Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model

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dc.contributor.authorFragnito, Natalie
dc.contributor.authorZhang, Qiang
dc.contributor.authorMyers, Alison
dc.contributor.authorSharma, Nitin
dc.date.accessioned2025-01-09T19:42:53Z
dc.date.available2025-01-09T19:42:53Z
dc.date.copyright© 2021 IEEE
dc.date.issued2021
dc.description.abstractMany rehabilitative exoskeletons use non-invasive surface electromyography (sEMG) to measure human volitional intent. However, signals from adjacent muscle groups interfere with sEMG measurements. Further, the inability to measure sEMG signals from deeply located muscles may not accurately measure the volitional intent. In this work, we combined sEMG and ultrasound (US) imaging-derived signals to improve the prediction accuracy of voluntary ankle effort. We used a multivariate linear model (MLM) that combines sEMG and US signals for ankle joint net plantarflexion (PF) moment prediction during the walking stance phase. We hypothesized that the proposed sEMG-US imaging-driven MLM would result in more accurate net PF moment prediction than sEMG-driven and US imaging-driven MLMs. Synchronous measurements including reflective makers coordinates, ground reaction forces, sEMG signals of lateral/medial gastrocnemius (LGS/MGS), and soleus (SOL) muscles, and US imaging of LGS and SOL muscles were collected from five able-bodied participants walking on a treadmill at multiple speeds. The ankle joint net PF moment benchmark was calculated based on inverse dynamics, while the net PF moment prediction was determined by the sEMG-US imaging-driven, sEMG-driven, and US imaging-driven MLMs. The findings show that the sEMG-US imaging-driven MLM can significantly improve the prediction of net PF moment during the walking stance phase at multiple speeds. Potentially, the proposed sEMG-US imaging-driven MLM can be used as a superior joint motion intent model in advanced and intelligent control strategies for rehabilitative exoskeletons.
dc.format.mimetypeapplication/pdf
dc.identifier.citationQ. Zhang, N. Fragnito, A. Myers and N. Sharma, "Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 6267-6272, doi: 10.1109/EMBC46164.2021.9630046.
dc.identifier.doi10.1109/EMBC46164.2021.9630046.
dc.identifier.orcidhttps://orcid.org/0000-0002-8806-9672
dc.identifier.urihttps://ir.ua.edu/handle/123456789/15127
dc.languageEnglish
dc.language.isoen_US
dc.publisherIEEE
dc.rights.licenseThis work is licensed under a CC BY-NC 4.0 license.
dc.subjectelectromyography
dc.subjectsEMG signals
dc.subjectultrasound (US) imaging-derived signals
dc.subjectvolitional motion
dc.subjectkinetics
dc.titlePlantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model
dc.typeconference paper
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