Recognition of facial movements by facial electromyogram signals in real time

Budko, Raisa; Chernov, N. N.; Budko, Artem

doi:10.17212/1814-1196-2018-2-59-74

Cited by 3 publications

(1 citation statement)

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“…, , U U U вычисляются от уровня ковариационной матрицы. Далее множество собственных ортогональных векторов образует ортонормированный базис [18]. Более подробное описание процедуры обучения сети описано в исследованиях [12,13].…”

Section: предварительная обработка сигнала и выделение признаковunclassified

Recognition of facial movements by facial electromyogram signals in real time

Budko¹,

Chernov²,

Budko³

2018

Science Bulletin of the NSTU

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Section: предварительная обработка сигнала и выделение признаковunclassified

Recognition of facial movements by facial electromyogram signals in real time

Budko¹,

Chernov²,

Budko³

2018

Science Bulletin of the NSTU

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Virtual Reality Mobile Platform for Restoring Upper Limbs Functions using Electromiography Data

Samman

Shakhnov

2021

HoBMSTU.SIE

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The article describes a mobile virtual reality platform based on the biological feedback of electromyography for restoring the functions of the upper limbs of people affected by accidents, stroke, Parkinson's disease or who suffered as a result of military operations. The definition of the electromyography (EMG) signal is given. The effectiveness of the biological feedback method in the rehabilitation process is indicated. The problem of initial data preprocessing is considered in order to identify the informative features of the EMG signal in the time domain. The general scheme of a mobile virtual reality platform based on biological feedback is described and preliminary evidence of the platform capability in its current state is presented. The block diagram of the EMG data acquisition module is developed. Developing a training program within the framework of computer games in two-dimensional or three-dimensional space is proposed. The algorithm of the mobile virtual reality platform based on the biological feedback of electromyography is illustrated. The results of the implementation of the proposed biofeedback electromyography system are presented. The advantages of the developed system in comparison with other systems currently available are emphasized; the disadvantages of this method are identified and ways to eliminate them are proposed

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Применение Инс В Интерфейсах Человек – Машина

Будко,

Будко

2019

№1(24) (2019)

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В настоящее время почти не осталось сфер человеческой деятельности, которых не касается автоматизация, получившая наибольшую популярность за последние несколько лет. На сегодняшний момент большую известность приобрели методы, которые основаны по принципу организации и функционирования биологических нейронных сетей. В статье приведен аналитический обзор возможностей применения искусственных нейронных сетей (ИНС) при разработке интерфейсов человек – машина, основанных на различных физических принципах взаимодействия с организмом человека. Данный интерфейс обеспечивает взаимодействие пользователя с управляемыми им машинами. Приведены примеры использования интерфейсов человек – машина в бытовой, медицинской и военных областях. Эффективность обусловлена гибкостью, нелинейностью, быстродействием и обучаемостью систем, основанных на нейронных сетях. Таким образом, пользователи могут проводить контроль процесса с большой точностью, добиваясь наилучшего результата. Рассмотрены вопросы применения ИНС в системах управления техническими объектами, основанных на распознавании естественной речи, отслеживании направления взгляда, анализе электрической активности головного мозга и мышечных волокон человека. Описаны задачи предварительной обработки информации, классификации, анализа результата, полученного с помощью обработки нейронной сетью. The relevance of this study is due to one of the main problems existing today in the field of building man-machine interfaces - is the creation of an effective management system that interacts directly with the user and external devices replacing functions (prostheses, wheelchairs, etc.). In this regard, this work is devoted to the study of the possibility of using physiological gestures from the daily life of a person to control the prosthesis with the safety of the forearm for at least one third. The leading approach to the study of this problem is the use of methods of statistical processing of experimental data, digital signal processing, machine learning algorithms and pattern recognition. This approach allows a comprehensive study of the electromyogram (EMG) of the forearm when making voluntary movements at different levels of the implementation of the myo-control system. The article presents the results of the EMG study recorded for 11 arbitrary movements from a group of subjects, describes the procedure for pre-processing the EMG and identifying characteristic features for signal recognition, discloses a method for classifying movements using an artificial neural network based on radial basic functions (RBF). Eight of the most suitable for classification movements were identified and ranked according to the classification accuracy: relaxation (like zero movement), hand opening, fist, hand flexion, hand supination, hand extension, hand pronation, pinch. The materials of the article are of practical value for building systems based on the human-machine interface, as well as for classification tasks in electrophysiology applications.

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Recognition of facial movements by facial electromyogram signals in real time

Cited by 3 publications

References 1 publication

Recognition of facial movements by facial electromyogram signals in real time

Recognition of facial movements by facial electromyogram signals in real time

Virtual Reality Mobile Platform for Restoring Upper Limbs Functions using Electromiography Data

Применение Инс В Интерфейсах Человек – Машина

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