158 research outputs found
Onset detection to study muscle activity in reaching and grasping movements in rats
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.EMG signals reflect the neuromuscular activation patterns related to the execution of a certain movement or task. In this work, we focus on reaching and grasping (R&G) movements in rats. Our objective is to develop an automatic algorithm to detect the onsets and offsets of muscle activity and use it to study muscle latencies in R&G maneuvers. We had a dataset of intramuscular EMG signals containing 51 R&G attempts from 2 different animals. Simultaneous video recordings were used for segmentation and comparison. We developed an automatic onset/offset detector based on the ratio of local maxima of Teager-Kaiser Energy (TKE). Then, we applied it to compute muscle latencies and other features related to the muscle activation pattern during R&G cycles. The automatic onsets that we found were consistent with visual inspection and video labels. Despite the variability between attempts and animals, the two rats shared a sequential pattern of muscle activations. Statistical tests confirmed the differences between the latencies of the studied muscles during R&G tasks. This work provides an automatic tool to detect EMG onsets and offsets and conducts a preliminary characterization of muscle activation during R&G movements in rats. This kind of approaches and data processing algorithms can facilitate the studies on upper limb motor control and motor impairment after spinal cord injury or stroke.Postprint (published version
Computational Intelligence in Electromyography Analysis
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research
Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review
The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles
Introduction to this Special Issue: Intelligent Data Analysis on Electromyography and Electroneurography
Computer-aided electromyography (EMG) and elec- troneurography (ENG) have become indispensable tools in the daily activities of neurophysiology laboratories in facilitating quantitative analysis and decision making in clinical neurophysiology, rehabilitation, sports medicine, and studies of human physiology. These tools form the basis of a new era in the practice of neurophysiology facilitating the: (i) Standardization . Diagnoses obtained with similar criteria in different laboratories can be veri- fied. (ii) Sensitivity . Neurophysiological findings in a particular subject under investigation may be compared with a database of normal values to determine whether abnormality exists or not. (iii) Specificity . Findings may be compared with databases derived from patients with known diseases, to evaluate whether they fit a specific diagnosis. (iv) Equivalence . Results from serial examin- ations on the same patient may be compared to decide whether there is evidence of disease progression or of response to treatment. Also, findings obtained from dif- ferent quantitative methods may be contrasted to deter- mine which are most sensitive and specific.
Different methodologies have been developed in com- puter-aided EMG and ENG analysis ranging from simple quantitative measures of the recorded potentials, to more complex knowledge-based and neural network systems that enable the automated assessment of neuromuscular disorders. However, the need still exists for the further advancement and standardization of these method- ologies, especially nowadays with the emerging health telematics technologies which will enable their wider application in the neurophysiological laboratory. The main objective of this Special Issue of Medical Engin- eering & Physics is to provide a snapshot of current activities and methodologies in intelligent data analysis in peripheral neurophysiology.
