Staffordshire University logo
STORE - Staffordshire Online Repository

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

Ramu, Saru Meena, CHATZISTERGOS, Panagiotis, CHOCKALINGAM, Nachiappan, Arampatzis, Adamantios and Maganaris, Constantinos (2022) Automated Method for Tracking Human Muscle Architecture on Ultrasound Scans during Dynamic Tasks. Sensors, 22 (17). p. 6498. ISSN 1424-8220

sensors-22-06498 final published.pdf - Publisher's typeset copy
Available under License Creative Commons Attribution 4.0 International (CC BY 4.0) .

Download (19MB) | Preview

Abstract or description

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: muscle architecture; ultrasonography; in vivo fascicle length; pennation angle; Chan–Vese model; particle filter
Faculty: School of Life Sciences and Education > Sport and Exercise
Depositing User: Panagiotis CHATZISTERGOS
Date Deposited: 02 Sep 2022 10:03
Last Modified: 24 Feb 2023 14:04

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000