202 research outputs found
Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors
Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
In recent years, deep learning algorithms have become increasingly more
prominent for their unparalleled ability to automatically learn discriminant
features from large amounts of data. However, within the field of
electromyography-based gesture recognition, deep learning algorithms are seldom
employed as they require an unreasonable amount of effort from a single person,
to generate tens of thousands of examples.
This work's hypothesis is that general, informative features can be learned
from the large amounts of data generated by aggregating the signals of multiple
users, thus reducing the recording burden while enhancing gesture recognition.
Consequently, this paper proposes applying transfer learning on aggregated data
from multiple users, while leveraging the capacity of deep learning algorithms
to learn discriminant features from large datasets. Two datasets comprised of
19 and 17 able-bodied participants respectively (the first one is employed for
pre-training) were recorded for this work, using the Myo Armband. A third Myo
Armband dataset was taken from the NinaPro database and is comprised of 10
able-bodied participants. Three different deep learning networks employing
three different modalities as input (raw EMG, Spectrograms and Continuous
Wavelet Transform (CWT)) are tested on the second and third dataset. The
proposed transfer learning scheme is shown to systematically and significantly
enhance the performance for all three networks on the two datasets, achieving
an offline accuracy of 98.31% for 7 gestures over 17 participants for the
CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw
EMG-based ConvNet. Finally, a use-case study employing eight able-bodied
participants suggests that real-time feedback allows users to adapt their
muscle activation strategy which reduces the degradation in accuracy normally
experienced over time.Comment: Source code and datasets available:
https://github.com/Giguelingueling/MyoArmbandDatase
Multikernel convolutional neural network for sEMG based hand gesture classification
openIl riconoscimento dei gesti della mano Ăš un argomento ampiamente discusso in letteratura, dove vengono analizzate diverse tecniche sia in termini di tipi di segnale in ingresso che di algoritmi. Tra i piĂč utilizzati ci sono i segnali elettromiografici (sEMG), giĂ ampiamente sfruttati nelle applicazioni di interazione uomo-macchina (HMI). Determinare come decodificare le informazioni contenute nei segnali EMG in modo robusto e accurato Ăš un problema chiave per il quale Ăš urgente trovare una soluzione.
Recentemente, molti incarichi di riconoscimento dei pattern EMG sono stati affrontati utilizzando metodi di deep learning. Nonostante le elevate prestazioni di questi ultimi, le loro capacitĂ di generalizzazione sono spesso limitate dall'elevata eterogeneitĂ tra i soggetti, l'impedenza cutanea, il posizionamento dei sensori, ecc.
Inoltre, poiché questo progetto Ú focalizzato sull'applicazione in tempo reale di protesi, ci sono maggiori vincoli sui tempi di risposta del sistema che riducono la complessità dei modelli. In questa tesi Ú stata testata una rete neurale convoluzionale multi-kernel su diversi dataset pubblici per verificare la sua generalizzabilità . Inoltre, Ú stata analizzata la capacità del modello di superare i limiti inter-soggetto e inter-sessione in giorni diversi, preservando i vincoli legati a un sistema embedded. I risultati confermano le difficoltà incontrate nell'estrazione di informazioni dai segnali emg; tuttavia, dimostrano la possibilità di ottenere buone prestazioni per un uso robusto di mani prostetiche. Inoltre, Ú possibile ottenere prestazioni migliori personalizzando il modello con tecniche di transfer learning e di adattamento al dominio.Hand gesture recognition is a widely discussed topic in the literature, where different techniques are analyzed in terms of both input signal types and algorithms. Among the most widely used are electromyographic signals (sEMG), which are already widely exploited in human-computer interaction (HMI) applications. Determining how to decode the information contained in EMG signals robustly and accurately is a key problem for which a solution is urgently needed.
Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. Despite their high performance, their generalization capabilities are often limited by high heterogeneity among subjects, skin impedance, sensor placement, etc.
In addition, because this project is focused on the real-time application of prostheses, there are greater constraints on the system response times that reduce the complexity of the models. In this thesis, a multi-kernel convolutional neural network was tested on several public datasets to verify its generalizability. In addition, the model's ability to overcome inter-subject and inter-session constraints on different days while preserving the constraints associated with an embedded system was analyzed. The results confirm the difficulties encountered in extracting information from emg signals; however, they demonstrate the possibility of achieving good performance for robust use of prosthetic hands. In addition, better performance can be achieved by customizing the model with transfer learning and domain-adaptationtechniques
Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sÚche
