Download or read book Real time Feature Extraction and Classification of Prehensile EMG Signals written by Christopher John Miller and published by . This book was released on 2008 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Feature Extraction for Classification of Prehensile Electromyography Patterns written by Sijiang Du and published by . This book was released on 2003 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction written by Bita Mokhlesabadifarahani and published by Springer. This book was released on 2015-02-10 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.
Download or read book Real time Measurement of Prehensile EMG Signals written by Saksit Siriprayoonsak and published by . This book was released on 2005 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book TypEMG written by Deniz Orkun Eren and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional input methods to interface with computer systems prove to be challenging for individuals with amputations or paralysis. Although several brain-machine interfaces were developed to address this problem, their invasive nature prevents widespread adoption. Alternatively, developing interfaces using non-invasive signals has been shown to be effective but they require large, non-intuitive gestures to function. In this work, we propose a framework to decode the subtle finger movements that occur naturally during typing via analyzing non-invasive EMG signals. Here, we establish synchronized communication with an amplifier to get signal recordings, perform signal preprocessing and utilize deep learning architectures for feature extraction and classification. Our approach achieves a within-session accuracy of up to 89.23% in detecting individual finger movements during a randomized typing task, with an average accuracy of 77.64% across all sessions. The time needed for classification is 4.16 ms per sample, making our framework suitable for real-time operation. Our framework demonstrates the possibility of identifying finger movements during typing in real-time using non-invasive EMG signals and provides a starting point for future work to allow individuals with amputations or disabilities to communicate effectively with computers.
Download or read book EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction written by Bita Mokhlesabadifarahani and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. This brief highlights a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. The neuro-fuzzy system is trained with 70 percent of the recorded Electromyography (EMG) cut off window and then used for classification and modeling purposes. The neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.
Download or read book Advances in VLSI Signal Processing Power Electronics IoT Communication and Embedded Systems written by Shubhakar Kalya and published by Springer Nature. This book was released on 2021-04-10 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises select peer-reviewed papers from the International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems (VSPICE-2020). The book provides insights into various aspects of the emerging fields in the areas Electronics and Communication Engineering as a holistic approach. The various topics covered in this book include VLSI, embedded systems, signal processing, communication, power electronics and internet of things. This book mainly focuses on the most recent innovations, trends, concerns and practical challenges and their solutions. This book will be useful for academicians, professionals and researchers in the area of electronics and communications and electrical engineering.
Download or read book Upper Extremity Electromyography Signal Feature Extraction and Classification written by Wan Mohd Bukhari Bin Wan Daud and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Hand Preshaping EMG Signal Feature Extraction and Grasp Classification for Prosthetic Hand Control written by Yiqiang Hou and published by . This book was released on 1996 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Feature extraction and pattern recognition for real time EEG processing written by Teera Achariyapaopan and published by . This book was released on 1985 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book EMG Signal Decomposition Using Motor Unit Potential Train Validity written by Hossein Parsaei and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are either time consuming or related to operator experience and skill. More importantly, they cannot be executed during automatic decomposition of EMG signals to assist with improving decomposition results. To overcome these issues, in this thesis the possibility of developing automatic MUPT validation algorithms has been explored. Several methods based on a combination of feature extraction techniques, cluster validation methods, supervised classification algorithms, and multiple classifier fusion techniques were developed. The developed methods, in general, use either the MU firing pattern or MUP-shape consistency of a MUPT, or both, to estimate its overall validity. The performance of the developed systems was evaluated using a variety of MUPTs obtained from the decomposition of several simulated and real intramuscular EMG signals. Based on the results achieved, the methods that use only shape or only firing pattern information had higher generalization error than the systems that use both types of information. For the classifiers that use MU firing pattern information of a MUPT to determine its validity, the accuracy for invalid trains decreases as the number of missed-classification errors in trains increases. Likewise, for the methods that use MUP-shape information of a MUPT to determine its validity, the classification accuracy for invalid trains decreases as the within-train similarity of the invalid trains increase. Of the systems that use both shape and firing pattern information, those that separately estimate MU firing pattern validity and MUP-shape validity and then estimate the overall validity of a train by fusing these two indices using trainable fusion methods performed better than the single classifier scheme that estimates MUPT validity using a single classifier, especially for the real data used. Overall, the multi-classifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance with overall accuracy of 99.4% and 98.8% for simulated and real data, respectively. The possibility of formulating an algorithm for automated editing MUPTs contaminated with a high number of false-classification errors (FCEs) during decomposition was also investigated. Ultimately, a robust method was developed for this purpose. Using a supervised classifier and MU firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by a high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and MUP shape information to detect MUPs that were erroneously assigned to the train. Evaluation based on simulated and real MU firing patterns, shows that contaminated MUPTs could be detected with 84% and 81% accuracy for simulated and real data, respectively. For a given contaminated MUPT, the algorithm on average correctly classified around 92.1% of the MUPs of the MUPT. The effectiveness of using the developed MUPT validation systems and the MUPT editing methods during EMG signal decomposition was investigated by integrating these algorithms into a certainty-based EMG signal decomposition algorithm. Overall, the decomposition accuracy for 32 simulated and 30 real EMG signals was improved by 7.5% (from 86.7% to 94.2%) and 3.4% (from 95.7% to 99.1%), respectively. A significant improvement was also achieved in correctly estimating the number of MUPTs represented in a set of detected MUPs. The simulated and real EMG signals used were comprised of 3-11 and 3-15 MUPTs, respectively.
