fatcat:isvpiqgdxnfindcry7z5izntr4

Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

Siho Shin, Mingu Kang, Jaehyo Jung, Youn Tae Kim
2021 Electronics  
In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector  ...  In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics10080923 fatcat:3s7gft7opbco5kmpctqvvmbepi

EMG Controlled Bionic Robotic Arm using Artificial Intelligence and Machine Learning

Farhan Fuad Rupom, Shafaitul Jannat, Farjana Ferdousi Tamanna, Gazi Musa Al Johan, Md. Motaharul Islam
2020 2020 IEEE Region 10 Symposium (TENSYMP)  
Then we used some algorithms of Machine Learning which are K-nearest Neighbor (KNN), Support Vector Machine (SVM), and also the combination of KNN and SVM both for feature classification on data recorded  ...  The purpose of this work is to utilize the power of Machine learning and Deep learning for predicting and recognizing hand gestures for prosthetic hand from collecting data of muscle activities.  ...  Acknowledgement First of all, we want to show our gratitude to Almighty Allah for whom our thesis  ... 
doi:10.1109/tensymp50017.2020.9230885 fatcat:izoehrlwgfejnpzz5f6hccxr4u
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