A total of 12 papers are published in this Special Issue under the following topics: Motor Unit Action Potential (MUAP) Analysis, Surface EMG (SEMG) Analysis, Electroneurography, and Decision Systems. In this intro- duction, the papers are briefly introduced, following a brief review of the major achievements in quantitative electromyography and electroneuropathy
Asymmetry index in muscle activations
Gait asymmetry is typically evaluated using spatio-temporal or joint kinematics parameters. Only a few studies addressed the problem of defining an asymmetry index directly based on muscle activity, extracting parameters from surface electromyography (sEMG) signals. Moreover, no studies used the extraction of the muscle principal activations (activations that are necessary for accomplishing a specific motor task) as the base to construct an asymmetry index, less affected by the variability of sEMG patterns. The aim of this study is to define a robust index to quantitative assess the asymmetry of muscle activations during locomotion, based on the extraction of the principal activations. SEMG signals were analyzed combining Statistical Gait Analysis (SGA) and a clustering algorithm that allows for obtaining the muscle principal activations. We evaluated the asymmetry levels of four lower limb muscles in: (1) healthy subjects of different ages (children, adults, and elderly); (2) different populations of orthopedic patients (adults with megaprosthesis of the knee after bone tumor resection, elderly subjects after total knee arthroplasty and elderly subjects after total hip arthroplasty); and (3) neurological patients (children with hemiplegic cerebral palsy and elderly subjects affected by idiopathic Normal Pressure Hydrocephalus). The asymmetry index obtained for each pathological population was then compared to that of age-matched controls. We found asymmetry levels consistent with the expected impact of the different pathologies on muscle activation during gait. This suggests that the proposed index can be successfully used in clinics for an objective assessment of the muscle activation asymmetry during locomotion
Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review
The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles
Development and optimization of a low-cost myoelectric upper limb prosthesis
Tese de Mestrado Integrado, Engenharia Biomédica e BiofÃsica (Engenharia ClÃnica e Instrumentação Médica), 2022, Universidade de Lisboa, Faculdade de CiênciasIn recent years, the increase in the number of accidents, chronic diseases, such as diabetes, and
the impoverishment of certain developing countries have contributed to a significant increase in
prostheses users. The loss of a particular limb entails numerous changes in the daily life of each user,
which are amplified when the user loses their hand. Therefore, replacing the hand is an urgent necessity.
Developing upper limb prostheses will allow the re-establishment of the physical and motor functions
of the upper limb as well as reduction of the rates of depression. Therefore, the prosthetic industry has
been reinventing itself and evolving. It is already possible to control a prosthesis through the user's
myoelectric signals, control known as pattern recognition control. In addition, additive manufacturing
technologies such as 3D printing have gained strength in prosthetics. The use of this type of technology
allows the product to reach the user much faster and reduces the weight of the devices, making them
lighter. Despite these advances, the rejection rate of this type of device is still high since most prostheses
available on the market are slow, expensive and heavy. Because of that, academia and institutions have
been investigating ways to overcome these limitations. Nevertheless, the dependence on the number of
acquisition channels is still limiting since most users do not have a large available forearm surface area
to acquire the user’s myoelectric signals.
This work intends to solve some of these problems and answer the questions imposed by the
industry and researchers. The main objective is to test if developing a subject independent, fast and
simple microcontroller is possible. Subsequently, we recorded data from forty volunteers through the
BIOPAC acquisition system. After that, the signals were filtered through two different processes. The
first was digital filtering and the application of wavelet threshold noise reduction. Later, the signal was
divided into smaller windows (100 and 250 milliseconds) and thirteen features were extracted in the
temporal domain. During all these steps, the MatLab® software was used. After extraction, three feature
selection methods were used to optimize the classification process, where machine learning algorithms
are implemented. The classification was divided into different parts. First, the classifier had to
distinguish whether the volunteer was making some movement or was at rest. In the case of detected
movement, the classifier would have to, on a second level, try to understand if they were moving only
one finger or performing a movement that involved the flexion of more than one finger (grip). If the
volunteer was performing a grip on the third level, the classifier would have to identify whether the
volunteer was performing a spherical or triad grip. Finally, to understand the influence of the database
on the classification, two methods were used: cross-validation and split validation.
After analysing the results, the e-NABLE Unlimbited arm was printed on The Original Prusa i3
MK3, where polylactic acid (PLA) was used.
This dissertation showed that the results obtained in the 250-millisecond window were better than
the obtained ones in a 100-millisecond window. In general, the best classifier was the K-Nearest
Neighbours (KNN) with k=2, except for the first level that was LDA. The best results were obtained for
the first classification level, with an accuracy greater than 90%. Although the results obtained for the
second and third levels were close to 80%, it was concluded that it was impossible to develop a
microcontroller dependent only on one acquisition channel. These results agree with the anatomical
characteristics since they are originated from the same muscle group. The cross-validation results were
lower than those obtained in the training-test methodology, which allowed us to conclude that the inter variability that exists between the subjects significantly affects the classification performance.
