Steuerungs- und Signalverarbeitungskonzepte für eine multifunktionale Handprothese (Control and Signal Processing Concepts for a Multifunctional Hand Prosthesis)

Reischl, Markus; Mikut, Ralf; Pylatiuk, Christian; Schulz, Stefan; Beck, Sebastian; Bretthauer, Georg

doi:10.1524/auto.2002.50.6.279

Cited by 7 publications

(1 citation statement)

References 8 publications

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“…researchers have proposed a variety of myoelectric control schemes, including threshold based decision, amplitude-based coding method, hierarchical control decision, etc. [5,6]. The thresholding method is that an EMG signal caused by muscle contraction is firstly corrected, filtered and modulated, that then its peak value is compared with the threshold(s), and that finally two or three states of output correspond to the prosthetic hand to relax, bending or stretching, respectively.…”

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confidence: 99%

New advances in EMG control methods of anthropomorphic prosthetic hand

Wang

Liu

Leng

et al. 2017

Sci. China Technol. Sci.

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Prosthetic hands are becoming more and more popular among people without a hand and it is able to perform certain manual operations in function. In biology, action potentials trigger muscle contractions, and surface electromyography (sEMG) signals are the sum of action potentials under the skin exposed by the electrodes (via sebum and solution conduction). Myoelectric control is a technique that relies on extracting information contained in SEMG signals to determine muscle contractions and then control peripheral devices [1][2][3]. However, how to collect sEMG signals effectively is the first difficult problem for scholars [4] because of weakness, randomness and low signal-to-noise ratio of the sEMG signals. Multi DOF prosthetic hands have high flexibility and then more control parameters are needed. Therefore, the key techniques of EMG control of anthropomorphic prosthetic hand are extracting appropriate control parameters and studying real-time and reliable control algorithms.At present, single-degree-of-freedom prosthetic hands are one of the mainstream of commercial prosthesis markets. With mature EMG signal processing and control system, the performance of them is stable, but the flexibility is not enough. There are also some multi DOF prosthetic hands † These authors contributed equally to this work. * Corresponding authors (email: gaow13975@163.com; shiyan@buaa.edu.cn) that have been commercialized, such as the I-limb prosthesis introduced by Touch Bionics (Scotland) with 5 independent fingers, 24 degrees of freedom. But it's so expensive that ordinary people can't afford it. researchers have proposed a variety of myoelectric control schemes, including threshold based decision, amplitude-based coding method, hierarchical control decision, etc. [5,6]. The thresholding method is that an EMG signal caused by muscle contraction is firstly corrected, filtered and modulated, that then its peak value is compared with the threshold(s), and that finally two or three states of output correspond to the prosthetic hand to relax, bending or stretching, respectively. The scheme is so simple and reliable that it is the control method for a single-degree-of-freedom prosthetic hand. However, due to less information given by each electrode, it is not suitable for the control of a multi DOF prosthetic hand.Like the thresholding decision, in the amplitude-based coding scheme [7], the amplitudes of sEMG collected by differential electrodes are divided into three states, and the artificial hand motion commands are determined by encoding outputs according to time sequence. This decision is simple, whereas requires a lot of training, with low real-time and inflexible control. About the hierarchical control decision, the hand grasp modes of muscle stump are recognized by using advanced signal modulation and pattern recognition al-

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confidence: 99%

New advances in EMG control methods of anthropomorphic prosthetic hand

Wang

Liu

Leng

et al. 2017

Sci. China Technol. Sci.

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Intelligent recognition system for hand gestures

Honda

Weber

Lueth

2007

2007 3rd International IEEE/EMBS Conference on Neural Engineering

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In this article a new recognition system for hand gestures developed for the purpose of controlling active hand prosthesis is presented. The recognition system allows for the measurement and classification of muscle contractions around the lower arm. The system associates muscle contractions to corresponding finger grips of an electrically controlled prosthesis. Thus, a user can control a hand prosthesis by contracting the muscles of his arm through making hand gestures. This contribution describes the setup of the system and a first analysis of the stability and precision for recognizing and differentiating discrete hand gestures.

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PCA and LDA for EMG-based control of bionic mechanical hand

Zhang

Xiong

Zhao

2012

2012 IEEE International Conference on Information and Automation

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Electromyography (EMG) has some good abilities for bionic mechanical hand's control and researchers have proposed many kinds of methods for EMG classification. Principal Components Analysis (PCA) which is an ideal tool for dimension reduction tool was introduced for EMG classification. Linear Discriminant Analysis (LDA) performs outstandingly on classification. This paper does a comparative study on PCA and LDA for EMG classification, mainly including LDA for raw EMG, LDA for features, PCA and LDA for raw EMG and PCA and LDA for features. Here five time-domain features and four frequency-domain features are selected. The five hand motions including hand closing, hand opening, index finger pinching, middle finger pinching and hand relaxing are selected for classification. The result shows PCA and LDA for features obtain 99.0% motion success rate and 99.8% success rate of classification. The bionic mechanical hand got a good performance.

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Steuerungs- und Signalverarbeitungskonzepte für eine multifunktionale Handprothese (Control and Signal Processing Concepts for a Multifunctional Hand Prosthesis)

Cited by 7 publications

References 8 publications

New advances in EMG control methods of anthropomorphic prosthetic hand

New advances in EMG control methods of anthropomorphic prosthetic hand

Intelligent recognition system for hand gestures

PCA and LDA for EMG-based control of bionic mechanical hand

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