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Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Osama Dorgham, Ibrahim Al-Mherat, Jawdat Al-Shaer, Sulieman Bani-Ahmad, Stephen Laycock
2019 Future Internet  
The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network.  ...  The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles.  ...  Artificial Neural Network The artificial neural network (ANN) is a nonlinear model used in this research to model the SEMG signal with three loads 3 kg, 5 kg and 7 kg, since the SEMG signal is nonlinear  ... 
doi:10.3390/fi11010025 fatcat:jn5jntgo6nb4jgnfuganfqzzjy

Recognition of motion of human upper limb using sEMG in real time: Towards bilateral rehabilitation

Zhibin Song, Shuxiang Guo, Muye Pang, Songyuan Zhang
2012 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO)  
The experimental results show both methods can obtain reliable accuracy of motion pattern recognition.  ...  The effect of feature exaction with both methods was discussed through the processing of classification where Back-propagation Neural Networks were trained.  ...  On the other hand, the recognition ratio using coefficients of wavelet packet transform as input of artificial neural network is higher than that of using parameters of AR model.  ... 
doi:10.1109/robio.2012.6491165 dblp:conf/robio/SongGPZ12 fatcat:4wkjbtgnyzfrzaoufw2mkguueu

Hybrid soft computing systems for electromyographic signals analysis: a review

Hong-Bo Xie, Tianruo Guo, Siwei Bai, Socrates Dokos
2014 BioMedical Engineering OnLine  
This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis.  ...  Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system.  ...  An EMG recognition experiment of seven wrist operations was performed using a BP neural network with the selected frequency band.  ... 
doi:10.1186/1475-925x-13-8 pmid:24490979 pmcid:PMC3922626 fatcat:uifnqy6tmfe4nbnkif2fwmun44

EMG Characterization and Processing in Production Engineering

Manuel del Olmo, Rosario Domingo
2020 Materials  
Electromyography (EMG) signals are biomedical signals that measure electrical currents generated during muscle contraction.  ...  These signals are strongly influenced by physiological and anatomical characteristics of the muscles and represent the neuromuscular activities of the human body.  ...  The use of Artificial Neural Networks, ANN, has become a handy tool for predicting and classifying patterns.  ... 
doi:10.3390/ma13245815 pmid:33419283 fatcat:pf3ja333qrd4zbcuczihag56qy

Grasp Force Estimation from HD-EMG Recordings with Channel Selection Using Elastic Nets: Preliminary Study

Itzel Jared Rodriguez Martinez, Francesco Clemente, Gunter Kanitz, Andrea Mannini, Angelo Maria Sabatini, Christian Cipriani
2018 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)  
We tested this hypothesis by using features extracted from surface HD-EMG recordings from forearm muscles, classified using artificial neural networks.  ...  Continuous estimates of the grasp force from the electromyographic (EMG) signals were proposed in the past.  ...  trials into segments of 120 ms and selected the best subsets of channels with elastic nets to be used as input of an artificial neural network (ANN) for each segment.  ... 
doi:10.1109/biorob.2018.8487894 dblp:conf/biorob/MartinezCKMSC18 fatcat:5rh4rprtpraezbwzkuz2ozwa5y

Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sherif Said, Ilyas Boulkaibet, Murtaza Sheikh, Abdullah S. Karar, Samer Alkork, Amine Nait-ali
2020 Sensors  
An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel  ...  In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20113144 pmid:32498289 fatcat:h5ftcfmcmnbpnm7qzbaiopkhym

Feasibility of EMG-Based Neural Network Controller for an Upper Extremity Neuroprosthesis

J.G. Hincapie, R.F. Kirsch
2009 IEEE transactions on neural systems and rehabilitation engineering  
The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient  ...  The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities.  ...  A time-delayed artificial neural network (TDANN) was used to predict shoulder and elbow joint angles from EMG signals recorded from proximal arm muscles [16] , demonstrating that EMG signals contain relevant  ... 
doi:10.1109/tnsre.2008.2010480 pmid:19211327 pmcid:PMC3611331 fatcat:qc2v7yt7jndf3gqft63ldn5eqm

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Nurhazimah Nazmi, Mohd Abdul Rahman, Shin-Ichiroh Yamamoto, Siti Ahmad, Hairi Zamzuri, Saiful Mazlan
2016 Sensors  
This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions.  ...  For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s16081304 pmid:27548165 pmcid:PMC5017469 fatcat:wuvnfahaxnbpfbcgyibzji4kde

