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Bayesian Filtering of Surface EMG for Accurate Simultaneous and Proportional Prosthetic Control.

Published on Dec 1, 2016in IEEE Transactions on Neural Systems and Rehabilitation Engineering3.48
· DOI :10.1109/TNSRE.2015.2501979
David Hofmann2
Estimated H-index: 2
(MPG: Max Planck Society),
Ning Jiang27
Estimated H-index: 27
(UW: University of Waterloo)
+ 1 AuthorsDario Farina70
Estimated H-index: 70
(GAU: University of Göttingen)
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Abstract
European Research Council (ERC) [DEMOVE 267888]; Federal Ministry of Education and Research (BMBF) of Germany [01GQ0811]; New Faculty Startup Grant of the University of Waterloo
  • References (32)
  • Citations (12)
Cite
References32
Newest
Ning Jiang27
Estimated H-index: 27
(GAU: University of Göttingen),
Ivan Vujaklija9
Estimated H-index: 9
+ 2 AuthorsDario Farina70
Estimated H-index: 70
(GAU: University of Göttingen)
In this paper, we present a systematic analysis of the relationship between the accuracy of the mapping between EMG and hand kinematics and the control performance in goal-oriented tasks of three simultaneous and proportional myoelectric control algorithms: nonnegative matrix factorization (NMF), linear regression (LR), and artificial neural networks (ANN). The purpose was to investigate the impact of the precision of the kinematics estimation by a myoelectric controller for accurately complete ...
Ning Jiang27
Estimated H-index: 27
,
Hubertus Rehbaum8
Estimated H-index: 8
+ 2 AuthorsDario Farina70
Estimated H-index: 70
We propose an approach for online simultaneous and proportional myoelectric control of two degrees-of-freedom (DoF) of the wrist, using surface electromyographic signals. The method is based on the nonnegative matrix factorization (NMF) of the wrist muscle activation to extract low-dimensional control signals translated by the user into kinematic variables. This procedure does not need a training set of signals for which the kinematics is known (labeled dataset) and is thus unsupervised (althoug...
Silvia Muceli18
Estimated H-index: 18
(GAU: University of Göttingen),
Ning Jiang27
Estimated H-index: 27
(GAU: University of Göttingen),
Dario Farina70
Estimated H-index: 70
(GAU: University of Göttingen)
Previous research proposed the extraction of myoelectric control signals by linear factorization of multi-channel electromyogram (EMG) recordings from forearm muscles. This paper further analyses the theoretical basis for dimensionality reduction in high-density EMG signals from forearm muscles. Moreover, it shows that the factorization of muscular activation patterns in weights and activation signals by non-negative matrix factorization (NMF) is robust with respect to the channel configuration ...
Published on Feb 1, 2014in IEEE Transactions on Biomedical Engineering4.49
Ali Ameri4
Estimated H-index: 4
(UNB: University of New Brunswick),
Erik Scheme16
Estimated H-index: 16
(UNB: University of New Brunswick)
+ 2 AuthorsPhilip A. Parker22
Estimated H-index: 22
(UNB: University of New Brunswick)
In this paper, the simultaneous real-time control of multiple degrees of freedom (DOF) for myoelectric systems is investigated. The goal of this study, in which ten able-bodied subjects participated, was to directly compare three control paradigms of constrained (force targeted), unconstrained (position targeted) and resisted unconstrained (position targeted) limb contractions. Artificial neural networks (ANNs) were trained for simultaneous myoelectric control of the three degrees of freedom (DO...
Published on Dec 1, 2013in Annals of Biomedical Engineering3.47
Tobias Pistohl9
Estimated H-index: 9
(Newcastle University),
Christian Cipriani28
Estimated H-index: 28
+ 1 AuthorsKianoush Nazarpour16
Estimated H-index: 16
(Newcastle University)
Powered hand prostheses with many degrees of freedom are moving from research into the market for prosthetics. In order to make use of the prostheses’ full functionality, it is essential to study efficient ways of high dimensional myoelectric control. Human subjects can rapidly learn to employ electromyographic (EMG) activity of several hand and arm muscles to control the position of a cursor on a computer screen, even if the muscle-cursor map contradicts directions in which the muscles would ac...
