Recently, surface electromyography (sEMG) emerged as a novel biometric authentication method. Since EMG system parameters, such as the feature extraction methods and the number of channels, have been known to affect system performances, it is important to investigate these effects on the performance of the sEMG-based biometric system to determine optimal system parameters. In this study, three robust feature extraction methods, Time-domain (TD) feature, Frequency Division Technique (FDT), and Autoregressive (AR) feature, and their combinations were investigated while the number of channels varying from one to eight. For these system parameters, the performance of sixteen static wrist and hand gestures was systematically investigated in two authentication modes: verification and identification. The results from 24 participants showed that the TD features significantly (p<0.05) and consistently outperformed FDT and AR features for all channel numbers. The results also showed that the performance of a four-channel setup was not significantly different from those with higher number of channels. The average equal error rate (EER) for a four-channel sEMG verification system was 4% for TD features, 5.3% for FDT features, and 10% for AR features. For an identification system, the average Rank-1 error (R1E) for a four-channel configuration was 3% for TD features, 12.4% for FDT features, and 36.3% for AR features. The electrode position on the flexor carpi ulnaris (FCU) muscle had a critical contribution to the authentication performance. Thus, the combination of the TD feature set and a four-channel sEMG system with one of the electrodes positioned on the FCU are recommended for optimal authentication performance.
Improving the clinical utility of lower-limb surface electromyography (sEMG) by quantifying and correcting for location changes in inter-session recordings
School of Biomedical Engineering, University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；Centre for Aging SMART, University of British Columbia（不列颠哥伦比亚大学老龄化SMART中心）；Focal Lines Technologies（Focal Lines科技有限公司）
SSEMG-Net: A Spectrogram-Based Mamba Network for Surface Electromyography Denoising
National Taiwan University（国立台湾大学）；Academia Sinica（中央研究院）；Stevens Institute of Technology（史蒂文斯理工学院）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）
Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNNLSTM hybrid framework to fully explore the temporal-spatial informatio…
TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement
National Taiwan University（台湾大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；National Taiwan University of Science and Technology（台湾科技大学）；Academia Sinica（中央研究院）；Chung Yuan Christian University（中原大学）
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University（国立台湾大学）；University of Palermo（巴勒莫大学）；Academic Sinica（中央研究院）；Research Center for Information Technology Innovation（资讯科技创新研究中心）
A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University（国立台湾大学）；University of Massachusetts（马萨诸塞大学）；National Institute of Information and Communications Technology（信息通信研究机构）；Academia Sinica（中央研究院）
In this paper, a different approach on the use of the ADS1299 (an analog front-end with features for electroencephalogram and electrocardiography signal acquisition) is considered, proposing the development of a surface electromyography (sEMG) device. The main features of the device include simultaneous recordings of eight muscular channels, wireless transmission and virtual instrumentation with the use of LabVIEWTM software. The proposed sEMG device contains a specifically designed protocol to accommodate data transmission by reducing the data size while still delivering adequate resolution (…
Deep convolutional neural networks (CNNs) are appealing for the purpose of classification of hand movements from surface electromyography (sEMG) data because they have the ability to perform automated person-specific feature extraction from raw data. In this paper, we make the novel contribution of proposing and evaluating a design for the early processing layers in the deep CNN for multichannel sEMG. Specifically, we propose a novel temporal-to-spatial (TtS) CNN architecture, where the first layer performs convolution separately on each sEMG channel to extract temporal features. This is motiv…
Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 elect…
Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize multi-length codes or passwords (for example, by performing different gestures). However, current EMG-based biometric research has two critical limitations: 1) a small subject pool, compared to other more established biometric traits, and 2) limited to single-session or single-d…
