E
Ernest Nlandu Kamavuako
Researcher at King's College London
Publications - 106
Citations - 1963
Ernest Nlandu Kamavuako is an academic researcher from King's College London. The author has contributed to research in topics: Computer science & Electromyography. The author has an hindex of 22, co-authored 93 publications receiving 1472 citations. Previous affiliations of Ernest Nlandu Kamavuako include University of Kindu & Aalborg University.
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Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques
Muhammad Zia ur Rehman,Asim Waris,Syed Omer Gilani,Mads Jochumsen,Imran Khan Niazi,Mohsin Jamil,Mohsin Jamil,Dario Farina,Ernest Nlandu Kamavuako +8 more
TL;DR: CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features, and this data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.
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Support Vector Regression for Improved Real-Time, Simultaneous Myoelectric Control
TL;DR: The first application of a support vector machine (SVM) based scheme for real-time simultaneous and proportional myoelectric control of multiple degrees of freedom (DOFs) appears to be due to its higher estimation accuracy of all DOFs during inactive and low amplitude segments.
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Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms
TL;DR: The constrained control method outperformed unconstrained control in two real-time testing metrics including completion time and path efficiency and suggests that the quality of control using constrained and unconStrained contraction-based myoelectric schemes is not appreciably different when using comparable levels of muscle activation.
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Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions
Asim Waris,Imran Khan Niazi,Mohsin Jamil,Kevin Englehart,Winnie Jensen,Ernest Nlandu Kamavuako +5 more
TL;DR: Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome, and training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.
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Relationship between grasping force and features of single-channel intramuscular EMG signals.
TL;DR: Investigating the capacity of selective single-channel iEMG recordings to represent the grasping force with respect to the use of sEMG indicates that a selective i EMG recording is representative of the applied grasping force and can be used for proportional control.