MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE Article Swipe
Gangsheng Cao
,
Jian Ji
,
Qing Wu
,
Chunming Xia
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25072257
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25072257
Mechanomyography (MMG) is a non-invasive technique for assessing muscle activity by measuring mechanical signals, offering high sensitivity and real-time monitoring capabilities, and it has many applications in rehabilitation training. Traditional MMG-based motion recognition relies on feature extraction and classifier training, which require segmenting continuous actions, leading to challenges in real-time performance and segmentation accuracy. Therefore, this paper proposes an innovative method for the real-time segmentation and classification of upper limb rehabilitation actions based on the You Only Look Once (YOLO) algorithm, integrating the Squeeze-and-Excitation (SE) attention mechanism to enhance the model’s performance. In this paper, the collected MMG signals were transformed into one-dimensional time-series images. After image processing, the training set and test set were divided for the training and testing of the YOLOv5s-SE model. The results demonstrated that the proposed model effectively segmented isolated and continuous MMG motions while simultaneously performing real-time motion category prediction and outputting results. In segmentation tasks, the base YOLOv5s model achieved 97.9% precision and 98.0% recall, while the improved YOLOv5s-SE model increased precision to 98.7% (+0.8%) and recall to 98.3% (+0.3%). Additionally, the model demonstrated exceptional accuracy in predicting motion categories, achieving an accuracy of 98.9%. This method realizes the automatic segmentation of time-domain motions, avoids the limitations of manual parameter adjustment in traditional methods, and simultaneously enhances the real-time performance of MMG motion recognition through image processing, providing an effective solution for motion analysis in wearable devices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25072257
- OA Status
- gold
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409145336
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409145336Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25072257Digital Object Identifier
- Title
-
MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SEWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-03Full publication date if available
- Authors
-
Gangsheng Cao, Jian Ji, Qing Wu, Chunming XiaList of authors in order
- Landing page
-
https://doi.org/10.3390/s25072257Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/s25072257Direct OA link when available
- Concepts
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Segmentation, Rehabilitation, Motion (physics), Computer science, Physical medicine and rehabilitation, Artificial intelligence, Computer vision, Medicine, Physical therapyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- References (count)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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