A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model

A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model

Physical fatigue significantly impacts safety and performance across industrial, athletic, and medical domains, yet its detection remains challenging due to individual variability and limited generalizability of existing methods. This study introduces a multimodal fatigue detection system integratin...

Full description

Saved in:
Bibliographic Details
Main Authors: Soree Hwang, Nayeon Kwon, Dongwon Lee, Jongman Kim, Sumin Yang, Inchan Youn, Hyuk-June Moon, Joon-Kyung Sung, Sungmin Han
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Sensors
Subjects:
physical fatigue detection
surface electromyography (sEMG)
inertial measurement unit (IMU)
hybrid deep learning
CNN-LSTM-attention
gait kinematics
Online Access:https://www.mdpi.com/1424-8220/25/11/3309
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/1424-8220/25/11/3309

Similar Items