IEEE access, 2021, Vol.9, p.68320-68331
2021
Volltextzugriff (PDF)
Details
- Autor(en) / Beteiligte
- Titel
- Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation
- Ist Teil von
- IEEE access, 2021, Vol.9, p.68320-68331
- Ort / Verlag
- Piscataway: IEEE
- Erscheinungsjahr
- 2021
- Quelle
- EZB Electronic Journals Library
- Beschreibungen/Notizen
- In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the field-programmable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2169-3536eISSN: 2169-3536DOI: 10.1109/ACCESS.2021.3077644Titel-ID: cdi_doaj_primary_oai_doaj_org_article_810f47e3c12a44ad965104583208459e
- Format
- –
- Schlagworte
- Adaptive algorithms, adaptive filter, Adaptive filters, Blanking, Correlation coefficients, Electromyography, Field programmable gate arrays, field-programmable gate array (FPGA), Filtering algorithms, Functional electrical stimulation (FES), Iron, Noise reduction, Real time, Real-time systems, Stimulation, stimulus artifact removal (SAR), surface electromyography (sEMG)