Development of Zero-Phase-Tracking Man-Machine Interfacewith Electro-Mechanical Delay of Electromyogram

Hayashi, Masamichi J.; Kogure, Hiroshi; Ura, K.; Iwase, Masami; Sadahiro, Teruyoshi; Hatakeyama, S.; Sawaguchi, Eita

doi:10.7210/jrsj.30.767

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Consequently, the unit of q is milliseconds. The q for NDARX indicates the average value of a dead time function [6]. Figure 10 shows waveforms of prediction results using each method.…”

Section: Prediction Resultsmentioning

confidence: 99%

“…Electromechanical delay (EMD) is a time delay between a generation of sEMG and subsequent occurrence of human motions, and sEMG can be obtained in advance of motions occurrence by using this time delay. A method to predict wrist angles in advance utilizing EMD of sEMG has been proposed by Hayashi et al [6]. Using EMD explicitly, FE is able to process in real time, if its data accumulation time is less than the time delay of EMD.…”

Section: Introductionmentioning

confidence: 99%

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A human motion angle prediction method using a neural network and feature-extraction-processed sEMG obtained in real time to utilize mechanical delay of sEMG

Ikeda

Sadahiro

2023

JASSE

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Surface electromyogram (sEMG) has been used to estimate human motions. Feature extraction (FE) has been proposed as a process to decrease noise and extract information about muscle activities from measured raw sEMG signals. Electromechanical delay (EMD) is a time delay between a generation of sEMG and subsequent occurrence of human motions, and sEMG can be obtained in advance of motions occurrence by using this time delay. The purpose of this study is to propose a real time human motion prediction method using a neural network model and FE-processed sEMG signals utilizing EMD. Experimental results show that the proposed method has better prediction results among other methods.

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“…Consequently, the unit of q is milliseconds. The q for NDARX indicates the average value of a dead time function [6]. Figure 10 shows waveforms of prediction results using each method.…”

Section: Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A human motion angle prediction method using a neural network and feature-extraction-processed sEMG obtained in real time to utilize mechanical delay of sEMG

Ikeda

Sadahiro

2023

JASSE

Self Cite

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Improvement of Estimation Accuracy of Body Motion Based on Muscle Potential Signal

Shingo

Hatakeyama

Iwase

et al. 2020

J. Phys.: Conf. Ser.

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The purpose of this research is to develop a man-machine interface that changes machine impedeace based on surface myoelectric potential. The impedance model of the wrist joint is constructed from the measured data to derive the moment of inertia, viscosity coefficient and rigidity of the human’s wrist joint, investigate the relationship between human joint impedance and electromyography (EMG), according to the change of EMG from the relationship, We have developed an interface to change the mechanical impedance of the robot arm. We investigate the relationship between human joint impedance and EMG, and apply the sparse modeling method to the modeling process. We have created an interface to change the mechanical impedance of the robot arm based on the model.As a result, myoelectric potential and force data have been acquired. Also, mechanical impedance has been acquired.In this study, we improve the estimation accuracy of physical motion by selecting characteristic parameters and terms using sparse modeling, which is considered to be feasible by estimating the force that occurs at the same time as the position at the time of physical movement.

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Experimental verification for maneuverability of man-machine interface based on EMG

Hirata

Hayashi

Iwase

2013

Proceedings of the 2013 IEEE/SICE International Symposium on System Integration

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Development of Zero-Phase-Tracking Man-Machine Interfacewith Electro-Mechanical Delay of Electromyogram

Cited by 3 publications

References 10 publications

A human motion angle prediction method using a neural network and feature-extraction-processed sEMG obtained in real time to utilize mechanical delay of sEMG

A human motion angle prediction method using a neural network and feature-extraction-processed sEMG obtained in real time to utilize mechanical delay of sEMG

Improvement of Estimation Accuracy of Body Motion Based on Muscle Potential Signal

Experimental verification for maneuverability of man-machine interface based on EMG

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