PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search

A stochastic scheme, namely, PLM-Lap, has recently been propounded, which relies on the profile likelihood (PL) constructed with a Laplace distribution for estimating muscle activation onsets (MAOs) in surface electromyographic (sEMG) data. The MAO detection accuracy and robustness of the PLM-Lap ha...

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Main Authors: Easter S. Suviseshamuthu, Didier Allexandre, Umberto Amato, Biancamaria Della Vecchia, Guang H. Yu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9110609/
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spelling doaj-1e019540f4a24536a30301a590c169b72021-03-30T02:57:53ZengIEEEIEEE Access2169-35362020-01-01810536210537510.1109/ACCESS.2020.30006939110609PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci SearchEaster S. Suviseshamuthu0https://orcid.org/0000-0002-8584-5947Didier Allexandre1Umberto Amato2Biancamaria Della Vecchia3Guang H. Yu4Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, NJ, USACenter for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, NJ, USAIstituto di Scienze Applicate e Sistemi Intelligenti Eduardo Caianiello, Consiglio Nazionale delle Ricerche, Napoli, ItalyDipartimento di Matematica, Università degli Studi La Sapienza, Roma, ItalyCenter for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, NJ, USAA stochastic scheme, namely, PLM-Lap, has recently been propounded, which relies on the profile likelihood (PL) constructed with a Laplace distribution for estimating muscle activation onsets (MAOs) in surface electromyographic (sEMG) data. The MAO detection accuracy and robustness of the PLM-Lap have been empirically shown to be better than those of several state-of-the-art approaches. The algorithm designates the data point index associated with the maximum of the PL function as an onset occurrence by regarding every sEMG data point as a candidate onset and hence exhaustively evaluating the objective function. This article concerns an expedient and faster approach premised on the discrete Fibonacci search (DFS) to locate the maximum of the discrete PL function. The experimental results support that both the exhaustive and DFS procedures are equivalent in a statistical sense, whereas the latter offers impressive computational savings by a factor of approximately 90. Owing to the speed-up, the accuracy of MAO estimation may further be enhanced by modeling the sEMG data with a set of PL functions, each one built using a suitable probability distribution, and picking the estimate from the best model. Three statistical criteria, i.e., Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling test, for choosing the probability distribution are recommended. A freely downloadable MATLAB package, namely PROLIFIC, meant for sEMG onset detection is available on MATLAB File Exchange from the following link: https://www.mathworks.com/matlabcentral/fileexchange/76495-prolific-profile-likelihood-based-on-fibonacci-search.https://ieeexplore.ieee.org/document/9110609/Anderson-Darling testdiscrete Fibonacci searchKolmogorov-Smirnov testLilliefors testmuscle activation onsetprofile likelihood
collection DOAJ
language English
format Article
sources DOAJ
author Easter S. Suviseshamuthu
Didier Allexandre
Umberto Amato
Biancamaria Della Vecchia
Guang H. Yu
spellingShingle Easter S. Suviseshamuthu
Didier Allexandre
Umberto Amato
Biancamaria Della Vecchia
Guang H. Yu
PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search
IEEE Access
Anderson-Darling test
discrete Fibonacci search
Kolmogorov-Smirnov test
Lilliefors test
muscle activation onset
profile likelihood
author_facet Easter S. Suviseshamuthu
Didier Allexandre
Umberto Amato
Biancamaria Della Vecchia
Guang H. Yu
author_sort Easter S. Suviseshamuthu
title PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search
title_short PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search
title_full PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search
title_fullStr PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search
title_full_unstemmed PROLIFIC: A Fast and Robust Profile-Likelihood-Based Muscle Onset Detection in Electromyogram Using Discrete Fibonacci Search
title_sort prolific: a fast and robust profile-likelihood-based muscle onset detection in electromyogram using discrete fibonacci search
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description A stochastic scheme, namely, PLM-Lap, has recently been propounded, which relies on the profile likelihood (PL) constructed with a Laplace distribution for estimating muscle activation onsets (MAOs) in surface electromyographic (sEMG) data. The MAO detection accuracy and robustness of the PLM-Lap have been empirically shown to be better than those of several state-of-the-art approaches. The algorithm designates the data point index associated with the maximum of the PL function as an onset occurrence by regarding every sEMG data point as a candidate onset and hence exhaustively evaluating the objective function. This article concerns an expedient and faster approach premised on the discrete Fibonacci search (DFS) to locate the maximum of the discrete PL function. The experimental results support that both the exhaustive and DFS procedures are equivalent in a statistical sense, whereas the latter offers impressive computational savings by a factor of approximately 90. Owing to the speed-up, the accuracy of MAO estimation may further be enhanced by modeling the sEMG data with a set of PL functions, each one built using a suitable probability distribution, and picking the estimate from the best model. Three statistical criteria, i.e., Kolmogorov-Smirnov, Lilliefors, and Anderson-Darling test, for choosing the probability distribution are recommended. A freely downloadable MATLAB package, namely PROLIFIC, meant for sEMG onset detection is available on MATLAB File Exchange from the following link: https://www.mathworks.com/matlabcentral/fileexchange/76495-prolific-profile-likelihood-based-on-fibonacci-search.
topic Anderson-Darling test
discrete Fibonacci search
Kolmogorov-Smirnov test
Lilliefors test
muscle activation onset
profile likelihood
url https://ieeexplore.ieee.org/document/9110609/
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