Use of probabilistic weights to enhance linear regression myoelectric control
Open accessAbstract
OBJECTIVE: Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. APPROACH: Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts' law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. MAIN RESULTS: Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. SIGNIFICANCE: Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.
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That approach gave usable signal amplitude and allowed some prosthetic limb control in transradial amputees.
“This strategy has provided reasonable signal amplitude and some control of a prosthetic limb in patients undergoing transradial amputations (24–28).”
From A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees (2020) · cites this paper as [28]
The Methods and amputee-subject Results report that two transradial amputees used EMG to control a cursor in a virtual task. The Discussion explicitly says physical-prosthesis testing remains to be done, and the paper does not establish that the strategy provided reasonable signal amplitude. Thus, it supports some myoelectric control in transradial amputees, but not the claimed signal amplitude or control of a physical prosthetic limb. (LLM, conf=0.87)
Experimental — accuracy not yet measuredEarlier studies obtained prosthetic control signals by connecting to intact residual-limb muscles.
“In previous studies, investigators have provided prosthetic control signals by interfacing with intact muscles in the residual limb.”
From A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees (2020) · cites this paper as [28]
Earlier work on simultaneous multiple-DOF control concentrated on wrist and hand open/closed control.
“Previous studies of simultaneous control of multiple DOFs have focused on controlling the wrist and hand open/closed (26, 28, 30).”
From A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees (2020) · cites this paper as [28]
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- Authors
- Smith, L. H., Kuiken, T. A., & Hargrove, L. J.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Smith, Lauren H
- Kuiken, Todd A
- Hargrove, Levi J
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2015-11-23
- Volume
- 12
- Issue
- 6
- Pages
- 066030
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC4808414
- OpenAlex
- W2285656057
- Cited by
- 15
- References
- 44
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 2 · 2020: 4 · 2019: 1 · 2018: 3 · 2017: 2
- FWCI
- 0.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Chicago Biomedical Consortium
- National Institute of Neurological Disorders and Stroke
- 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, linear regression, transradial amputation, prosthetic hand, electromyography, Gaussian model, movement classification, Fitts' law, degrees of freedom
- MeSH terms
- Amputees, Electromyography, Hand, Humans, Probability, Linear Models
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