Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane
Open accessAbstract
This paper presents a quantitative representation method for the upper-limb elbow joint angle using only electromyography (EMG) signals for continuous elbow joint voluntary flexion and extension in the sagittal plane. The dynamics relation between the musculotendon force exerted by the biceps brachii muscle and the elbow joint angle is developed for a modified musculoskeletal model. Based on the dynamics model, a quadratic-like quantitative relationship between EMG signals and the elbow joint angle is built using a Hill-type-based muscular model. Furthermore, a state switching model is designed to stabilize the transition of EMG signals between different muscle contraction motions during the whole movement. To evaluate the efficiency of the method, ten subjects performed continuous experiments during a 4-day period and five of them performed a subsequent consecutive stepping test. The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally. The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments. It is also easier to calibrate and implement.
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Alternative strategies for high-resolution analysis of an infant's motor development involve quantitative data from digital imaging systems or commercial wearable sensors packages, often also with electroencephalography or electromyography.
“Alternative strategies for high-resolution analysis of an infant’s motor development involve quantitative data from digital imaging systems or commercial wearable sensors packages (11–15), often also with electroencephalography (16) or electromyography (17).”
From Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants (2021) · cites this paper as [17]
The Abstract describes a quantitative EMG method for estimating elbow movement, supporting the citation as an example of electromyography-based motor analysis. The Experimental Setup reports ten healthy volunteers with a mean age of 24.60 years; the paper does not study infant motor development or establish that EMG is often used for its analysis. No page numbers are available in our copy. (LLM, conf=0.86)
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“Alternative strategies for high-resolution analysis of an infant’s motor development involve quantitative data from digital imaging systems or commercial wearable sensors packages (11–15), often also with electroencephalography (16) or electromyography (17).”
BackgroundEMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee
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- Authors
- Pang, M., Guo, S., Huang, Q., Ishihara, H., & Hirata, H.
- Affiliations
- Kagawa University (JP)
- Beijing Institute of Technology (CN)
- Corresponding author
- Guo, Shuxiang
- Venue
- Journal of Medical and Biological Engineering
- Published
- 2015-04
- Volume
- 35
- Issue
- 2
- Pages
- 165-177
- ISSN
- 1609-0985 (ISSN-L)
- 2199-4757
- PMC
- PMC4414936
- OpenAlex
- W2160601648
- Cited by
- 53
- References
- 28
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 7 · 2018: 6 · 2017: 4 · 2016: 6 · 2015: 2
- FWCI
- 2.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Medical and Biological Engineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Keywords
- electromyography, biceps brachii, Hill-type muscle model, musculoskeletal model, exoskeleton control, sagittal plane motion, stepping test
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