Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees
Myoelectric control schemes, pivotal in the control of prosthetic limbs, are often developed and evaluated in ideal laboratory conditions. However, these controlled environments may not fully represent the diverse challenges users face in real-world scenarios. The present study aims to tackle some o...
Saved in:
| Main Authors: | Elaheh Mohammadreza, Vinicius Prado da Fonseca, Xianta Jiang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10942474/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction
by: Shuo Zhang, et al.
Published: (2025-03-01) -
Reduce the User Burden of Multiuser Myoelectric Interface via Few-Shot Domain Adaptation
by: Bo Xue, et al.
Published: (2023-01-01) -
Enhancing and Optimizing User-Machine Closed-Loop Co-Adaptation in Dynamic Myoelectric Interface
by: Wei Li, et al.
Published: (2025-01-01) -
An Affordable AI-Driven and 3D-Printed Personalized Myoelectric Prosthesis: Design, Development, and Assessment
by: Enzo Romero, et al.
Published: (2025-01-01) -
Prosthetic rehabilitation of the upper limb amputee
by: Bernard O′Keeffe
Published: (2011-01-01)