Lukai Liu4
Estimated H-index: 4
,
Pu-Kun Liu2
Estimated H-index: 2
+ 2 AuthorsKevin Englehart36
Estimated H-index: 36
(UNB: University of New Brunswick)
Time and frequency domain features of the surface electromyogram (EMG) signal acquired from multiple channels have frequently been investigated for use in controlling upper-limb prostheses. A common control method is EMG-based motion classification. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening decorrelates the EMG signal and has been shown to be advantageous in other EMG applications including EMG amplitude estimation and EMG-fo...
Published on May 1, 2013in IEEE Transactions on Biomedical Engineering4.49
Aaron J. Young19
Estimated H-index: 19
(Rehabilitation Institute of Chicago),
Lauren H. Smith11
Estimated H-index: 11
(NU: Northwestern University)
+ 1 AuthorsLevi J. Hargrove29
Estimated H-index: 29
(NU: Northwestern University)
Advanced upper limb prostheses capable of actuating multiple degrees of freedom (DOFs) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classificat...
Published on Apr 1, 2013in Biological Cybernetics1.30
Claudio Castellini26
Estimated H-index: 26
(DLR: German Aerospace Center),
Patrick van der Smagt17
Estimated H-index: 17
(TUM: Technische Universität München)
Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-re...
Published on Sep 1, 2012
Janne M. Hahne7
Estimated H-index: 7
,
Hubertus Rehbaum8
Estimated H-index: 8
(GAU: University of Göttingen)
+ 5 AuthorsLucas C. Parra46
Estimated H-index: 46
(CCNY: City College of New York)
Previous approaches for extracting real-time proportional control information simultaneously for multiple degree of Freedom(DoF) from the electromyogram (EMG) often used non-linear methods such as the multilayer perceptron (MLP). In this pilot study we show that robust control is also possible with conventional linear regression if EMG power measures are available for a large number of electrodes. In particular, we show that it is possible to linearize the problem with simple nonlinear transform...
Published on Jul 1, 2012in IEEE Transactions on Biomedical Engineering4.49
Kianoush Nazarpour16
Estimated H-index: 16
(University of Birmingham),
Christian Ethier15
Estimated H-index: 15
(NU: Northwestern University)
+ 3 AuthorsLee E. Miller37
Estimated H-index: 37
(NU: Northwestern University)
A constrained point-process filtering mechanism for prediction of electromyogram (EMG) signals from multichannel neural spike recordings is proposed here. Filters from the Kalman family are inherently suboptimal in dealing with non-Gaussian observations, or a state evolution that deviates from the Gaussianity assumption. To address these limitations, we modeled the non-Gaussian neural spike train observations by using a generalized linear model that encapsulates covariates of neural activity, in...
Cited By12
Newest
Published on Jan 1, 2020in Biomedical Signal Processing and Control2.94
Anand Kumar Mukhopadhyay (IIT-KGP: Indian Institute of Technology Kharagpur), Suman Samui2
Estimated H-index: 2
(IIT-KGP: Indian Institute of Technology Kharagpur)
Abstract The classification of surface electromyography (sEMG) signal has an important usage in the man-machine interfaces for proper controlling of prosthetic devices with multiple degrees of freedom. The vital research aspects in this field mainly focus on data acquisition, pre-processing, feature extraction and classification along with their feasibility in practical scenarios regarding implementation and reliability. In this article, we have demonstrated a detailed empirical exploration on D...
Naishi Feng1
Estimated H-index: 1
(NU: Northeastern University),
HongWANG37
Estimated H-index: 37
(NU: Northeastern University)
+ 3 AuthorsFeng Wang (NU: Northeastern University)
Abstract Before the myoelectric prosthesis become a good substitute of a natural arm, there still exist several big challenges in terms of hand structural design and control algorithms. The overall objective of this paper is to propose a new humanoid hand with specify force output under sEMG control. A fiber reinforced three-cavity structure is proposed with thumb and middle finger bendable and the rest of the fingers bendable and twistable simultaneously to imitate the finger-to-finger function...
