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Boosting training for myoelectric pattern recognition using Mixed-LDA
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014Pattern recognition based myoelectric prostheses (MP) need a training procedure for calibrating the classifier. Due to the non-stationarity inhered in surface electromyography (sEMG) signals, the system should be retrained day by day in long-term use of MP.
Jianwei Liu +3 more
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Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control
Medical Engineering and Physics, 2015The non-stationary property of electromyography (EMG) signals in real life settings usually hinders the clinical application of the myoelectric pattern recognition for prosthesis control. The classical EMG pattern recognition approach consists of two separate steps: training and testing, without considering the changes between training and testing data
Jie Liu
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Effects of Long-Term Myoelectric Signals on Pattern Recognition
2013The long-term electromyography (EMG) signal can make significant effect on prosthesis control based on pattern recognition. In this paper, we collected myoelectric signals lasting twelve days and compared the performance of six different features in time and frequency domains.
Jiayuan He 0001 +3 more
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Effects of interelectrode distance on the robustness of myoelectric pattern recognition systems
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Myoelectric pattern recognition control can potentially provide upper limb amputees with intuitive control of multiple prosthetic functions. However, the lack of robustness of myoelectric pattern recognition algorithms is a barrier for clinical implementation.
Aaron J. Young, Levi J. Hargrove
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Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses [PDF]
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010 We evaluated real-time myoelectric pattern recognition control of a virtual arm by transradial amputees. Five unilateral patients performed 10 wrist and hand movements using their amputated and intact arms. In order to demonstrate the value of information from intrinsic hand muscles, this data was included in EMG recordings from the intact arm.
Guanglin Li, Todd A Kuiken
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Prosthesis-guided training of pattern recognition-controlled myoelectric prosthesis
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012Pattern recognition can provide intuitive control of myoelectric prostheses. Currently, screen-guided training (SGT), in which individuals perform specific muscle contractions in sync with prompts displayed on a screen, is the common method of collecting the electromyography (EMG) data necessary to train a pattern recognition classifier.
Caitlin L. Chicoine +2 more
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Effect of dynamic change of arm position on myoelectric pattern recognition
2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2012Robustness of pattern recognition is a key point for myoelectric prosthesis developed in laboratory to be applied successfully in real life. This study investigated the issue about effect of dynamic change of arm position on myoelectric pattern recognition. Six kinds of surface electromyography (sEMG) features were used and compared in detail.
Jianwei Liu +3 more
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Evaluation of Pattern Recognition in Myoelectric Signal Using Netlab GLM
2018 25th International Conference "Mixed Design of Integrated Circuits and System" (MIXDES), 2018The myoelectric signal that is collected from the surface of the skin can be used to construct rehabilitation systems for people who have suffered some trauma or who were born with some form of malformation. This signal is used to feed classifiers that can tell with some degree of distinction which movement each signal belongs to.
Gabriel Cirac Mendes Souza +2 more
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Adaptive classification in EMG pattern recognition for myoelectric control
2019 Chinese Automation Congress (CAC), 2019Myoelectric control based on pattern recognition technology is largely influenced by intrinsic and extrinsic factors over time in practical application, which often result in performance degradation. The aim of this study is to develop an adaptive classification scheme based on linear discriminant analysis (LDA) for myoelectric control system.
Shuai Ding +5 more
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Continuous multifunction myoelectric control using pattern recognition
Technology and Disability, 2003This work represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses pattern recognition to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement.
Englehart, Kevin +2 more
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