Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only
Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walk...
Main Authors: | Pankwon Kim, Jinkyu Lee, Choongsoo S. Shin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/12/4204 |
Similar Items
-
Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only
by: Pankwon Kim, et al.
Published: (2024-01-01) -
Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements
by: Moh Arozi, et al.
Published: (2020-04-01) -
Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal
by: Pengjie Qin, et al.
Published: (2020-07-01) -
A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking
by: Christian Morbidoni, et al.
Published: (2019-08-01) -
Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors
by: Xugang Xi, et al.
Published: (2017-05-01)