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Automated Method for Tracking Human Muscle Architecture on Ultrasound Scans during Dynamic Tasks

Ramu, SM, Chatzistergos, P, Chockalingam, N, Arampatzis, A and Maganaris, C (2022) Automated Method for Tracking Human Muscle Architecture on Ultrasound Scans during Dynamic Tasks. Sensors, 22 (17). ISSN 1424-8220

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Abstract

Existing approaches for automated tracking of fascicle length (FL) and pennation angle (PA) rely on the presence of a single, user-defined fascicle (feature tracking) or on the presence of a specific intensity pattern (feature detection) across all the recorded ultrasound images. These prerequisites are seldom met during large dynamic muscle movements or for deeper muscles that are difficult to image. Deep-learning approaches are not affected by these issues, but their applicability is restricted by their need for large, manually analyzed training data sets. To address these limitations, the present study proposes a novel approach that tracks changes in FL and PA based on the distortion pattern within the fascicle band. The results indicated a satisfactory level of agreement between manual and automated measurements made with the proposed method. When compared against feature tracking and feature detection methods, the proposed method achieved the lowest average root mean squared error for FL and the second lowest for PA. The strength of the proposed approach is that the quantification process does not require a training data set and it can take place even when it is not possible to track a single fascicle or observe a specific intensity pattern on the ultrasound recording.

Item Type: Article
Uncontrolled Keywords: 0301 Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry
Subjects: R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Sport & Exercise Sciences
Publisher: MDPI AG
SWORD Depositor: A Symplectic
Date Deposited: 17 May 2023 11:32
Last Modified: 17 May 2023 11:45
DOI or ID number: 10.3390/s22176498
URI: https://researchonline.ljmu.ac.uk/id/eprint/19513
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