Multi-Modal Sensing Techniques for Interfacing Hand Prostheses: A Review

Yinfeng Fang, Nalinda Hettiarachchi, Dalin Zhou, Honghai Liu
2015 IEEE Sensors Journal  
These sensing techniques include electromyography (EMG), sonomyography (SMG), mechnomyography (MMG), electroneurography (ENG), electroencephalograhy (EEG), electrocorticography (ECoG), intracortical neural  ...  Relevant approaches that interpret bio-signals in the view of prosthetic hand manipulation are involved in as well.  ...  To implement the task of artificial hand manipulation by amputees, classical approaches are based on EMG signals, while the emergent interest is to use neural signals directly [82] .  ... 
doi:10.1109/jsen.2015.2450211 fatcat:3dnyesg4xbcqrlmbe3iadcnesm

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

2017 Sensors and materials  
First, the discrete wavelet transformation (DWT) technique is used to acquire the feature vectors of the EMG signals.  ...  For predicting his or her movement intention in advance, the EMG signals of the patient's muscle are captured and identified to realize the proposed EMG-triggered control.  ...  Acknowledgments The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan for financially/partially supporting this research under Contract Nos.  ... 
doi:10.18494/sam.2017.1736 fatcat:zjucdj733jaczfeofqykp5cueu

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

Rania Kolaghassi, Mohamad Kenan Al-Hares, Konstantinos Sirlantzis
2021 IEEE Access  
NETWORK An artificial neural network (ANN) is implemented by Kutilek et al [99] , to predict joint angles using cyclograms.  ...  An Elman neural network is implemented by Wang et al [104] , to detect knee joint angles using EMG. The signals were recorded during leg extension exercises at various speeds, with and without load.  ... 
doi:10.1109/access.2021.3104464 fatcat:bwnhkab6yjajtkvp3srzejpxru

A Comprehensive Review of Myoelectric Prosthesis Control [article]

Mohammad Reza Mohebbian, Marjan Nosouhi, Farzaneh Fazilati, Zahra Nasr Esfahani, Golnaz Amiri, Negar Malekifar, Fatemeh Yusefi, Mohsen Rastegari, Hamid Reza Marateb
2021 arXiv   pre-print
Prosthetic hands can be used to support upper-body amputees.  ...  Some of myoelectric prosthesis control's significant challenges are comfort, durability, cost, the application of under-sampled signals, and electrode shift.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
arXiv:2112.13192v1 fatcat:oc6kfbcrerduxfselt7izd2lp4

Applied Exoskeleton Technology: A Comprehensive Review of Physical and Cognitive Human-Robot-Interfac [article]

Farhad Nazari, Navid Mohajer, Darius Nahavandi, Abbas Khosravi, Saeid Nahavandi
2022 arXiv   pre-print
The outcomes of modelling show the potential use of single-channel input in low-power assistive devices.  ...  This model is utilised to predict the gate phase from a single Electromyography (EMG) channel input.  ...  Some of the widely used algorithms in the field are linear discriminant analysis (LDA) [34] , support vector machine (SVM) [20] , artificial neural networks (ANN) [27] and fuzzy neural networks (FNN  ... 
arXiv:2111.12860v6 fatcat:h7y6crlveffalg3vxrdclubvim

Using Artificial Intelligence for Pattern Recognition in a Sports Context

Ana Cristina Nunes Rodrigues, Alexandre Santos Pereira, Rui Manuel Sousa Mendes, André Gonçalves Araújo, Micael Santos Couceiro, António José Figueiredo
2020 Sensors  
The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification  ...  Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition.  ...  Conflicts of Interest: The authors declare no conflict of interest. Sensors 2020, 20, 3040  ... 
doi:10.3390/s20113040 pmid:32471189 pmcid:PMC7309132 fatcat:tmbu4hygyfca3drgfciy5ms2m4

Development of a Shoulder Disarticulation Prosthesis System Intuitively Controlled With the Trunk Surface Electromyogram

Susumu Kimizuka, Yohei Tanaka, Shunta Togo, Yinlai Jiang, Hiroshi Yokoi
2020 Frontiers in Neurorobotics  
via the EMG of the chest and back.  ...  We measured the surface EMG of the trunk of healthy subjects at multiple points and analyzed through principal component analysis to identify the proper EMG measurement portion of the trunk, which was  ...  Control Method In this study, the control mechanism of the system was based on a three-layered artificial neural network (ANN), which corresponds to the myoelectric pattern and the motion of the shoulder  ... 
doi:10.3389/fnbot.2020.542033 pmid:33192432 pmcid:PMC7658101 fatcat:imu6mf4hlrhpbdi4saqznnmmf4
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