Evaluating EMG feature and classifier selection for application to partial-hand prosthesis control
Open accessAbstract
Pattern recognition-based myoelectric control of upper limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p<0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p=0.07), intrinsic (p=0.06), or combined extrinsic and intrinsic muscle EMG (p=0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p<0.001) and time domain/autoregressive feature sets (p<0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.
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Pattern recognition control of myoelectric prostheses is getting popularity due to better classification of EMG signals using various methods.
“Pattern recognition control of myoelectric prostheses is also getting popularity due to its better classification of EMG signals (user intentions) using various methods, i.e., linear discriminant analysis classifier, tacit learning system, deep learning with convolutional neural network (Adewuyi et al., 2016; Atzori et al., 2016; Fani et al., 2016; Oyama et al., 2016), etc.”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Adewuyi et al., 2016
The Introduction (page unavailable) describes LDA as a commonly used EMG classifier, and the Conclusion (page unavailable) reports improved classification from selecting EMG features. The paper does not establish that pattern-recognition prosthesis control is growing in popularity because of better classification; it also does not evaluate the tacit-learning or convolutional-network methods listed in the citing sentence. (LLM, conf=0.84)
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- Authors
- Adewuyi, A. A., Hargrove, L. J., & Kuiken, T. A.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Adewuyi, Adenike A.
- Venue
- Frontiers in Neurorobotics
- Publisher
- Frontiers Media SA
- Published
- 2016-10-19
- Volume
- 10
- Pages
- 15-15
- ISSN
- 1662-5218 (ISSN-L)
- PMC
- PMC5069722
- OpenAlex
- W2538120249
- Cited by
- 85
- References
- 43
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 12 · 2022: 13 · 2021: 8 · 2020: 11 · 2019: 13 · 2018: 13 · 2017: 5
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neurorobotics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Disability, Independent Living, and Rehabilitation Research (90RE5014-02-00)
- National Institutes of Health (T32 HD7418)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (1F31HD078092-01)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- myoelectric control, partial hand amputation, upper limb prosthesis, hand motion classification, wrist posture, linear discriminant analysis, artificial neural networks, quadratic discriminant analysis, autoregressive features, pattern recognition
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