Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy
Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects’ movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. Th...
Main Authors: | Nissrin Amrani El Yaakoubi, Caitlin McDonald, Olive Lennon |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-10-01
|
Series: | Bioengineering |
Subjects: | |
Online Access: | https://www.mdpi.com/2306-5354/10/10/1162 |
Similar Items
-
Prognosis of Sleep Bruxism Using Power Spectral Density Approach Applied on EEG Signal of Both EMG1-EMG2 and ECG1-ECG2 Channels
by: Dakun Lai, et al.
Published: (2019-01-01) -
An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand
by: Jinhua Zhang, et al.
Published: (2019-03-01) -
FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL
by: R. Aishwarya, et al.
Published: (2013-01-01) -
Auditory Cue Effects on Gait-Phase-Dependent Electroencephalogram (EEG) Modulations during Overground and Treadmill Walking
by: Kittichai Tharawadeepimuk, et al.
Published: (2024-02-01) -
MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals
by: Md Shafayet Hossain, et al.
Published: (2023-05-01)