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.
Papers
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Journal ArticleDOI
A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN.
Asim Waris,Muhammad Zia ur Rehman,Imran Khan Niazi,Imran Khan Niazi,Imran Khan Niazi,Mads Jochumsen,Kevin Englehart,Winnie Jensen,Heidi Haavik,Ernest Nlandu Kamavuako +9 more
TL;DR: Results suggest that time variations in the iEMG signal can be catered by concatenating the data over several days, and this scheme can be helpful in attaining stable and robust performance.
Journal ArticleDOI
Toward Proportional Control of Myoelectric Prostheses with Muscle Synergies
TL;DR: In this article, the authors used muscle synergies extracted from targeted surface EMG for estimating force during multiple-degree-of-freedom (DoF) contractions involving the wrist and hand.
Journal ArticleDOI
Affordable Embroidered EMG Electrodes for Myoelectric Control of Prostheses: A Pilot Study.
Ernest Nlandu Kamavuako,Ernest Nlandu Kamavuako,Mitchell Brown,Xinqi Bao,Ines Chihi,Ines Chihi,Samuel Pitou,Matthew Howard +7 more
TL;DR: In this paper, the usability of embroidered EMG electrodes for myoelectric control was investigated by comparing online and offline performance against conventional gel electrodes, and the results indicated that embroidered electrodes can achieve similar performance to gel electrodes paving the way for low-cost myelectric prostheses.
Journal ArticleDOI
Identification of a self-paced hitting task in freely moving rats based on adaptive spike detection from multi-unit M1 cortical signals.
TL;DR: The results showed the feasibility of detecting a motor task in a less restricted environment than commonly applied within invasive BCI research.
Book ChapterDOI
Use of Sample Entropy Extracted from Intramuscular EMG Signals for the Estimation of Force
TL;DR: In this article, the authors used sample entropy as a feature extracted from intramuscular electromyography (EMG) for the estimation of force in 10 able-bodied subjects.