Active aging prediction from muscle electrical activity using HD-sEMG signals and machine learning-Reference-Cited by-同舟云学术

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Active aging prediction from muscle electrical activity using HD-sEMG signals and machine learning

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)
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Author:

Sid'El Moctar Sidi Mohamed¹,Diab Ahmad¹,Rida Imad¹,Kinugawa Kiyoka²,Boudaoud Sofiane¹

Affiliation:

1. Alliance Sorbonne Université, Université de Technologie de Compiégnez,Laboratoire de Biomécanique et de Bioingénierie UMR CNRS,Compiegne,France,7338

2. Sorbonne Université,CNRS, UMR Biological Adaptation and Aging, AP-HP, Charles Foix Hospital, Functional Exploration Unit for Older Patients,Ivry-sur-Seine,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10178426/10178704/10178845.pdf?arnumber=10178845

Reference24 articles.

1. Fast generation model of high density surface EMG signals in a cylindrical conductor volume

2. A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface

3. On the benefits of using HD-sEMG technique for estimating muscle force

4. Palmprint recognition with an efficient data driven ensemble classifier

5. Muscle Changes in Aging

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. sEMG Signal Generation for Data Augmentation Using Time Series Transformer Based Conditional GAN;2023 Seventh International Conference on Advances in Biomedical Engineering (ICABME);2023-10-12

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