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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">donstu</journal-id><journal-title-group><journal-title xml:lang="en">Advanced Engineering Research (Rostov-on-Don)</journal-title><trans-title-group xml:lang="ru"><trans-title>Advanced Engineering Research (Rostov-on-Don)</trans-title></trans-title-group></journal-title-group><issn pub-type="epub">2687-1653</issn><publisher><publisher-name>Don State Technical University</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.12737/10373</article-id><article-id custom-type="elpub" pub-id-type="custom">donstu-228</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MECHANICS</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МЕХАНИКА</subject></subj-group></article-categories><title-group><article-title>Identification of the elbow motion kinematic parameters by means of artificial neural networks technology</article-title><trans-title-group xml:lang="ru"><trans-title>Идентификация кинематических параметров движения локтя с помощью технологий искусственных нейронных сетей</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Бонилья</surname><given-names>Феликс</given-names></name><name name-style="western" xml:lang="en"><surname>Bonilla</surname><given-names>Felix</given-names></name></name-alternatives><email xlink:type="simple">vbonilla@yahoo.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Лукьянов</surname><given-names>Евгений Анатольевич</given-names></name><name name-style="western" xml:lang="en"><surname>Lukyanov</surname><given-names>Evgeny Anatolyevich</given-names></name></name-alternatives><email xlink:type="simple">lea_rnd@mail333.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Литвин</surname><given-names>Анатолий Витальевич</given-names></name><name name-style="western" xml:lang="en"><surname>Litvin</surname><given-names>Anatoly Vitalyevich</given-names></name></name-alternatives><email xlink:type="simple">lea_rnd@mail333.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Деплов</surname><given-names>Дмитрий Алексеевич</given-names></name><name name-style="western" xml:lang="en"><surname>Deplov</surname><given-names>Dmitry Alexeyevich</given-names></name></name-alternatives><email xlink:type="simple">dimadeplov@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Донской государственный технический университет, г. Ростов-на-Дону, Российская Федерация</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Don State Technical University, Rostov-on-Don, Russian Federation</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2015</year></pub-date><pub-date pub-type="epub"><day>30</day><month>03</month><year>2015</year></pub-date><volume>15</volume><issue>1</issue><fpage>39</fpage><lpage>47</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Bonilla F., Lukyanov E.A., Litvin A.V., Deplov D.A., 2015</copyright-statement><copyright-year>2015</copyright-year><copyright-holder xml:lang="ru">Бонилья Ф., Лукьянов Е.А., Литвин А.В., Деплов Д.А.</copyright-holder><copyright-holder xml:lang="en">Bonilla F., Lukyanov E.A., Litvin A.V., Deplov D.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.vestnik-donstu.ru/jour/article/view/228">https://www.vestnik-donstu.ru/jour/article/view/228</self-uri><abstract><p>The research objective is to study elbow flexion kinematic parameters using the artificial neural networks (ANN). Parameters of the surface electromyogram (sEMG) are used as ANN inputs. The ANN output is kinematic parameters of motion: direction, angular displacement, and angular velocity. The study has involved DSTU students and staff (11 people without pathologies of the musculoskeletal system). The sEMG signals taken from the biceps of each trial subject during no-load elbow bending are registered. During the experiment, shoulder and elbow joints are fixed by the passive exoskeleton. The feature vector for the neural network is formed using methods of the spectral and statistical analysis. The statistical analysis in the time domain includes the determination of the following parameters: dispersion of sEMG amplitude values, arithmetic mean value and mean-square value of sEMG absolute amplitudes, sEMG signal zero crossing rates. In the frequency domain, sEMG signal spectral analysis is performed by Fast Fourier Transform method. The power spectrum and the mean frequency of the power spectrum are determined. Best results of determining the kinematic parameters are obtained when using the mean frequency of the power spectrum and the total integrated sEMG signal power as inputs to the ANN. The ANN is trained by the method of the direct signal propagation and the back propagation of error. The results obtained can be used in the development of the bioelectric control systems for the mechatronic devices.</p></abstract><trans-abstract xml:lang="ru"><p>Целью исследования является определение кинематических параметров сгибания локтя с помощью искусственных нейронных сетей (ИНС). В качестве входов ИНС использовались параметры поверхностной электромиограммы (пЭМГ). Выходом ИНС были кинематические параметры движения: направление, угловое перемещение и угловая скорость. У каждого испытуемого регистрировались сигналы пЭМГ, снимаемые с бицепса при сгибании локтя без нагрузки. Во время эксперимента плечевой и локтевой суставы фиксировались пассивным экзоскелетом. Формирование вектора признаков для нейронной сети выполнялось с помощью методов спектрального и статистического анализов. Статистический анализ во временной области включал в себя определение: дисперсии амплитудных значений пЭМГ, среднеарифметического и среднеквадратического значений абсолютных амплитуд пЭМГ, частоты пересечения нулевой линии сигналом пЭМГ. В частотной области выполнялся спектральный анализ сигналов ЭМГ методом быстрого преобразования Фурье. Определялись спектр мощности, средняя частота спектра мощности. Установлены параметры пЭМГ, использование которых в качестве входных сигналов ИНС обеспечивает наименьшую ошибку оценки параметров движения. ИНС обучалась методом прямого распространения сигнала и обратного распространения ошибки. Наилучшие результаты определения кинематических параметров получены при использовании в качестве входов ИНС средней частоты спектра мощности и общей интегральной мощности сигнала пЭМГ. Полученные результаты могут быть использованы при разработке систем биоэлектрического управления мехатронными устройствами.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>пассивный экзоскелет</kwd><kwd>параметры движения локтя</kwd><kwd>электромиографический сигнал</kwd><kwd>статистический анализ</kwd><kwd>спектральный анализ</kwd><kwd>искусственная нейронная сеть</kwd><kwd>Matlab</kwd><kwd>Simulink</kwd><kwd>passive exoskeleton</kwd><kwd>elbow motion parameters</kwd><kwd>electromyographic signal</kwd><kwd>statistical analysis</kwd><kwd>spectral analysis</kwd><kwd>artificial neural network</kwd><kwd>Matlab</kwd><kwd>Simulink</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Современные системы управления протезами. Конструкции электродов и усилителей биосигналов [Электронный ресурс] / Д. 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