A State-Space EMG Model for the Estimation of Continuous Joint Movements

Han, Jianda; Ding, Qichuan; Xiong, Anbin; Zhao, Xingang

doi:10.1109/tie.2014.2387337

Cited by 152 publications

(78 citation statements)

References 29 publications

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“…And the attractive force makes the platform move to the target position. The APF of hybrid shared control Q to can be represented as [28], [29] Q to = Q at + Q re (18) with…”

Section: Hybrid Shared Controlmentioning

confidence: 99%

“…In fact, electromyography (EMG) signals can reflect the muscle activation which is regulated by the CNS [16] [17]. Thus, EMG profile can be regarded as a representation to indicate the human control intention [18]. The EMGbased methods can be integrated with Kinect sensor [19] [20] and inertial measure unit sensor to achieve human control of mobile robots or omnidirectional wheelchairs [21] [22].…”

Section: Introductionmentioning

confidence: 99%

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A Teleoperation Framework for Mobile Robots Based on Shared Control

Luo

Lin

et al. 2020

IEEE Robot. Autom. Lett.

107

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“…And the attractive force makes the platform move to the target position. The APF of hybrid shared control Q to can be represented as [28], [29] Q to = Q at + Q re (18) with…”

Section: Hybrid Shared Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Teleoperation Framework for Mobile Robots Based on Shared Control

Luo

Lin

et al. 2020

IEEE Robot. Autom. Lett.

107

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“…Han et al [22] developed a state space EMG model based on Hill Muscle Model (HillMM) for continuous estimation of elbow joint which however involves many physiological parameters. The computational complexity of the method to predict joint motion states makes it unsuitable for real-time applications.…”

Section: Overview On Electromyographymentioning

confidence: 99%

Processing of sEMG signals for online motion of a single robot joint through GMM modelization

Valentini

Michieletto

Spolaor

et al. 2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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At bottom, robotics is about us. It is the discipline of emulating our lives, of wondering how we work.Rod Grupen 3 4 SommarioLo scopo di questa tesi è di esplorare la possibilità di utilizzare i segnali elettromiograci (EMG) per allenare un modello di probabilità a componenti gaussiane (Gaussian Mixture Model) che riesca a stimare in tempo reale l'angolo di curvatura di un singolo giunto del corpo umano.Le informazioni del segnale sono state estratte utlizzando due approcci: il primo facente uso di trasformate nel dominio del tempo e nel dominio della frequenza, mentre il secondo utilizza la trasformata wavelet di cui è stata approfondita la migliore congurazione.Le funzioni wavelet di Daubechies Db2, Db4, Db6, Db7, Db44, Db45 sono state applicate ai segnali EMG corrispondenti a diversi muscoli della gamba a cui è seguita una aggregazione statistica dei dati da usare come input per la creazione del modello.Il modello GMM è stato validato su dati sconosciuti e le prestazioni sono state confrontate in base alla funzioni mother wavelet scelte per il calcolo della trasformata ed al numero di ripetizioni usate durante la fase di allenamento.La procedura migliore è stata utilizzata come base per sviluppare un software scritto in linguaggio C++ in grado di interfacciarsi con un robot umanoide a cui è stato fatto eseguire il movimento corrispondente a quello di una persona. Il software è stato sviluppato sotto forma di modulo ROS separando le varie fasi di elaborazione: generazione del segnale, acquisizione del segnale, analisi nel tempo e nella frequenza, sottomissione al modello e comunicazione del movimento al robot. Ogni fase è stata vericata indipendentemente dalle altre in modo da individuare la congurazione migliore per ciascuna. Il sistema è stato testato su un robot modello Aldebaran NAO.I risultati ottenuti mostrano come la procedura sia in grado di ottenere alte Achieved results showed that our procedure is able to obtain high perfor-

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“…Currently, sEMG-based continuous motion estimation includes two methods: kinetic model methods and regression model methods. J. Han et al [4] constructed a state-space sEMG model based on Hill's muscle model for continuous estimation of the elbow joint angle and angular velocity. E.E.…”

Section: Introductionmentioning

confidence: 99%

Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

Liu

et al. 2018

2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Continuous joint motion estimation plays an important part in accomplishing more compliant and safer human-machine interaction (HMI). Surface electromyogram (sEMG) signals, which contain abundant motion information, can be used as a source for continuous joint motion estimation. In this paper, a knee joint angle prediction system based on muscle synergy theory and generalized regression neural network (GRNN) was proposed. The wavelet transform threshold method was used for sEMG signals and angle trajectories denoising. The time-domain features wave-length extracted from four-channel sEMG signals were decomposed into a synergy matrix and an activation coefficient matrix by using nonnegative matrix factorization based on muscle synergy theory. A GRNN based on golden-section search was employed to build the activation model mapping from the activation coefficients to the knee joint angles, so as to realize the continuous knee joint angle estimation. The experimental results show that the average coefficient of determination is 0.933. In addition, a user graphic interface based on the Java platform was designed to display the dynamic sEMG data and predicted knee joint angles in real time.

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A State-Space EMG Model for the Estimation of Continuous Joint Movements

Cited by 152 publications

References 29 publications

A Teleoperation Framework for Mobile Robots Based on Shared Control

A Teleoperation Framework for Mobile Robots Based on Shared Control

Processing of sEMG signals for online motion of a single robot joint through GMM modelization

Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

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