The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Prediction of knee trajectory based on surface electromyogram with independent component analysis combined with support vector regression
Open accessAbstract
In recent years, surface electromyogram signals have been increasingly used to operate wearable devices. These devices can aid to help workers or soldiers to lower the load in the task to boost efficiency. However, achieving effective signal prediction has always been a challenge. It is critical to use an appropriate signal preprocessing method and prediction algorithm when developing a controller that can accurately predict and control human movements in real time. For this purpose, this article investigates the effect of various surface electromyogram preprocessing methods and algorithms on prediction results. Walking data (surface electromyogram angle) were collected from 10 adults (5 males and 5 females). To investigate the effect of preprocessing methods on the experimental results, the raw surface electromyogram signals were grouped and subjected to different preprocessing (bandpass/principal component analysis/independent component analysis, respectively). The processed data were then imported into the random forest and support vector regression algorithm for training and prediction. Multiple scenarios were combined to compare the results. The independent component analysis-processed data had the best performance in terms of convergence time and prediction accuracy in the support vector regression algorithm. The prediction accuracy of knee motion with this scheme was 94.54% ± 2.98. Notably, the forecast time was halved in comparison to the other combinations. The independent component analysis algorithm’s “blind source separation” feature effectively separates the original surface electromyogram signal and reduces signal noise, hence increasing prediction efficiency. The main contribution of this work is that the method (independent component analysis + support vector regression) has the potency of best prediction of surface electromyogram signal for knee movement. This work is the first step toward myoelectric control of assisted exoskeleton robots through discrete decoding.
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Independent Component Analysis (ICA) performs blind source separation extracting statistically independent components, isolates noise sources from physiological signals, and is used for EEG denoising and motor imagery feature extraction.
“| Independent Component | Blind source separation extracting | Isolate noise sources from | EEG denoising and motor imagery feature |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [240]
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Metadata
- Authors
- Zhu, M., Guan, X., Li, Z., Gao, Y., Zou, K., Gao, X., Wang, Z., Li, H., & Cai, K.
- Affiliations
- Nanjing University of Science and Technology (CN)
- School of Mechanical Engineering, Nanjing University of Science and Technology, Xiaolingwei, Nanjing City, Jiangsu Province, China
- Jiangsu Province Hospital (CN)
- Corresponding author
- Guan, XiaoRong
- Venue
- International Journal of Advanced Robotic Systems
- Publisher
- SAGE Publications
- Published
- 2022-07-01
- Volume
- 19
- Issue
- 4
- ISSN
- 1729-8806 (ISSN-L)
- 1729-8814
- OpenAlex
- W4293544165
- Cited by
- 11
- References
- 32
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 4
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Advanced Robotic Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- NATIONAL DEFENSE BASIC SCIENTIFIC RESEARCH PROGRAM OF CHINA (JCKY2019209B003)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
- Keywords
- surface electromyogram, independent component analysis, support vector regression, bandpass filtering, principal component analysis, myoelectric control, wearable exoskeleton, blind source separation
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