.png){kind=icon}
.png){kind=icon}
Return
Wearable fatigue detection system for wheelchair propulsion: Identifying key muscles via an attention-based LSTM Model
.png){kind=icon}
PRE
AI
L
X
J
M
T
DOI：10.1080/10400435.2026.2699367.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fatigue induces adverse short- and long-term health risks in wheelchair users. This study aimed to develop a machine learning approach to predict fatigue onset during wheelchair incremental exercise via surface electromyography (sEMG). sEMG from eight upper-limb muscles and oxygen uptake data were collected from nine wheelchair users (3 females, 6 males) performing incremental wheelchair propulsion on an ergometer. Ventilatory threshold (VT) derived from oxygen uptake was defined as the fatigue onset label, with sEMG signals as predictive features. A dynamic weighted attention long short-term memory (DWA-LSTM) model was built to distinguish non-fatigued and fatigue-transition propulsion cycles and identify critical muscle contributors. The model achieved an average classification accuracy of 94.82% using eight-muscle sEMG intensity. Notably, single-channel pectoralis major sEMG still yielded 89% accuracy. These findings indicate wearable fatigue monitoring systems may rely on single-muscle EMG to detect propulsion-related fatigue onset.
Keywords:
Dynamic weighted attention-long-short-term memory model
electromyography
exercise-induce fatigue
oxygen uptake
spinal cord injury
Journal
A
IF:
2.5
Papers:
159
Citations:
1.6K
Organization
Cited Papers