With the aim to control a multiple degrees of freedom electromechanical devices, e.g., assistive robots, powered wheelchair, etc., this paper proposes a real-time multichannel surface electromyography classification scheme based on the coordination or synergies between a functional group of muscles: biceps brachii, triceps brachii, pronator teres, and brachioradialis. The muscular synergy is evaluated by the analysis of a multivariate function, composed by the four corresponding neuromuscular activation functions, and the cross-correlation matrix of muscular force estimated through the root mean square (RMS) value of sEMG amplitude. The resulting features from the training set were used to train an artificial neural network with classification accuracy up of 90%.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/IEMBS.2010.5626679 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Metastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.
J Thorac Imaging
September 2024
School of Computer Science and Engineering, The Hebrew University of Jerusalem.
Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.
Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.
Triply periodic minimal surfaces for thermo-mechanical protection.
Sci Rep
January 2025
Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.
Triply periodic minimal surface (TPMS) metamaterials show promise for thermal management systems but are challenging to integrate into existing packaging with strict mechanical requirements. Composite TPMS lattices may offer more control over thermal and mechanical properties through material and geometric tuning. Here, we fabricate copper-plated, 3D-printed triply periodic minimal surface primitive lattices and evaluate their suitability for battery thermal management systems.
View Article and Find Full Text PDFMulti-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network.
Sensors (Basel)
December 2024
Institute of Applied Physics, Jiangxi Academy of Sciences, Nanchang 330000, China.
Although approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these problems, this study combines traditional visual detection methods and deep neural network technology to propose a transfer learning multi-channel fusion decision network without significantly increasing the number of network layers or the structural complexity. Each channel of the network is designed according to the characteristics of different types of defects.
View Article and Find Full Text PDFLiquid-Metal-Based Multichannel Strain Sensor for Sign Language Gesture Classification Using Machine Learning.
ACS Appl Mater Interfaces
January 2025
Centre for Robotics and Automation, Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China.
Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates has been a challenge due to high surface tension. In this paper, a new method is proposed to overcome the difficulties in fabricating liquid-state strain sensors.
View Article and Find Full Text PDFA Leadfield-Free Optimization Framework for Transcranially Applied Electric Currents.
bioRxiv
December 2024
Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark.
Background: Transcranial Electrical Stimulation (TES), Temporal Interference Stimulation (TIS), Electroconvulsive Therapy (ECT) and Tumor Treating Fields (TTFields) are based on the application of electric current patterns to the brain.
Objective: The optimal electrode positions, shapes and alignments for generating a desired current pattern in the brain vary between persons due to anatomical variability. The aim is to develop a flexible and efficient computational approach to determine individually optimal montages based on electric field simulations.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!