Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review.
Electromyography (EMG)
control algorithms
control architecture
lower limb amputation
movement intention recognition
pattern recognition
Journal
IEEE transactions on medical robotics and bionics
ISSN: 2576-3202
Titre abrégé: IEEE Trans Med Robot Bionics
Pays: United States
ID NLM: 101749706
Informations de publication
Date de publication:
Aug 2023
Aug 2023
Historique:
medline:
1
9
2023
pubmed:
1
9
2023
entrez:
1
9
2023
Statut:
ppublish
Résumé
Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.
Identifiants
pubmed: 37655190
doi: 10.1109/tmrb.2023.3282325
pmc: PMC10470657
mid: NIHMS1924311
doi:
Types de publication
Journal Article
Langues
eng
Pagination
547-562Subventions
Organisme : NICHD NIH HHS
ID : R01 HD079428
Pays : United States
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