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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.18266 |
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| _version_ | 1866916412375695360 |
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| author | Lan, Bangyu Niu, Kenan |
| author_facet | Lan, Bangyu Niu, Kenan |
| contents | Understanding the relationship between muscle activation and thickness deformation is critical for diagnosing muscle-related diseases and monitoring muscle health. Although ultrasound technique can measure muscle thickness change during muscle movement, its application in portable devices is limited by wiring and data collection challenges. Surface electromyography (sEMG), on the other hand, records muscle bioelectrical signals as the muscle activation. This paper introduced a deep-learning approach to leverage sEMG signals for muscle thickness deformation prediction, eliminating the need for ultrasound measurement. Using a dual-attention framework combining self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data. Experimental results with six healthy subjects showed that the approach could accurately predict muscle excursion with an average precision of 0.923$\pm$0.900mm, which shows that this method can facilitate real-time portable muscle health monitoring, showing potential for applications in clinical diagnostics, sports science, and rehabilitation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18266 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting Muscle Thickness Deformation from Muscle Activation Patterns: A Dual-Attention Framework Lan, Bangyu Niu, Kenan Signal Processing Machine Learning Understanding the relationship between muscle activation and thickness deformation is critical for diagnosing muscle-related diseases and monitoring muscle health. Although ultrasound technique can measure muscle thickness change during muscle movement, its application in portable devices is limited by wiring and data collection challenges. Surface electromyography (sEMG), on the other hand, records muscle bioelectrical signals as the muscle activation. This paper introduced a deep-learning approach to leverage sEMG signals for muscle thickness deformation prediction, eliminating the need for ultrasound measurement. Using a dual-attention framework combining self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data. Experimental results with six healthy subjects showed that the approach could accurately predict muscle excursion with an average precision of 0.923$\pm$0.900mm, which shows that this method can facilitate real-time portable muscle health monitoring, showing potential for applications in clinical diagnostics, sports science, and rehabilitation. |
| title | Predicting Muscle Thickness Deformation from Muscle Activation Patterns: A Dual-Attention Framework |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2409.18266 |