Development of an upper limb neuroprosthesis to voluntarily control elbow and hand

Ogiri, Yosuke; Yamanoi, Yusuke; Nishino, Wataru; Kato, Ryu; Takagi, Tasuku; Yokoi, Hiroshi

doi:10.1109/roman.2017.8172317

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Две степени подвижности нейропротеза управляются с помощью технологии поверхностной ЭМГ [25]. ЭМГ-сигналы, полученные с четырех сухих электродов, и система распознавания образов позволяют записать пять видов движений, с помощью которых осуществляется управление протезом.…”

Section: нейроинтерфейсыunclassified

Control methods for upper extremity prostheses

Gorokhova¹,

Golovin²,

Chezhin³

2019

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Проанализированы методы управления протезами верхних конечностей. Рассмотрены особенности управления протезами на три вида ампутации (на уровне кисти и пальцев, на уровне предплечья и после вычленения предплечья, на уровне плеча и после вычленения плеча). Показано, как уровень ампутации связан с используемыми методами управления. Наиболее перспективными методами управления в протезах кисти и пальцев являются методы с расширенной обратной связью и адаптивные; на уровне предплечья и после его вычленения актуально использование нейроинтерфейсов, явления мышечных синергий и других; на уровне плеча и после его вычленения эффективны неинвазивные и интуитивные методы управления. Несмотря на многообразие достижений современных технологий задача полного восстановления всех утраченных функций верхней конечности остается актуальной. Выделен ряд нерешенных задач в области разработки систем управления протезами верхних конечностей: организация расширенной обратной связи «человек-протез», управление большим количеством степеней подвижности протеза и одновременное использование источников полезных сигналов различной природы. Ключевые слова протез верхней конечности, система управления, метод управления, автоматическое управление, ЭМГ, ЭЭГ, распознавание образов, нейронная сеть, интуитивное управление, адаптивное управление

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Section: нейроинтерфейсыunclassified

Control methods for upper extremity prostheses

Gorokhova¹,

Golovin²,

Chezhin³

2019

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…One of the products of this prominent project is the SSSA-MyHand [5]. Meanwhile, our group has been developing multi-DOF myoelectric hands for several years now [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Learning Data Correction for Myoelectric Hand Based on “Survival of the Fittest”

et al. 2021

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In recent years, myoelectric hands have become multi-degree-of-freedom (DOF) devices, which are controlled via machine learning methods. However, currently, learning data for myoelectric hands are gathered manually and thus tend to be of low quality. Moreover, in the case of infants, gathering accurate learning data is nearly impossible because of the difficulty of communicating with them. Therefore, a method that automatically corrects errors in the learning data is necessary. Myoelectric hands are wearable robots and thus have volumetric and weight constraints that make it infeasible to store large amounts of data or apply complex processing methods. Compared with general machine learning methods such as image processing, those for myoelectric hands have limitations on the data storage, although the amount of data to be processed is quite large. If we can use this huge amount of processing data to correct the learning data without storing the processing data, the machine learning performance is expected to improve. We then propose a method for correcting the learning data through utilisation of the signals acquired during the use of the myoelectric hand. The proposed method is inspired by “survival of the fittest.” The effectiveness of the method was verified through offline analysis. The method reduced the amount of learning data and learning time by approximately a factor of 10 while maintaining classification rates. The classification rates improved for one participant but slightly deteriorated on average among all participants. To solve this problem, verifying the method via interactive learning will be necessary in the future.

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“…Another problem of the classical approach to the analysis of EMG, based on the classification and recognition of gestures, is the limitation on the number of degrees of freedom for a one-time movement of the operation point [13][14][15][16]. For example, a gesture classifier-based operating point position control system may be based on four gestures, brush up, down, left, and right.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Possibility of Vector-Command Control Based on Forearm EMG

Budko

Medvedev

Budko

et al. 2021

Electromechanics and Robotics

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The work is devoted to solving the problems of increasing the depth and increasing the long-term stability of communication channels in human-machine interfaces, built on the electrical activity databases of the forearm muscles. For this, a method for analyzing electromyogram (EMG) signals is proposed, which combines vector and command control. The mathematical model for vector analysis of EMG is built in spherical coordinates based on the real spatial arrangement of the electrodes on the forearm, taking into account the possibility of a random phase shift during operation. Vector analysis of EMG is used to solve the calibration task of EMG sensors channels by the spatial arrangement of the electrodes and calculating the resultant vector of muscular forces. These forces are used as an additional information channel to set the movement direction of the control object operating point. Command control is based on gesture recognition by means of a pretrained artificial neural network (ANN) of the multilayer perceptron type. Processing results of actually recorded EMG signals by the proposed method are presented. There are given research results of correlation between processed signal fragments duration and the process of extracting the hand rotational movement information. It is proposed to use the signal duration of 250 ms. An algorithm of reassigning and calibrating the EMG channels amplification is proposed, which makes it possible to use further a once trained ANN for recognition and classification of gestures, while the position changing of electrodes between operation sessions is allowed. The work results can be used for calibration algorithms development, gesture recognition, and control of technical objects based on electromyographic human-machine interfaces.

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Development of an upper limb neuroprosthesis to voluntarily control elbow and hand

Cited by 5 publications

References 9 publications

Control methods for upper extremity prostheses

Control methods for upper extremity prostheses

Learning Data Correction for Myoelectric Hand Based on “Survival of the Fittest”

Investigation of the Possibility of Vector-Command Control Based on Forearm EMG

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