Furthermore, both the dominant and non-dominant arms were used in this work, which also increased
the discrepancy between signals. Indeed, the results showed that it is impossible to develop a
microcontroller adaptable to all users. Therefore, in the future, the best path will be to opt for the
customization of the prototype. In order to test the implementation of a microcontroller in the printed model, it was necessary to design a support structure in Solidworks that would support the motors used
to flex the fingers and Arduino to control the motors. Consequently, the e-NABLE model was re adapted, making it possible to develop a clinical training prototype. Even though it is a training
prototype, it is lighter than those on the market and cheaper.
The objectives of this work have been fulfilled and many answers have been given. However,
there is always space for improvement. Although, this dissertation has some limitations, it certainly
contributed to clarify many of the doubts that still exist in the scientific community. Hopefully, it will
help to further develop the prosthetic industry.Nos últimos anos, o aumento do número de acidentes por doenças crónicas, como, por exemplo,
a diabetes, e o empobrecimento de determinados paÃses em desenvolvimento têm contribuÃdo para um
aumento significativo no número de utilizadores de próteses. A perda de um determinado membro
acarreta inúmeras mudanças no dia-a-dia de cada utilizador. Estas são amplificadas quando a perda é
referente à mão ou parte do antebraço. A mão é uma ferramenta essencial no dia-a-dia de cada ser
humano, uma vez que é através dela que são realizadas as atividades básicas, como, por exemplo, tomar
banho, lavar os dentes, comer, preparar refeições, etc. A substituição desta ferramenta é, portanto, uma
necessidade, não só porque permitirá restabelecer as funções fÃsicas e motoras do membro superior,
como, também, reduzirá o nÃvel de dependência destes utilizadores de outrem e, consequentemente, das
taxas de depressão. Para colmatar as necessidades dos utilizadores, a indústria prostética tem-se
reinventado e evoluÃdo, desenvolvendo próteses para o membro superior cada vez mais sofisticadas.
Com efeito, já é possÃvel controlar uma prótese através da leitura e análise dos sinais mioelétricos do
próprio utilizador, o que é denominado por muitos investigadores de controlo por reconhecimento de
padrões. Este tipo de controlo é personalizável e permite adaptar a prótese a cada utilizador. Para além
do uso de sinais elétricos provenientes do musculo do utilizador, a impressão 3D, uma técnica de
manufatura aditiva, têm ganho força no campo da prostética. Por conseguinte, nos últimos anos os
investigadores têm impresso inúmeros modelos com diferentes materiais que vão desde o uso de
termoplásticos, ao uso de materiais flexÃveis. A utilização deste tipo de tecnologia permite, para além
de uma rápida entrega do produto ao utilizador, uma diminuição no tempo de construção de uma prótese
tornando-a mais leve e barata. Além do mais, a impressão 3D permite criar protótipos mais sustentáveis,
uma vez que existe uma redução na quantidade de material desperdiçado. Embora já existam inúmeras
soluções, a taxa de rejeição deste tipo de dispositivos é ainda bastante elevada, uma vez que a maioria
das próteses disponÃveis no mercado, nomeadamente as mioelétricas, são lentas, caras e pesadas. Ainda
que existam alguns estudos que se debrucem neste tipo de tecnologias, bem como na sua evolução
cientÃfica, o número de elétrodos utilizados é ainda significativo. Desta forma, e, tendo em conta que a
maioria dos utilizadores não possuà uma área de superfÃcie do antebraço suficiente para ser feita a
aquisição dos sinais mioelétricos, o trabalho feito pela academia não se revelou tão contributivo para a
indústria prostética como este prometia inicialmente.
Este trabalho pretende resolver alguns desses problemas e responder às questões mais impostas
pela indústria e investigadores, para que, no futuro, o número de utilizadores possa aumentar, assim
como o seu Ãndice de satisfação relativamente ao produto. Para tal, recolheram-se os sinais mioelétricos
de quarenta voluntários, através do sistema de aquisição BIOPAC. Após a recolha, filtraram-se os sinais
de seis voluntários através de dois processos diferentes. No primeiro, utilizaram-se filtros digitais e no
segundo aplicou-se a transformada de onda para a redução do ruÃdo. De seguida, o sinal foi segmentado
em janelas mais pequenas de 100 e 250 milissegundos e extraÃram-se treze features no domÃnio temporal.
