Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Electromyography spectrogram-driven Vision Transformers for automated muscle fatigue assessment in construction operations. |
| Συγγραφείς: |
Gautam, Yogesh1 (AUTHOR), Ojha, Amit1 (AUTHOR), Jebelli, Houtan1 (AUTHOR) hjebelli@illinois.edu |
| Πηγή: |
Canadian Journal of Civil Engineering. 6/1/2026, Vol. 53, p1-18. 18p. |
| Θεματικοί όροι: |
*Electromyography, *Transformer models, *Biomechanics, *Machine learning, *Spectrograms, *Muscle fatigue, *Industrial hygiene, *Construction workers |
| Περίληψη: |
Construction workers face significant physical strain and musculoskeletal risks. While electromyography (EMG) has emerged as a robust assessment tool, current frameworks struggle with generalizability across different muscle groups. To solve this, we propose a Vision Transformer framework that applies self-attention to EMG spectrograms, modeling spatial-temporal dependencies to capture localized fatigue patterns. Experimental evaluations conducted on material lifting and hammering tasks targeting the biceps brachii, lumbar-erector spinae, and rectus femoris muscle groups demonstrate that the developed framework achieves an average classification accuracy of 82.7% (biceps brachii: 83.7%, lumbar-erector spinae: 84.4%, rectus femoris: 79.9%). These findings support the feasibility of a unified transformer-based spectrogram framework for localized fatigue monitoring across muscle groups with different biomechanical roles. The study contributes a task-grounded EMG fatigue assessment pipeline and provides evidence for its applicability in construction-relevant conditions, supporting the development of scalable fatigue-monitoring systems for physically demanding occupational environments. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: |
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