Pattern Matching for Real-Time Extraction of Fast and Slow Spectral Components From sEMG Signals
Previous studies have demonstrated the potential of surface electromyography (sEMG) spectral decomposition in evaluating muscle performance, motor learning, and early diagnosis of muscle conditions. However, decomposition techniques require large data sets and are computationally demanding, making t...
Saved in:
Main Authors: | Alvaro Costa-Garcia (Author), Akihiko Murai (Author), Shingo Shimoda (Author) |
---|---|
Format: | Book |
Published: |
IEEE,
2023-01-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton
by: Ung Heo, et al.
Published: (2022) -
Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals
by: Jie Liang, et al.
Published: (2021) -
Cross-sectional Study of EMG and EMG Rise During Fast and Slow Hamstring Exercises
by: Kasper Krommes, et al.
Published: (2021) -
Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals
by: Shuhao Ma, et al.
Published: (2024) -
User Training With Error Augmentation for sEMG-Based Gesture Classification
by: Yunus Bicer, et al.
Published: (2024)