Depuis plusieurs annĂ©es la robotique est vue comme une solution clef pour amĂ©liorer la qualitĂ© de vie des personnes ayant subi une amputation. Pour crĂ©er de nouvelles prothĂšses intelligentes qui peuvent ĂȘtre facilement intĂ©grĂ©es Ă la vie quotidienne et acceptĂ©e par ces personnes, celles-ci doivent ĂȘtre non-intrusives, fiables et peu coĂ»teuses. LâĂ©lectromyographie de surface fournit une interface intuitive et non intrusive basĂ©e sur lâactivitĂ© musculaire de lâutilisateur permettant dâinteragir avec des robots. Cependant, malgrĂ© des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilitĂ©, car ils ne sont pas robustes face au bruit Ă court terme (par exemple, petit dĂ©placement des Ă©lectrodes, fatigue musculaire) ou Ă long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon pĂ©riodique. Lâobjectif de mon projet de recherche est de proposer une interface myoĂ©lectrique humain-robot basĂ© sur des algorithmes dâapprentissage par transfert et dâadaptation de domaine afin dâaugmenter la fiabilitĂ© du systĂšme Ă long-terme, tout en minimisant lâintrusivitĂ© (au niveau du temps de prĂ©paration) de ce genre de systĂšme. Lâaspect non intrusif est obtenu en utilisant un bracelet Ă Ă©lectrode sĂšche possĂ©dant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-mĂȘme) conception et a Ă©tĂ© rĂ©alisĂ© durant mon doctorat. Ă lâheure dâĂ©crire ces lignes, le 3DC Armband est le bracelet sans fil pour lâenregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des Ă©lectrodes Ă base de gel qui nĂ©cessitent un rasage de lâavant-bras, un nettoyage de la zone de placement et lâapplication dâun gel conducteur avant lâutilisation, le brassard du 3DC peut simplement ĂȘtre placĂ© sur lâavant-bras sans aucune prĂ©paration. Cependant, cette facilitĂ© dâutilisation entraĂźne une diminution de la qualitĂ© de lâinformation du signal. Cette diminution provient du fait que les Ă©lectrodes sĂšches obtiennent un signal plus bruitĂ© que celle Ă base de gel. En outre, des mĂ©thodes invasives peuvent rĂ©duire les dĂ©placements dâĂ©lectrodes lors de lâutilisation, contrairement au brassard. Pour remĂ©dier Ă cette dĂ©gradation de lâinformation, le projet de recherche sâappuiera sur lâapprentissage profond, et plus prĂ©cisĂ©ment sur les rĂ©seaux convolutionels. Le projet de recherche a Ă©tĂ© divisĂ© en trois phases. La premiĂšre porte sur la conception dâun classifieur permettant la reconnaissance de gestes de la main en temps rĂ©el. La deuxiĂšme porte sur lâimplĂ©mentation dâun algorithme dâapprentissage par transfert afin de pouvoir profiter des donnĂ©es provenant dâautres personnes, permettant ainsi dâamĂ©liorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de prĂ©paration nĂ©cessaire pour utiliser le systĂšme. La troisiĂšme phase consiste en lâĂ©laboration et lâimplĂ©mentation des algorithmes dâadaptation de domaine et dâapprentissage faiblement supervisĂ© afin de crĂ©er un classifieur qui soit robuste au changement Ă long terme.For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a userâs muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the systemâs accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifierâs robustness to long-term changes
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steerâa gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control
After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis
Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses
Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems
Madala maksumusega elektromĂŒograafide rakendatavus ergonoomikalises hindamises
A thesis
for applying for the degree of Doctor of Philosophy
in Engineering Sciences.Every year a considerable amount of gross domestic product in several countries is lost due to work-related musculoskeletal disorders (WMSDs). Thus, one of the goals of ergonomics is to prevent WMSDs. A body of knowledge required to prevent WMSDs has existed for decades; however, the exploitation of this knowledge is hindered by the shortcomings in the risk assessment methods. As a rule, objective methods should be preferred to subjective methods, though often access to objective methods is restricted by the cost of the apparatus. The potential to make one of such devices more accessible by reducing the costs was investigated in the thesis. The thesis focused on the electromyograph â a device to study and monitor the electrical activity produced by skeletal muscles. Nowadays one can assemble an electromyograph from low-cost semi-universal components; however, the functionality and usability of such a device is unknown. At first the technical characteristics of components that can be used to assemble an electromyograph were evaluated. Then the electromyographs were assembled and tested in the laboratory and in the field. The results showed that the low-cost electromyographs may be partially utilised in ergonomic risk assessment; however, the use of such equipment in comparison to commercial high-cost apparatus increases the demands on user knowledge, skills and time expenditure. On the other hand, the functionality of the do-it-yourself electromyograph may exceed the commercial device.Tööga seotud luu- ja lihaskonna ĂŒlekoormushaiguste tĂ”ttu kaotavad riigid igal aastal mĂ€rkimisvÀÀrse osa sisemajanduse kogutoodangust. SeetĂ”ttu on ĂŒheks ergonoomika eesmĂ€rgiks luu- ja lihaskonna ĂŒlekoormushaiguste ennetamine. Teadmised töötaja ĂŒlekoormuse ennetamiseks on olemas juba aastakĂŒmneid. Paraku takistavad teadmiste tĂ”husat rakendamist puudused riskihindamise meetodites. Riskide hindamisel tuleb subjektiivsetele meetoditele eelistada objektiivseid meetodeid, kuid sageli piirab objektiivsete meetodite kasutamist mÔÔteseadmete maksumus. Doktoritöös uuriti ĂŒhe sellist liiki mÔÔteseadme, lihaste elektrilise aktiivsuse uurimiseks mĂ”eldud seireseadme ehk elektrimĂŒograafi kĂ€ttesaadavuse ja rakendamise suurendamise vĂ”imalust seadme maksumuse vĂ€hendamisega. NĂŒĂŒdisajal on vĂ”imalus elektromĂŒograafe kokku panna madala maksumusega ja pool-universaalsetest komponentidest. Samas pole selge, milline on sellisel viisil valmistatud elektromĂŒograafi funktsionaalsus ja kasutatavus. Doktoritöös hinnati esmalt elektromĂŒograafi madala maksumusega komponentide tehnilisi omadusi ning seejĂ€rel katsetati koostatud elektromĂŒograafe laboris ja töökeskkonnas. Doktoritöö andis kinnitust, et madala maksumusega elektromĂŒograafe on vĂ”imalik riskihindamisel osaliselt rakendada, kuid selliste seadmete kasutamine eeldab riskihindajalt pĂ”hjalikumaid teadmisi ja oskusi ning suuremat ajakulu kui kallite kommertsseadmete kasutamine. Samas vĂ”ib spetsialisti kokkupandud elektromĂŒograafi funktsionaalsus kommertsseadmeid ĂŒletada.Publication of this thesis is supported by the Estonian University of
Life Sciences. This research was supported by European Regional
Development Fundâs Doctoral Studies and Internationalisation
Programme DoR
- âŠ