185 research outputs found

    Real-time classification of finger movements using two-channel surface electromyography

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    The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes the recognition system for decoding the individual and combined finger movements using two channels surface EMG. The proposed system utilizes Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The experimental results show that the proposed system was able to classify ten classes of individual and combined finger movements, offline and online with accuracy 97.96 % and 97.07% respectively

    Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

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    Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

    Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

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    This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    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

    Evaluation of surface EMG-based recognition algorithms for decoding hand movements

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    Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins\u27 set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands

    putEMG -- a surface electromyography hand gesture recognition dataset

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    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

    AN INVESTIGATION OF ELECTROMYOGRAPHIC (EMG) CONTROL OF DEXTROUS HAND PROSTHESES FOR TRANSRADIAL AMPUTEES

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    In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services.There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.Ministry of Higher Education and Scientific Research and Baghdad University- Baghdad/Ira
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