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning
University of California Los Angeles (UCLA)（加州大学洛杉矶分校（UCLA））；New York University (NYU)（纽约大学（NYU））；NYU WIRELESS（纽约大学无线研究中心）；NYU Center for Urban Science and Progress (CUSP)（纽约大学城市科学与进步中心（CUSP））
In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five N…
Assessing Neuromorphic Computing for Fingertip Force Decoding from Electromyography
Department of Electrical and Computer Engineering, NC State University, USA（电气与计算机工程系，北卡罗来纳州立大学）；Lampe Joint Department of Biomedical Engineering, NC State University / University of North Carolina at Chapel Hill, USA（拉姆佩联合生物医学工程系，北卡罗来纳州立大学 / 北卡罗来纳大学教堂山分校）
National Taiwan University（国立台湾大学）；Academia Sinica（中央研究院）；National Taiwan University of Science and Technology（国立台湾科技大学）
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart. Previous studies have developed and proposed various methods, such as high-pass filtering, template subtraction and so forth. However, these methods remain limited by the requirement of reference signals and distortion of original sEMG. This study proposed a novel denoising method to eliminate ECG artifacts from the single-channel sEMG signals using fully convolutional networks (FCN). The proposed method adopts a denoise autoencode…
The probability density function (pdf) of surface Electromyography (sEMG) signals follows any one of the standalone standard distributions: the Gaussian or the Laplacian. Further, the choice of the model is dependent on muscle contraction force (MCF) levels. Hence, a unified model is proposed which explains the statistical nature of sEMG signals at different MCF levels. In this paper, we propose the Laplacian Gaussian Mixture (LGM) model for the signals recorded from upper limbs. This model is able to explain the sEMG signals from different activities corresponding to different MCF levels. The…
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University（国立台湾大学）；National Taipei University of Technology（国立台北科技大学）；National Taiwan University of Science and Technology（国立台湾科技大学）；Academia Sinica（中央研究院）
University of Belgrade（贝尔格莱德大学）；School of Electrical Engineering（电气工程学院）；Innovation Center, University of Belgrade（贝尔格莱德大学创新中心）；Tecnalia Serbia Ltd.（西班牙泰克纳利亚塞尔维亚有限公司）
Repositioning of recording electrode array across repeated electromyography measurements may result in a displacement error in hand movement classification systems. In order to examine if the classifier re-training could reach satisfactory results when electrode array is translated along or rotated around subject's forearm for varying number of features, we recorded surface electromyography signals in 10 healthy volunteers for three types of grasp and 6 wrist movements. For feature extraction we applied principal component analysis and the feature set size varied from one to 8 principal compon…
The grip force required to handle an object depends on the object's mass and the friction coefficient of its surface. The control of grip force in myoelectric prosthesis is crucial for handling objects adequately. In the current paper we propose a new method for improving the proportional and continuous grasping force estimation to improve control systems for myoelectric prosthesis based on surface electromyography (sEMG) recordings. For this purpose, we develop an approach based on multivariable system identification in the state-space (SS) and continuous force estimation with Kalman Filter (…
Integrated Sensing and Communications for Real-time Avatar Control in XR over 5G
IDLab, University of Antwerp - imec（ID实验室，安特卫普大学 - imec）；IDLab, Ghent University - imec（ID实验室，根特大学 - imec）；Lighting Technology Lab, KU Leuven（照明技术实验室，鲁汶大学）
Surface electromyography (sEMG) is becoming exceeding useful in applications involving analysis of human motion such as in human-machine interface, assistive technology, healthcare and prosthetic development. The proposed work presents a novel dual stage classification approach for classification of grasping gestures from sEMG signals. A statistical assessment of these activities is presented to determine the similar characteristics between the considered activities. Similar activities are grouped together. In the first stage of classification, an activity is identified as belonging to a group…
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG Signals
New York University Abu Dhabi（纽约大学阿布扎比分校）；New York University（纽约大学）；Center for AI and Robotics (CAIR)（人工智能与机器人中心）；NYUAD Research Institute（纽约大学阿布扎比分校研究院）
