A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography
Author:
Affiliation:
1. College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
2. School of Advanced Manufacturing, Fuzhou University, Fuzhou, China
3. Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
Funder
S&T Department of Fujian Province
National Key R&D Program
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation
Link
http://xplorestaging.ieee.org/ielx7/7397573/10048702/10042170.pdf?arnumber=10042170
Reference48 articles.
1. The relation between chaotic feature of surface EEG and muscle force: Case study report;rahatabad;Journal of medical signals and sensors,2021
2. Electrical impedance myography: Background, current state, and future directions
3. Pilot Study for Grip Force Prediction Using Neural Signals from Different Brain Regions
4. EEG source localization using spatio-temporal neural network
5. Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach
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2. Advancements and Challenges in Electrical Impedance Myography (EIM): A Comprehensive Overview of Technology Development, Applications in Sports Health, and Future Directions;IEEE Journal of Microwaves;2024
3. Comparing the Effectiveness of EMG and Electrical Impedance myography Measurements for Controlling Prosthetics;2023 IEEE 4th International Multidisciplinary Conference on Engineering Technology (IMCET);2023-12-12
4. Low Back Muscle Fatigue Monitoring Using Proprietary Electrical Impedance Myography System;2023 IEEE Biomedical Circuits and Systems Conference (BioCAS);2023-10-19
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