Para que o processo de classificação fosse otimizado, foram aplicados três métodos de seleção de
features. A classificação foi dividida em três nÃveis diferentes nos quais dois algoritmos de
aprendizagem automática foram implementados, individualmente. No primeiro nÃvel, o objetivo foi a
distinção entre os momentos em que o voluntário fazia movimento ou que estava em repouso. Caso o
output do classificador fosse a classe movimento, este teria de, num segundo nÃvel, tentar perceber se o
voluntário estaria a mexer apenas um dedo ou a realizar um movimento que envolvesse a flexão de mais
de que um dedo (preensão). No caso de uma preensão, passava-se ao terceiro nÃvel onde o classificador
teria de identificar se o voluntário estaria a realizar a preensão esférica ou em trÃade. Para todos os nÃveis
de classificação, obtiveram-se resultados para o método de validação cruzada e o método de teste e
treino, sendo que neste, 70% dos dados foram utilizados como conjunto de treino e 30% como teste.
Efetuada a análise dos resultados, escolheu-se um dos modelos da comunidade e-NABLE. O modelo foi
impresso na impressora The Original Prusa i3 MK3S e o material escolhido foi o ácido poliláctico
(PLA). Para que fosse possÃvel testar a implementação de um microcontrolador num modelo que
originalmente depende da flexão do cotovelo realizada pelo utilizador, foi necessário desenhar uma
estrutura de suporte que suportasse, não só os motores utilizados para flexionar os dedos, como, também,
o ArduÃno. O suporte desenhado foi impresso com o mesmo material e com a mesma impressora.
Os resultados obtidos mostraram que a janela de 250 milissegundo foi a melhor e que, regra geral,
o melhor classificador é o K-Nearest Neighbors (KNN) com k=2, com exceção do primeiro nÃvel, em
que o melhor classificador foi o Linear Discriminant Analysis (LDA). Os melhores resultados
obtiveram-se no primeiro nÃvel de classificação onde a accuracy foi superior a 90%. Embora os
resultados obtidos para o segundo e terceiro nÃvel tenham sido próximos de 80%, concluiu-se que não
era possÃvel desenvolver um microcontrolador dependente apenas de um canal de aquisição. Tal era
expectável, uma vez que os movimentos estudados são originados pelo mesmo grupo muscular e a
intervariabilidade dos sujeitos um fator significativo. Os resultados da validação cruzada foram menos
precisos do que os obtidos para a metodologia de treino-teste, o que permitiu concluir que a
intervariabilidade existente entre os voluntários afeta significativamente o processo de classificação.
Para além disso, os voluntários utilizaram o braço dominante e o braço não dominante, o que acabou
por aumentar a discrepância entre os sinais recolhidos. Com efeito, os resultados mostraram que não é
possÃvel desenvolver um microcontrolador que seja adaptável a todos os utilizadores e, portanto, no
futuro, o melhor caminho será optar pela personalização do protótipo. Tendo o conhecimento prévio
desta evidência, o protótipo desenvolvido neste trabalho apenas servirá como protótipo de treino para o
utilizador. Ainda assim, este é bem mais leve que os existentes no mercado e muito mais barato. Nele é
ainda possÃvel testar e controlar alguns dos componentes que no futuro irão fazer parte da prótese
completa, prevenindo acidentes.
Não obstante o cumprimento dos objetivos deste trabalho e das muitas respostas que por ele foram
dadas, existe sempre espaço para melhorias. Dado à limitação de tempo, não foi possÃvel testar o
microcontrolador em tempo-real nem efetuar testes mecânicos de flexibilidade e resistência dos
materiais da prótese. Deste modo, seria interessante no futuro fazer testes de performance em tempo real
e submeter a prótese a condições extremas, para que a tensão elástica e a tensão dos pins sejam testadas.
