Effect of the sEMG electrode (re)placement and feature set size on the hand movement recognition
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）
Nadica Miljković, Milica S. Isaković
备注：
19 pages, 4 figures, 5 tables
发表信息：
Nadica Miljković, Milica S. Isaković, Effect of the sEMG electrode (re)placement and feature set size on the hand movement recognition, Biomedical Signal Processing and Control, Volume 64, 2021, 102292, ISSN 1746-8094
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 components. We compared results of re-trained classifier with results from leave-one-out cross-validation classification procedure for three classifiers: LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis), and ANN (Artificial Neural Network). Our results showed that there was no significant difference in classification accuracy when the array electrode was repositioned indicating successful classification re-training and optimal feature set selection. The results also indicate expectedly that the number of principal components plays a key role for acceptable classification accuracy ~90 %. For the largest dataset (9 hand movements), LDA and QDA outperformed ANN, while for three grasping movements ANN showed promising results. Interestingly, we showed that interaction between electrode array position and the feature set size is not statistically significant. This study emphasizes the importance of testing the interaction of factors that influence classification accuracy and classifier selection altogether with their impact independently in order to establish guiding principles for design of hand movement recognition system. Data recorded for this study are stored on Zenodo repository (doi: 10.5281/zenodo.4039550).
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）
Concordia University（康考迪亚大学）；New York University（纽约大学）；University of Calgary（卡尔加里大学）
Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a compact deep learning framework referred to as the CT-HGR, which employs a vision transformer network to conduct hand gesture recognition using highdensity sEMG (HD-sEMG) signals. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while deali…
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 var…
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…
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…
User Training with Error Augmentation for Electromyogram-based Gesture Classification
Northeastern University（东北大学）；Khoury College of Computer Sciences（科里计算机科学学院）；Meta Reality Labs Research（Meta现实实验室）；Institute for Experiential AI（体验式人工智能研究院）；Department of Physical Therapy, Movement, and Rehabilitation Sciences（物理治疗、运动与康复科学系）
A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for electroencephalography (EEG) recordings, which require the placement of multiple electrodes in contact with the scalp. In this work, we propose to identify the optimal wearable EEG electrode set-up, in terms of minimal number of electrodes, comfortable location and performance, for EEG-based event detection and monitoring. By relying on the demonstrated power of autoencoder (AE) networks to learn latent representations …
This paper explores a new sequential selection framework for the optimal sensor placement (OSP) in Electrocardiography imaging networks (ECGI). The proposed methodology incorporates the use a recent experimental design method for the sequential selection of landmarkings on biological objects, namely, Gaussian process landmarking (GPLMK) for better exploration of the candidate sensors. The two experimental design methods work as a source of the training and the validation locations which is fitted using a spatiotemporal Gaussian process (STGP). The STGP is fitted using the training set to predi…
Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer
UCL Institute of Health Informatics（伦敦大学学院健康信息学研究所）；Wellcome Centre for Human Neuroimaging（惠康人类神经影像学中心）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经病学研究所）；Great Ormond Street Hospital（大奥蒙德街医院）；UCL Institute of Child Health（伦敦大学学院儿童健康研究所）；UMC Utrecht Brain Center（乌得勒支大学医学中心脑中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Stichting Epilepsie Instellingen Nederland (SEIN)（荷兰癫痫机构基金会）；Columbia University（哥伦比亚大学）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会）
