putEMG -- a surface electromyography hand gesture recognition dataset

In this paper, we present a putEMG dataset intended for evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches, and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject's forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. putEMG dataset is available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license at: https://www.biolab.put.poznan.pl/putemg-dataset/. The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. Accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin's and Du's feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. Presented dataset can be used as a benchmark for various classification methods, evaluation of electrode localisation concepts, or development of classification methods invariant to user-specific features or electrode displacement

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

EEG Processing for Fast and Efficient Analysis

Hobbies on the human body have never diminished and explore on it has never ceased since hundreds of years back. An investigation of EEG for examination of the creation of the cerebrum and intellectual methods for biomedical applications is progressing theme for exploration. For the legitimate conclusion of numerous neurological maladies, for example, epilepsy, tumors, issues connected with injury exact examination of EEG signs is key. Moreover, to upgrade the viability of Brain Computer Interface (BCI) frameworks it is obliged to focus systems for expanding the sign to-commotion proportion (SNR) of the watched EEG signals. EEG measured by setting cathodes on scalp generally has little abundancy in microvolts, so the examination of EEG information and the extraction of data from this information is a troublesome issue. This issue gets to be more entangled by the presentation of antiques, for example, line commotion from the force lattice, eye flickers, eye developments, pulse, breathing, and other muscle action. Discrete wavelet change offers a viable answer for denoising nonstationary EEG signals. In this paper, wavelet denoising is connected to EEG obtained amid performing diverse mental assignments. The initial decay of the EEG signal from database utilizing five unique sorts of wavelets viz. Haar, Daubechies, Symlet, Coiflet,Dmey is completed. In denoising process, the thresholding system utilized for expelling clamor from sullied EEG. Our goal to discover best suitable wavelet sort to specific errand which gave better execution measure, for example, bigger sign to-Noise Ratio (SNR). The EEG database from the Colorado state college is utilized for experimentation

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 steer—a 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

Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sèche

Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à 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 user’s 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 system’s 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 classifier’s robustness to long-term changes

Early Disease Detection by Extracting Features of Biomedical Signals

Elderly people face a lot of health problems in day to day life due to old age and so many reasons. Therefore a regular health check-up is needed for them which is much more expensive and cannot be afforded by many people. Again the diagnosis is much more complicated to understand and in many cases there is a chance of mistreatment. There is another chance of delay in the detection of disease and late treatment causing risk in their lives. So, the disease should be detected in the early stage for lower cost and lower risk in life. The present work is related to the different physiological parameters of a human being that are to be measured to accurately diagnose the related disease. Though there are numerous physiological parameters, this work emphasizes on some of the most common physiological parameters such as blood pressure, heart rate and ECG which are of primary importance to elderly people. Accurate measurement and analysis of these parameters can lead to diagnose of several lethal disease. In this work, the method of measurement and analysis of these physiological parameters are described. The simulation, processing and analyses of these signals are also done in the work. The prime objective of the research work is to analyze and extract the features of ECG signal and blood pressure signal for early diagnosis of life threatening diseases

A Comparative Analysis Of Wavelet Families For The Classification Of Finger Motions

Wavelet transform (WT) has been widely used in biomedical, rehabilitation and engineering applications. Due to the natural characteristic of WT, its performance is mostly depending on the selection of mother wavelet function. A proper mother wavelet ensures the optimum performance; however, the selection of mother wavelet is mostly empirical and varies according to dataset. Hence, this paper aims to investigate the best mother wavelet of discrete wavelet transform (DWT) and wavelet packet transform (WPT) in the classification of different finger motions. In this study, twelve mother wavelets are evaluated for both DWT and WPT. The electromyography (EMG) data of 12 finger motions are acquired from online database. Four useful features are extracted from each recorded EMG signal via DWT and WPT transformation. Afterward, support vector machine (SVM) and linear discriminate analysis (LDA) are employed for performance evaluation. Our experimental results demonstrate Bior3.3 to be the most suitable mother wavelet in DWT. On the other hand, WPT with Bior2.2 overtakes other mother wavelets in the classification of finger motions. The results obtained suggest that Biorthogonal families are more suitable for accurate EMG signals classification

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Towards Amyotrophic Lateral Sclerosis Interpretable Diagnosis Using Surface Electromyography

Amyotrophic Lateral Sclerosis (ALS) is a fast-progressing disease with no cure. It is diagnosed through the assessment of clinical exams, such as needle electromyography, which measures themuscles’ electrical activity by inserting a needle into themuscle tissue. Nevertheless, surface electromyography (SEMG) is emerging as a more practical and less painful alternative. Even though these exams provide relevant information regarding the electric structures conducted in the muscles, ALS symptoms are similar to those of other neurological disorders, preventing a faster detection of the disease. This dissertation focuses on implementing and analyzing innovative SEMG features related to the morphology of the functional structures present in the signal. To assess the efficiency of these features, a framework is proposed, aiming to distinguish healthy from pathological signals through the use of a classification algorithm. The classification task was performed using SEMG signals acquired from the upper limb muscles of healthy and ALS subjects. The results show the utility of employing the proposed set of features for ALS diagnosis, with an F1 Score higher than 80% in most experimental conditions. The novel features improved the model’s overall performance when combined with other state-of-art SEMG features and also demonstrated efficiency when used individually. These outcomes are of significant importance in supporting the use of SEMG as a complementary diagnosis exam. The proposed features demonstrate promising contributions for better and faster detection of ALS and increased classification interpretabilityA Esclerose Lateral Amiotrófica (ELA) é uma doença incurável de progressão rápida. O seu diagnóstico é feito através da avaliação de exames clínicos como a eletromiografia de profundidade, que mede a atividade elétrica muscular com agulhas inseridas no músculo. No entanto, a eletromiografia de superfície (SEMG) surge como uma alternativa mais prática e menos dolorosa. Embora ambos os exames forneçam informações relevantes sobre as estruturas elétricas conduzidas nos músculos, os sintomas da ELA são semelhantes aos de outras doenças neurológicas, impedindo uma identificação mais precoce da doença. Esta dissertação foca-se na implementação e análise de atributos inovadores de SEMG relacionados com a morfologia das estruturas funcionais presentes no sinal. Para avaliar a eficiência destes atributos, é proposto um framework, com o objetivo de distinguir sinais saudáveis e sinais patológicos através de um algoritmo de classificação. A tarefa de classificação foi realizada utilizando sinais de SEMG adquiridos dos músculos dos membros superiores de indivíduos saudáveis e com ELA. Os resultados demonstram a utilidade do conjunto de atributos proposto para o diagnóstico de ELA, com uma métrica de classificação F1 superior a 80% na maioria das condições experimentais. Os novos atributos melhoraram o desempenho geral do modelo quando combinados com outros atributos de SEMG do estado da arte, e também se comprovaram eficientes quando aplicados individualmente. Estes resultados são de grande importância na justificação da aplicabilidade da SEMG como um exame complementar de diagnóstico da ELA. Os atributos apresentados demonstram ser promissores para um melhor e mais rápido diagnóstico, e facilitam a explicação dos resultados da classificação devido à sua interpretabilidade

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting