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Tytuł pozycji:

Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds.

Tytuł:
Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds.
Autorzy:
Zhang Q; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 1840 Entrepreneur Dr., 27695, Raleigh, NC, USA.; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 333 S Columbia St., 27514, Chapel Hill, NC, USA.
Fragnito N; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 1840 Entrepreneur Dr., 27695, Raleigh, NC, USA.; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 333 S Columbia St., 27514, Chapel Hill, NC, USA.
Franz JR; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 1840 Entrepreneur Dr., 27695, Raleigh, NC, USA.; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 333 S Columbia St., 27514, Chapel Hill, NC, USA.
Sharma N; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 1840 Entrepreneur Dr., 27695, Raleigh, NC, USA. .; Joint Department of Biomedical Engineering at the University of North Carolina-Chapel Hill and North Carolina State University, 333 S Columbia St., 27514, Chapel Hill, NC, USA. .
Źródło:
Journal of neuroengineering and rehabilitation [J Neuroeng Rehabil] 2022 Aug 09; Vol. 19 (1), pp. 86. Date of Electronic Publication: 2022 Aug 09.
Typ publikacji:
Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
Język:
English
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2004-
MeSH Terms:
Muscle, Skeletal*/diagnostic imaging
Muscle, Skeletal*/physiology
Walking Speed*
Ankle Joint/physiology ; Electromyography/methods ; Humans ; Walking/physiology
References:
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Contributed Indexing:
Keywords: B-mode ultrasound imaging; Human intent prediction; Neuromuscular model; Rehabilitative/assistive robotics; Sensor fusion; Surface electromyography
Entry Date(s):
Date Created: 20220809 Date Completed: 20220811 Latest Revision: 20220816
Update Code:
20240104
PubMed Central ID:
PMC9361708
DOI:
10.1186/s12984-022-01061-z
PMID:
35945600
Czasopismo naukowe
Background: Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI's prediction accuracy.
Objective: The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging.
Methods: Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds.
Results: On average, the normalized moment prediction root mean square error was reduced by 14.58 % ([Formula: see text]) and 36.79 % ([Formula: see text]) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction.
Conclusions: The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.
(© 2022. The Author(s).)
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