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...

Ful tanımlama

Kaydedildi:

Detaylı Bibliyografya
Asıl Yazarlar:	Soree Hwang, Nayeon Kwon, Dongwon Lee, Jongman Kim, Sumin Yang, Inchan Youn, Hyuk-June Moon, Joon-Kyung Sung, Sungmin Han
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	MDPI AG 2025-05-01
Seri Bilgileri:	Sensors
Konular:	physical fatigue detection surface electromyography (sEMG) inertial measurement unit (IMU) hybrid deep learning CNN-LSTM-attention gait kinematics
Online Erişim:	https://www.mdpi.com/1424-8220/25/11/3309
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

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

Benzer Materyaller