Published on May 1, 2019in Applied Soft Computing4.87
Bei Wang (University of Sheffield), Zhichao Li (ECUST: East China University of Science and Technology)+ 2 AuthorsXuefeng Yan15
Estimated H-index: 15
(ECUST: East China University of Science and Technology)
Abstract Probabilistic principal component analysis (PPCA) based approaches have been widely used in the field of process monitoring. However, the traditional PPCA approach is still limited to linear dimensionality reduction. Although the nonlinear projection model of PPCA can be obtained by Gaussian process mapping, the model still lacks robustness and is susceptible to process noise. Therefore, this paper proposes a new nonlinear process monitoring and fault diagnosis approach based on the Bay...
Anjana Gayathri Arunachalam , Kevin Englehart36
Estimated H-index: 36
(UNB: University of New Brunswick),
Jon Sensinger2
Estimated H-index: 2
(UNB: University of New Brunswick)
Published on May 1, 2019in Biomedical Signal Processing and Control2.94
Luzheng Bi9
Estimated H-index: 9
(BIT: Beijing Institute of Technology),
Aberham ->Genetu Feleke (BIT: Beijing Institute of Technology), Cuntai Guan38
Estimated H-index: 38
(NTU: Nanyang Technological University)
Abstract Electromyography (EMG) signal is one of the widely used biological signals for human motor intention prediction, which is an essential element in human-robot collaboration systems. Studies on motor intention prediction from EMG signal have been concentrated on classification and regression models, and there are numerous review and survey papers on classification models. However, to the best of our knowledge, there is no review paper on regression models or continuous motion prediction f...
Published on Apr 18, 2019in Sensors3.03
Vinicius Horn Cene3
Estimated H-index: 3
,
Mauricio Tosin1
Estimated H-index: 1
+ 1 AuthorsAlexandre Balbinot7
Estimated H-index: 7
Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reli...
Published on Apr 1, 2019 in ICRA (International Conference on Robotics and Automation)
Michele Barsotti6
Estimated H-index: 6
(Sant'Anna School of Advanced Studies),
Sigrid S. G. Dupan (Radboud University Nijmegen)+ 3 AuthorsDario Farina70
Estimated H-index: 70
(Imperial College London)
A hand impairment can have a profound impact on the quality of life. This has motivated the development of dexterous prosthetic and orthotic devices. However, their control with neuromuscular interfacing remains challenging. Moreover, existing myocontrol interfaces typically require an extensive calibration. We propose a minimally supervised, online myocontrol system for proportional and simultaneous finger force estimation based on ridge regression using only individual finger tasks for trainin...
Published on Mar 1, 2019in Journal of Medical Systems2.42
Qiang Meng (Zhengzhou University), Jianjun Zhang (Zhengzhou University), Xi Yang (Zhengzhou University)
Aiming at the characteristics that electromyography (EMG) signals can reflect the human body’s motive intention and the information of muscle’s motive state, this paper makes a thorough study on the evaluation of surface electromyography signals’ motive state. At the same time, EMG signals can reflect the characteristics of limb movement and its changing rules, and can acquire the functional characteristics of limb movement so as to accurately evaluate the rehabilitation status of patients. In t...
Published on Dec 1, 2018in Journal of Neuroengineering and Rehabilitation3.58
Ivan Vujaklija9
Estimated H-index: 9
(Imperial College London),
Vahid Shalchyan3
Estimated H-index: 3
(IUST: Iran University of Science and Technology)
+ 3 AuthorsDario Farina70
Estimated H-index: 70
(Imperial College London)
In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach. Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized...
Published on Sep 1, 2018in Biomedical Signal Processing and Control2.94
Vinicius Horn Cene3
Estimated H-index: 3
(UFRGS: Universidade Federal do Rio Grande do Sul),
Alexandre Balbinot7
Estimated H-index: 7
(UFRGS: Universidade Federal do Rio Grande do Sul)
Abstract Several Machine Learning techniques have been employed to process sEMG signals in order to provide a reliable control biosignal. Although some papers report accuracy rates superior to 90%, there is a lack of more detailed reasoning for reliable systems capable of providing control signals to users that may, for instance, control a prosthetic device. In this paper, we combined two strategies in order to increase the representativity of the sEMG signals: (a) the use of a stochastic filter...
View next paperThe Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges