Wrist EMG signals identification using neural network

Oyama, Tadahiro; Mitsukura, Yasue; Karungaru, Stephen; Tsuge, Shin; Fukumi, Minoru

doi:10.1109/iecon.2009.5415065

Cited by 14 publications

(10 citation statements)

References 6 publications

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“…Besides those, wrist motion recognition using wrist EMG was carried out using statistical methods, neural networks, etc. [11]- [14]. Such a work, however, caused a long training time or relatively low accuracy, and was not adequate for an on-line learning system.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, there have been several researches using wrist EMG signals, including related work by our group [11]- [15]. In these references, recognition of 7 wrist motions [11] [12] [14] and Japanese Janken [13], and control of a three hands machine [15] were carried out using wrist EMG signals. From these backgrounds, in this paper we measure EMG by attaching dry type sensors to wrist in the same way as the references [11]- [15], and then discriminate Japanese Janken by wrist EMG data.…”

Section: Introductionmentioning

confidence: 99%

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Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

Hiraoka¹,

Ito

et al. 2017

ECTI-CIT

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We propose a method which can discriminate hand motions in this paper. We measure an electromyogram (EMG) of wrist by using 8 dry type sensors. We focus on four motions, such as “Rock-Scissors-Paper” and “Neutral”. “Neutral” is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove a hum noise. Next, we combine values of sensors based on a Gaussian function. In this Gaussian function, variance and mean are 0.2 and 0, respectively. We then apply normalization by linear transformation to the values. Subsequently, we resize the values into the range from -1 to 1. Finally, a support vector machine (SVM) conducts learning and discrimination to classify them. We conducted experiments with seven subjects. Average of discrimination accuracy was 89.8%. In the previous method, the discrimination accuracy was 77.1%. Therefore, the proposed method is better in accuracy than the previous method. In future work, we will conduct an experiment which discriminates Japanese Janken of a subject who is not learned.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

Hiraoka¹,

Ito

et al. 2017

ECTI-CIT

Self Cite

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“…For the position mode, the feedback signals are from the potentiometers; for torque control mode, the feedback signals are the current used by the motor. NN and SVM are two well-known classifiers that have been used for sEMG classification [9]- [13]. Both classifiers have high tolerance to noise data as well as the ability to classify untrained pattern.…”

Section: Mechanical Design Of the Exoskeletonmentioning

confidence: 99%

Towards the development of a portable wrist exoskeleton

Xiao

Menon

2011

2011 IEEE International Conference on Robotics and Biomimetics

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This work investigates the design of a portable wrist exoskeleton prototype for rehabilitation of stroke survivors. Portability and ease of use are important to enable stroke victims to exercise in their domestic environments thus maximizing practice and consequently therapy effectiveness. A portable wrist exoskeleton could also be used as an assistive device to augment force. The device could therefore be used by seniors to improve their independent living at home or by workers in industrial settings. In this paper we propose a novel portable exoskeleton having two degrees of freedom to assist movements of the wrist. Surface electromyographic (sEMG) signals are recorded from the forearm and used to control the device. A support vector machine (SVM) is used as real-time classifier. Preliminary tests of the system are reported and discussed.

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“…Researchers used classifiers such as neural network (NN) classifier, fuzzy classifier, Neuro-fuzzy classifiers, k Nearest Neighbour classifier (kNN), support vector machine (SVM), [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] etc. In this paper, the author compared feature selective statistical classification technique simple logistic regression (SLR) to that of widely used NN, SVM and LDA.…”

Section: Introductionmentioning

confidence: 99%

A mechatronics platform to study prosthetic hand control using EMG signals

Geethanjali

2016

Australas Phys Eng Sci Med

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In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control. Various time domain features were extracted and grouped into five ensembles to compare the influence of features in feature-selective classifiers (SLR) with widely considered non-feature-selective classifiers, such as neural networks (NN), linear discriminant analysis (LDA) and support vector machines (SVM) applied with different kernels. The results divulged that the average classification accuracy of the SVM, with a linear kernel function, outperforms other classifiers with feature ensembles, Hudgin's feature set and auto regression (AR) coefficients. However, the slight improvement in classification accuracy of SVM incurs more processing time and memory space in the low-level controller. The Kruskal-Wallis (KW) test also shows that there is no significant difference in the classification performance of SLR with Hudgin's feature set to that of SVM with Hudgin's features along with AR coefficients. In addition, the KW test shows that SLR was found to be better in respect to computation time and memory space, which is vital in a low-level controller. Similar to SVM, with a linear kernel function, other non-feature selective LDA and NN classifiers also show a slight improvement in performance using twice the features but with the drawback of increased memory space requirement and time. This prototype facilitated the study of various issues of pattern recognition and identified an efficient classifier, along with a feature ensemble, in the implementation of EMG controlled prosthetic hands in a laboratory setting at low-cost. This platform may help to motivate and facilitate prosthetic hand research in developing countries.

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Wrist EMG signals identification using neural network

Cited by 14 publications

References 6 publications

Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

Towards the development of a portable wrist exoskeleton

A mechatronics platform to study prosthetic hand control using EMG signals

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