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Hand-motion patterns recognition based on mechanomyographic signal analysis
Abstract
A Mechanomyography (MMG) based hand-motion patterns recognition approach was proposed in this paper. With the MMG signal acquired in the upper arm via a single sensor, eleven original features were extracted, and they were further processed by principal components analysis (PCA) in order to reduce the dimension of the feature space. Quadratic discriminant analysis (QDA) was used for four hand-motion patterns recognition. The cross-validated experimental results show that PCA method is practical in dimension reduction and QDA is functional in classifying the four types of hand-motion modes. The average classification accuracy of eight subjects is 79.66%±7.32%. It also reveals that MMG signal is effective in classifying more than two hand-motion patterns even with only one channel signal, and can provide a new choice of control signal for upper-limb prosthetic hand design.
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“In a study done with able-bodied subjects, it was found superior to EMG in processing signals from hand muscles [125].”
From Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review (2020) · cites this paper as [125]
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Cites
No references could be resolved yet.
Research works printed but not linked: 15
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Cited by
Sorted by citations
Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review
Yildiz, K. A., Shin, A. Y., & Kaufman, K. R. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00667-5 · Cited by 113
“Mechanomyography (MMG), or vibromyography (phonomyography), is a non-invasive technique to measure lateral oscillations provoked by muscle contraction and stretching, which can be recorded by a hydrophone, piezoelectric contact sensor, a laser distance sensor, a microphone or an accelerometer [125].”
Background
Metadata
- Authors
- Zeng, Y., Yang, Z., Cao, W., & Xia, C.
- Affiliations
- East China University of Science and Technology (CN)
- Venue
- 2009 International Conference on Future BioMedical Information Engineering (FBIE)
- Publisher
- IEEE
- Published
- 2009-12
- Volume
- 75
- Pages
- 21-24
- OpenAlex
- W2540762065
- Cited by
- 24
- References
- 0
- Citations by year
- 2022: 3 · 2021: 1 · 2020: 2 · 2019: 3 · 2018: 2 · 2017: 4 · 2015: 3 · 2014: 2 · 2013: 2 · 2012: 1
- FWCI
- 1.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Future BioMedical Information Engineering (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- mechanomyography, feature extraction, principal component analysis, quadratic discriminant analysis, myoelectric signal classification
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Hand-motion patterns recognition based on mechanomyographic signal analysis. https://app.cochat.ai/papers/10.1109/fbie.2009.5405882
- The paper
- Zeng, Y., Yang, Z., Cao, W., & Xia, C. (2009). Hand-motion patterns recognition based on mechanomyographic signal analysis. 2009 International Conference on Future BioMedical Information Engineering (FBIE). https://doi.org/10.1109/fbie.2009.5405882