Download or read book A Multiclassifier Approach to Motor Unit Potential Classification for EMG Signal Decomposition electronic Resource written by Rasheed, Sarbast and published by University of Waterloo. This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Towards Practical Brain Computer Interfaces written by Brendan Z. Allison and published by Springer Science & Business Media. This book was released on 2012-08-21 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain-computer interfaces (BCIs) are devices that enable people to communicate via thought alone. Brain signals can be directly translated into messages or commands. Until recently, these devices were used primarily to help people who could not move. However, BCIs are now becoming practical tools for a wide variety of people, in many different situations. What will BCIs in the future be like? Who will use them, and why? This book, written by many of the top BCI researchers and developers, reviews the latest progress in the different components of BCIs. Chapters also discuss practical issues in an emerging BCI enabled community. The book is intended both for professionals and for interested laypeople who are not experts in BCI research.
Download or read book Biceps Brachii Surface EMG Classification Using Neural Networks written by Chong Ee Lin and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents an approach of MATLAB-based system for clinical rehabilitation monitoring application. The main rationale for the development of such a system is that the pattern of the EMG signals elicited may differ depending on the activity of the muscle movement. Therefore, this research aims to study EMG signals elicited from biceps brachii muscle and classify the signal pattern to their respective class of activity. The proposed system consists of two main parts. The first part is about the development of an EMG acquisition platform. This platform consists of three modules; acquisition module, preprocessing module and feature extraction module. The acquisition module is used to acquire EMG signals from the subject. Several signal processing methods are carried out in the preprocessing module, where the EMG signal will undergo a series of processes like filtering, rectification and integration. After preprocessing, the signal is passed to the feature extraction module. In this module, statistical features such as mean, maximum, variance and standard deviation are computed to represent the signal pattern. The second part is regarding EMG pattern classification using neural networks. Feedforward BackPropagation Network (BPN) and Probabilistic Neural Network (PNN) are chosen as the classifiers to classify muscle activities. In the experimentation phase, 30 female subjects took part in this study. They were asked to perform several series of voluntary movement with respect to biceps brachii muscle. The experimental results show that EMG signals of different biceps activity is differed and simple statistical features are sufficient to represent the EMG pattern. The proposed BPN with Levenberg-Marquardt (LM) algorithm and PNN had achieved an overall classification rate of 88% while BPN with Resilient-Propagation (RP) algorithm achieved an overall classification of 87.11%. With these satisfactory results, the effectiveness of the proposed classifiers in EMG pattern classification problem is proven. xvii.
Download or read book Smart Intelligent Computing and Applications written by Suresh Chandra Satapathy and published by Springer. This book was released on 2018-11-04 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings covers advanced and multi-disciplinary research on design of smart computing and informatics. The theme of the book broadly focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries. The volume publishes quality work pertaining to the scope of the conference which is extended towards deployment of emerging computational and knowledge transfer approaches, optimizing solutions in varied disciplines of science, technology and healthcare.
Download or read book Classification of Finger Gestures from Myoelectric Singals written by Peter Ming-Wei Ju and published by . This book was released on 2000 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computational Intelligence in Electromyography Analysis written by Ganesh R. Naik and published by BoD – Books on Demand. This book was released on 2012-10-17 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research.
