An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject
Open accessAbstract
The functionality of upper limb prostheses can be improved by intuitive control strategies that use bioelectric signals measured at the stump level. One such strategy is the decoding of motor volition via myoelectric pattern recognition (MPR), which has shown promising results in controlled environments and more recently in clinical practice. Moreover, not much has been reported about daily life implementation and real-time accuracy of these decoding algorithms. This paper introduces an alternative approach in which MPR allows intuitive control of four different grips and open/close in a multifunctional prosthetic hand. We conducted a clinical proof-of-concept in activities of daily life by constructing a self-contained, MPR-controlled, transradial prosthetic system provided with a novel user interface meant to log errors during real-time operation. The system was used for five days by a unilateral dysmelia subject whose hand had never developed, and who nevertheless learned to generate patterns of myoelectric activity, reported as intuitive, for multi-functional prosthetic control. The subject was instructed to manually log errors when they occurred via the user interface mounted on the prosthesis. This allowed the collection of information about prosthesis usage and real-time classification accuracy. The assessment of capacity for myoelectric control test was used to compare the proposed approach to the conventional prosthetic control approach, direct control. Regarding the MPR approach, the subject reported a more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. This paper represents an alternative to the conventional use of MPR, and this alternative may be particularly suitable for a certain type of amputee patients. Moreover, it represents a further validation of MPR with dysmelia cases.
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Intuitive motor control can be attained via advanced techniques such as targeted muscle reinnervation, regenerative peripheral nerve interfaces, or myoelectric pattern recognition.
“Intuitive motor control can be attained via the use of the advanced techniques such as targeted muscle reinnervation (TMR), regenerative peripheral nerve interfaces (RPNIs) or the use of myoelectric pattern recognition [19–21].”
From Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review (2020) · cites this paper as [20]
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- Authors
- Mastinu, E., Ahlberg, J., Lendaro, E., Hermansson, L., Hakansson, B., & Ortiz-Catalan, M.
- Affiliations
- Chalmers University of Technology (SE)
- KTH Royal Institute of Technology (SE)
- Örebro University (SE)
- Integrum (Sweden) (SE)
- Corresponding author
- Ortiz-Catalan, Max
- Venue
- IEEE Journal of Translational Engineering in Health and Medicine
- Published
- 2018
- Volume
- 6
- Pages
- 1-12
- ISSN
- 2168-2372 (ISSN-L)
- PMC
- PMC5881457
- OpenAlex
- W2789760074
- Cited by
- 48
- References
- 31
- Citations by year
- 2025: 4 · 2024: 5 · 2023: 5 · 2022: 9 · 2021: 8 · 2020: 6 · 2019: 8 · 2018: 3
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Journal of Translational Engineering in Health and Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion, public-domain)
- Funders
- Swedish Research Council (Vetenskapsrådet)
- Stiftelsen Promobilia
- VINNOVA
- European Commission (H2020, DeTOP project)
- 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 pattern recognition, prosthetic hand, transradial amputation, dysmelia, prosthetic control, grip selection, activities of daily living, intuitive control, amputees
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