Para além disso, testar os mecanismos de segurança da prótese quando o utilizador tem de fazer muita
força é fundamental. O teste destes parâmetros evitará a ocorrência de falhas que poderão magoar o
utilizador, bem como estragar os objetos com os quais a prótese poderá interagir. Por fim, é necessário
melhorar o aspeto cosmético das próteses. Para que isso aconteça, poderão ser utilizados polÃmeros com
uma coloração próxima do tom da pele do utilizador. Uma outra forma de melhorar este aspeto, seria
fazer o scanning do braço saudável do utilizador e usar materiais flexÃveis para as articulações e dedos
que, juntamente com uma palma de termoplásticos resistentes e um microcontrolador, permitissem um
movimento bastante natural próximo do biológico.
Em suma, apesar de algumas limitações, este trabalho contribuiu para o esclarecimento de muitas
das dúvidas que ainda existiam na comunidade cientÃfica e ajudará a desenvolver a indústria prostética
Non-Invasive Investigation of Human Foot Muscles Function
Appropriate functioning of the human foot is fundamental for good quality of life. The
intrinsic foot muscles (IFM) are a crucial component of the foot, but their natural
behaviour and contribution to good foot health is currently poorly understood.
Recording muscle activation from IFM has been attempted with invasive techniques, but
these generally only allow assessment of one muscle at a time and are not much used
in many clinical populations (e.g. children, patients with peripheral neuropathy or on
blood thinning medication). Here a novel application of multi-channel surface
electromyography (sEMG) electrodes is presented to non-invasively, record sEMG and
quantify activation patterns of IFMs from across the plantar region of the foot.
sEMG (13×5 array), kinematics and force plate data were recorded from 30 healthy adult
volunteers who completed six postural balance tasks (e.g. bipedal stance, one-foot
stance, two-foot tip-toe). Linear (amplitude based) and non-linear (entropy based)
methodologies were used to evaluate the physiological features of the sEMG, the
patterns of activation, the association with whole body and foot biomechanics and the
neuromuscular drive to the IFM.
EMG signals features (amplitude and frequency) were shown to be in the physiological
ranges reported in the literature (Basmajian and De Luca, 1985), with spatially clustered
patterns of high activation corresponding to the Flexor digitorum brevis muscle. IFMs
responded differently based on the direction of postural sway, with greater activations
associated with sways in the mediolateral direction. Entropy based, non-linear analysis
revealed that neuromuscular drive to IFM depends on the balance demand of the
postural task, with greater drive evident for more challenging tasks (i.e. standing on tiptoe). Combining non-invasive measures of IFM activation and entropy based assessment
of temporal organisation (or structure) of EMG signal variability is therefore revealing of
IFM function and will enable a more detailed assessment of IFM function across healthy
and clinical populations
Identification of periodic bursts in surface EMG: Applications to the erector spinae muscles of sitting violin players
Objective: This work compares two known and one novel techniques for the detection of surface EMG (sEMG) quasi-periodic burst-like signals and the estimation of their frequency. The novel method (ES) is based on the spectral analysis of the envelope signal, the other two methods use a fixed (FT) or automatically selected optimal threshold (OT). Methods: The methods are compared using both simulated signals and samples of High Density sEMG experimental signals collected using electrode arrays applied to the erector spinae muscles of violinists. Results: The ES method does not require thresholds. It detects presence/absence of bursts and their frequency, even in cases of a few missing bursts. It does not provide their duration. The FT method requires the selection of a fixed threshold value, estimates burst duration but is applicable only if bursts are present. The OT method identifies an optimal threshold, estimates burst duration but behaves irregularly when bursts are small or absent. Conclusions: The ES method provides the estimates closest to those of an expert human counter and is not sensitive to amplitude fluctuations. It is suitable when the general bursts periodicity is of interest even if some bursts may be missing. The FT and OT methods are sensitive to amplitude fluctuations and identify random threshold crossings as bursts even when burst activity is absent. Significance: Postural muscles are often activated in a burst-like fashion. The proposed ES method identifies presence/absence of bursts and their frequency, which is important for studying the neurophysiological mechanism generating them
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